{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:30:25Z","timestamp":1775187025276,"version":"3.50.1"},"reference-count":179,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Autom. Comput."],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s11633-021-1313-0","type":"journal-article","created":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T01:02:27Z","timestamp":1637802147000},"page":"887-914","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0160-9689","authenticated-orcid":false,"given":"Isaac Baffour","family":"Senkyire","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1197-0390","authenticated-orcid":false,"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,25]]},"reference":[{"issue":"6","key":"1313_CR1","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/cld.556","volume":"7","author":"A Reuben","year":"2016","unstructured":"A. Reuben. Examination of the abdomen. Clinical Liver Disease, vol. 7, no. 6, pp. 143\u2013150, 2016. DOI: https:\/\/doi.org\/10.1002\/cld.556.","journal-title":"Clinical Liver Disease"},{"key":"1313_CR2","volume-title":"NCI dictionary of cancer terms","author":"T N C I Dictionary","year":"2020","unstructured":"T. N. C. I. Dictionary, C. Terms, G. Nci, C. T. Widget. NCI dictionary of cancer terms. National Cancer Institute. [Online], Available: https:\/\/www.cancer.gov\/publications\/dictionaries\/cancer-terms\/def\/abdominal, March 31, 2020."},{"issue":"3","key":"1313_CR3","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1002\/ca.22832","volume":"30","author":"M Bilal","year":"2017","unstructured":"M. Bilal, V. Voin, N. Topale, J. Iwanaga, M. Loukas, R. S. Tubbs. The clinical anatomy of the physical examination of the abdomen: A comprehensive review. Clinical Anatomy, vol. 30, no. 3, pp. 352\u2013356, 2017. DOI: https:\/\/doi.org\/10.1002\/ca.22832.","journal-title":"Clinical Anatomy"},{"key":"1313_CR4","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-981-10-3373-5_4","volume-title":"Progress in Intelligent Computing Techniques: Theory, Practice, and Applications","author":"R Kaur","year":"2018","unstructured":"R. Kaur, M. Juneja. Comparison of different renal imaging modalities: An overview. In Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, P. K. Sa, M. N. Sahoo, M. Murugappan, Y. L. Wu, B. Majhi, Eds., Singapore: Springer, pp. 47\u201357, 2018. DOI: https:\/\/doi.org\/10.1007\/978-981-10-3373-5_4."},{"issue":"3","key":"1313_CR5","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.cjtee.2020.03.003","volume":"23","author":"M Shojaee","year":"2020","unstructured":"M. Shojaee, A. Sabzghabaei, A. Heidari. Efficacy of new scoring system for diagnosis of abdominal injury after blunt abdominal trauma in patients referred to emergency department. Chinese Journal of Traumatology, vol. 23, no. 3, pp. 145\u2013148, 2020. DOI: https:\/\/doi.org\/10.1016\/j.cjtee.2020.03.003.","journal-title":"Chinese Journal of Traumatology"},{"issue":"4","key":"1313_CR6","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1016\/j.clinimag.2016.02.014","volume":"40","author":"Z J Ricci","year":"2016","unstructured":"Z. J. Ricci, S. K. Oh, M. W. Stein, B. Kaul, M. Flusberg, V. Chernyak, A. M. Rozenblit, F. S. Mazzariol. Solid organ abdominal ischemia, part I: Clinical features, etiology, imaging findings, and management. Clinical Imaging, vol. 40, no. 4, pp. 720\u2013731, 2016. DOI: https:\/\/doi.org\/10.1016\/j.clin-imag.2016.02.014.","journal-title":"Clinical Imaging"},{"issue":"4","key":"1313_CR7","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1016\/j.clinimag.2016.02.016","volume":"40","author":"Z J Ricci","year":"2016","unstructured":"Z. J. Ricci, F. S. Mazzariol, B. Kaul, S. K. Oh, V. Chernyak, M. Flusberg, M. W. Stein, A. M. Rozenblit. Hollow organ abdominal ischemia, part II: Clinical features, etiology, imaging findings and management. Clinical Imaging, vol. 40, no. 4, pp. 751\u2013764, 2016. DOI: https:\/\/doi.org\/10.1016\/j.clinimag.2016.02.016.","journal-title":"Clinical Imaging"},{"issue":"4","key":"1313_CR8","doi-asserted-by":"publisher","first-page":"16","DOI":"10.37757\/MR2017.V19.N4.5","volume":"19","author":"C De Dios Soler-morej\u00f3n","year":"2017","unstructured":"C. De Dios Soler-morej\u00f3n, T. A. Lombardo-vaillant, T. O. Tamargo-Barbeito, M. L. N. G. Malbrain. Predicting abdominal surgery mortality: A model based on intra-abdominal pressure. MEDICC Review, vol. 19, no. 4, pp. 16\u201320, 2017. DOI: https:\/\/doi.org\/10.37757\/MR2017.V19.N4.5.","journal-title":"MEDICC Review"},{"key":"1313_CR9","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1007\/978-3-319-60618-7_45","volume-title":"Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition","author":"P Chinmayi","year":"2016","unstructured":"P. Chinmayi, L. Agilandeeswari, M. Prabukumar. Survey of image processing techniques in medical image analysis: Challenges and methodologies. In Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition, Springer, Vellore, India, pp. 460\u2013471, 2016. DOI: https:\/\/doi.org\/10.1007\/978-3-319-60618-7_45."},{"key":"1313_CR10","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-981-10-8527-7_21","volume-title":"Proceedings of the 4th International Conference on Recent Developments in Science, Engineering and Technology Data Science and Analytics","author":"M Dabass","year":"2018","unstructured":"M. Dabass, S. Vashisth, R. Vig. Effectiveness of region growing based segmentation technique for various medical images \u2014 a study. In Proceedings of the 4th International Conference on Recent Developments in Science, Engineering and Technology Data Science and Analytics, Gurgaon, India, Springer, pp. 234\u2013259, 2018. DOI: https:\/\/doi.org\/10.1007\/978-981-10-8527-7_21."},{"key":"1313_CR11","doi-asserted-by":"publisher","unstructured":"C. Chen, C. Qin, H. Q. Qiu, G. Tarroni, J. M. Duan, W. J. Bai, D. Rueckert. Deep learning for cardiac image segmentation: A review. Frontiers in Cardiovascular Medicine, vol. 7, Article number 25, 2020. DOI: https:\/\/doi.org\/10.3389\/fcvm.2020.00025.","DOI":"10.3389\/fcvm.2020.00025"},{"key":"1313_CR12","doi-asserted-by":"publisher","first-page":"143597","DOI":"10.1109\/ACCESS.2019.2944849","volume":"7","author":"G Zhang","year":"2019","unstructured":"G. Zhang, S. H. Dong, H. Xu, H. Y. Zhang, Y. J. Wu, Y. W. Zhang, X. M. Xi, Y. L. Yin. Correction learning for medical image segmentation. IEEE Access, vol. 7, pp. 143597\u2013143607, 2019. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2019.2944849.","journal-title":"IEEE Access"},{"issue":"1","key":"1313_CR13","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-020-09854-1","volume":"54","author":"S A Taghanaki","year":"2021","unstructured":"S. A. Taghanaki, K. Abhishek, J. P. Cohen, J. Cohen-Adad, G. Hamarneh. Deep semantic segmentation of natural and medical images: A review. Artificial Intelligence Review, vol. 54, no. 1, pp. 137\u2013178, 2021. DOI: https:\/\/doi.org\/10.1007\/s10462-020-09854-1.","journal-title":"Artificial Intelligence Review"},{"issue":"4","key":"1313_CR14","volume":"52","year":"2019","unstructured":"S. Ghosh, N. Das, I. Das, U. Maulik. Understanding deep learning techniques for image segmentation. ACM Computing Surveys, vol. 52, no. 4, Article number 73, 2019.","journal-title":"ACM Computing Surveys"},{"issue":"12","key":"1313_CR15","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X M Li","year":"2018","unstructured":"X. M. Li, H. Chen, X. J. Qi, Q. Dou, C. W. Fu, P. A. Heng. H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Transactions on Medical Imaging, vol. 37, no. 12, pp. 2663\u20132674, 2018. DOI: https:\/\/doi.org\/10.1109\/TMI.2018.2845918.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1313_CR16","doi-asserted-by":"publisher","first-page":"5937","DOI":"10.1109\/EMBC.2019.8856774","volume-title":"Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society","author":"Z Z Yang","year":"2019","unstructured":"Z. Z. Yang, L. Zhang, M. Zhang, J. Feng, Z. Wu, F. G. Ren, Y. Lv. Pancreas segmentation in abdominal CT scans using inter-\/intra-slice contextual information with a cascade neural network. In Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Berlin, Germany, pp. 5937\u20135940, 2019. DOI: https:\/\/doi.org\/10.1109\/EMBC.2019.8856774."},{"issue":"3","key":"1313_CR17","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s10334-017-0664-6","volume":"31","author":"O Gloger","year":"2018","unstructured":"O. Gloger, R. B\u00fclow, K. T\u00fcnnies, H. V\u00f6lzke. Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin \u2014 enhanced MRCP sequences. Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 31, no. 3, pp. 383\u2013397, 2018. DOI: https:\/\/doi.org\/10.1007\/s10334-017-0664-6.","journal-title":"Magnetic Resonance Materials in Physics, Biology and Medicine"},{"issue":"2","key":"1313_CR18","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1109\/TBME.2017.2764752","volume":"65","author":"Y K Huo","year":"2018","unstructured":"Y. K. Huo, J. Q. Liu, Z. B. Xu, R. L. Harrigan, A. Assad, R. G. Abramson, B. A. Landman. Robust multicontrast MRI spleen segmentation for splenomegaly using multi-atlas segmentation. IEEE Transactions on Biomedical Engineering, vol. 65, no. 2, pp. 336\u2013343, 2018. DOI: https:\/\/doi.org\/10.1109\/TBME.2017.2764752.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"1313_CR19","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.media.2019.04.005","volume":"55","author":"Y Wang","year":"2019","unstructured":"Y. Wang, Y. Y. Zhou, W. Shen, S. Park, E. K. Fishman, A. L. Yuille. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Medical Image Analysis, vol. 55, pp. 88\u2013102, 2019. DOI: https:\/\/doi.org\/10.1016\/j.media.2019.04.005.","journal-title":"Medical Image Analysis"},{"issue":"8","key":"1313_CR20","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","volume":"37","author":"E Gibson","year":"2018","unstructured":"E. Gibson, F. Giganti, Y. P. Hu, E. Bonmati, S. Bandula, K. Gurusamy, B. Davidson, S. P. Pereira, M. J. Clarkson, D. C. Barratt. Automatic multi-organ segmentation on abdominal CT with dense V-Networks. IEEE Transactions on Medical Imaging, vol. 37, no. 8, pp. 1822\u20131834, 2018. DOI: https:\/\/doi.org\/10.1109\/TMI.2018.2806309.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1313_CR21","doi-asserted-by":"publisher","unstructured":"S. Q. Chen, X. Zhong, S. Dorn, N. Ravikumar, Q. H. Tao, X. L. Huang, M. Lell, M. Kachelriess, A. Maier. Improving generalization capability of multi-organ segmentation models using dual-energy CT. IEEE Transactions on Radiation and Plasma Medical Sciences, to be published. DOI: https:\/\/doi.org\/10.1109\/TRPMS.2021.3055199.","DOI":"10.1109\/TRPMS.2021.3055199"},{"key":"1313_CR22","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-030-32606-7_2","volume-title":"Deep Learning in Healthcare: Paradigms and Applications","author":"K L Rom\u00e1n","year":"2020","unstructured":"K. L. Rom\u00e1n, M. Inmaculada Garc\u00eda Oca\u00f1a, N. L. Urzelai, M. \u00c1ngel Gonz\u00e1lez Ballester, I. M. Oliver. Medical image segmentation using deep learning. In Deep Learning in Healthcare: Paradigms and Applications, Springer, Cham, Germany, pp. 17\u201331, 2020. DOI: https:\/\/doi.org\/10.1007\/978-3-030-32606-7_2."},{"issue":"3","key":"1313_CR23","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s11831-018-9257-4","volume":"26","author":"S S Chouhan","year":"2019","unstructured":"S. S. Chouhan, A. Kaul, U. P. Singh. Image segmentation using computational intelligence techniques: Review. Archives of Computational Methods in Engineering, vol. 26, no. 3, pp. 533\u2013596, 2019. DOI: https:\/\/doi.org\/10.1007\/s11831-018-9257-4.","journal-title":"Archives of Computational Methods in Engineering"},{"key":"1313_CR24","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2019.01.103","volume":"338","author":"G T Wang","year":"2019","unstructured":"G. T. Wang, W. Q. Li, M. Aertsen, J. Deprest, S. Ourselin, T. Vercauteren. Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing, vol. 338, pp. 34\u201345, 2019. DOI: https:\/\/doi.org\/10.1016\/j.neucom.2019.01.103.","journal-title":"Neurocomputing"},{"issue":"5","key":"1313_CR25","doi-asserted-by":"publisher","first-page":"e148","DOI":"10.1002\/mp.13649","volume":"47","author":"H Seo","year":"2020","unstructured":"H. Seo, M. B. Khuzani, V. Vasudevan, C. Huang, H. Y. Ren, R. X. Xiao, X. Jia, L. Xing. Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications. Medical Physics, vol. 47, no. 5, pp. e148\u2013e167, 2020.","journal-title":"Medical Physics"},{"key":"1313_CR26","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/ICASS.2018.8651980","volume-title":"Proceedings of International Conference on Applied Smart Systems","author":"A Chebli","year":"2018","unstructured":"A. Chebli, A. Djebbar, H. F. Marouani. Semi-supervised learning for medical application: A survey. In Proceedings of International Conference on Applied Smart Systems, IEEE, Medea, Algeria, pp. 24\u201325, 2018. DOI: https:\/\/doi.org\/10.1109\/ICASS.2018.8651980."},{"key":"1313_CR27","doi-asserted-by":"publisher","first-page":"100243","DOI":"10.1109\/ACCESS.2019.2930111","volume":"7","author":"F Kulwa","year":"2019","unstructured":"F. Kulwa, C. Li, X. Zhao, B. C. Cai, N. Xu, S. L. Qi, S. Chen, Y. Y. Teng. A state-of-the-art survey for microorganism image segmentation methods and future potential. IEEE Access, vol. 7, pp. 100243\u2013100269, 2019. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2019.2930111.","journal-title":"IEEE Access"},{"issue":"1","key":"1313_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-31333-5","volume":"8","year":"2018","unstructured":"I. Aganj, M. G. Harisinghani, R. Weissleder, B. Fischl. Unsupervised medical image segmentation based on the local center of mass. Scientific Reports, vol. 8, no. 1, Article number 13012, 2018. DOI: https:\/\/doi.org\/10.1038\/s41598-018-31333-5.","journal-title":"Scientific Reports"},{"key":"1313_CR29","doi-asserted-by":"publisher","first-page":"3146","DOI":"10.1109\/ICPR.2016.7900118","volume-title":"Proceedings of the 23rd International Conference on Pattern Recognition","author":"M Borga","year":"2016","unstructured":"M. Borga, T. Andersson, O. D. Leinhard. Semi-supervised learning of anatomical manifolds for atlas-based segmentation of medical images. In Proceedings of the 23rd International Conference on Pattern Recognition, IEEE, Cancun, Mexico, pp. 3146\u20133149, 2016. DOI: https:\/\/doi.org\/10.1109\/ICPR.2016.7900118."},{"key":"1313_CR30","doi-asserted-by":"publisher","first-page":"6799","DOI":"10.1007\/s11042-019-08320-7","volume":"79","author":"X Yao","year":"2019","unstructured":"X. Yao, Y. Q. Song, Z. Liu. Advances on pancreas segmentation: A review. Multimedia Tools and Applications, vol. 79, pp. 6799\u20136821, 2019.","journal-title":"Multimedia Tools and Applications"},{"key":"1313_CR31","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.cmpb.2018.01.014","volume":"157","author":"H R Torres","year":"2018","unstructured":"H. R. Torres, S. Queir\u00f3s, P. Morais, B. Oliveira, J. C. Fonseca, J. L. Vila\u00e7a. Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review. Computer Methods and Programs in Biomedicine, vol. 157, pp. 49\u201367, 2018. DOI: https:\/\/doi.org\/10.1016\/j.cmpb.2018.01.014.","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"4","key":"1313_CR32","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/s13244-017-0558-1","volume":"8","author":"A Gotra","year":"2017","unstructured":"A. Gotra, L. Sivakumaran, G. Chartrand, K. N. Vu, F. Vandenbroucke-Menu, C. Kauffmann, S. Kadoury, B. Gallix, J. A. De Guise, A. Tang. Liver segmentation: Indications, techniques and future directions. Insights into Imaging, vol. 8, no. 4, pp. 377\u2013392, 2017. DOI: https:\/\/doi.org\/10.1007\/s13244-017-0558-1.","journal-title":"Insights into Imaging"},{"issue":"4","key":"1313_CR33","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s10462-017-9550-x","volume":"50","author":"M Moghbel","year":"2018","unstructured":"M. Moghbel, S. Mashohor, R. Mahmud, M. I. B. Saripan. Review of liver segmentation and computer assisted detection\/diagnosis methods in computed tomography. Artificial Intelligence Review, vol. 50, no. 4, pp. 497\u2013537, 2018. DOI: https:\/\/doi.org\/10.1007\/s10462-017-9550-x.","journal-title":"Artificial Intelligence Review"},{"key":"1313_CR34","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.cmpb.2019.07.002","volume":"178","author":"H Kumar","year":"2019","unstructured":"H. Kumar, S. V. Desouza, M. S. Petrov. Automated pancreas segmentation from computed tomography and magnetic resonance images: A systematic review. Computer Methods and Programs in Biomedicine, vol. 178, pp. 319\u2013328, 2019. DOI: https:\/\/doi.org\/10.1016\/j.cmpb.2019.07.002.","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"1","key":"1313_CR35","doi-asserted-by":"publisher","first-page":"67","DOI":"10.2214\/AJR.15.15996","volume":"207","author":"R M Summers","year":"2016","unstructured":"R. M. Summers. Progress in fully automated abdominal CT interpretation. American Journal of Roentgenology, vol. 207, no. 1, pp. 67\u201379, 2016. DOI: https:\/\/doi.org\/10.2214\/AJR.15.15996.","journal-title":"American Journal of Roentgenology"},{"issue":"20","key":"1313_CR36","doi-asserted-by":"publisher","first-page":"30321","DOI":"10.1007\/s11042-020-09592-0","volume":"80","author":"A Rehman","year":"2021","unstructured":"A. Rehman, F. G. Khan. A deep learning based review on abdominal images. Multimedia Tools and Applications, vol. 80, no. 20, pp. 30321\u201330352, 2021. DOI: https:\/\/doi.org\/10.1007\/S11042-020-09592-0.","journal-title":"Multimedia Tools and Applications"},{"key":"1313_CR37","doi-asserted-by":"publisher","first-page":"59514","DOI":"10.1109\/ACCESS.2019.2915121","volume":"7","author":"F M Meng","year":"2019","unstructured":"F. M. Meng, L. L. Guo, Q. B. Wu, H. L. Li. A new deep segmentation quality assessment network for refining bounding box based segmentation. IEEE Access, vol. 7, pp. 59514\u201359523, 2019. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2019.2915121.","journal-title":"IEEE Access"},{"key":"1313_CR38","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/3239576.3239590","volume-title":"Proceedings of the 2nd International Conference on Advances in Image Processing","author":"Z Jiang","year":"2018","unstructured":"Z. Jiang, C. Xu, X. H. Tu, T. Li, N. Gao. A Co-segmentation method for image pairs based on maximum common subgraph and GrabCut. In Proceedings of the 2nd International Conference on Advances in Image Processing, ACM, Chengdu, China, pp. 39\u201343, 2018. DOI: https:\/\/doi.org\/10.1145\/3239576.3239590."},{"issue":"5","key":"1313_CR39","doi-asserted-by":"publisher","DOI":"10.3390\/rs11050514","volume":"11","year":"2019","unstructured":"L. B. Yang, L. R. Mansaray, J. F. Huang, L. M. Wang. Optimal segmentation scale parameter, feature subset and classification algorithm for geographic object-based crop recognition using multisource satellite imagery. Remote Sensing, vol. 11, no. 5, Article number 514, 2019. DOI: https:\/\/doi.org\/10.3390\/rs11050514.","journal-title":"Remote Sensing"},{"issue":"9","key":"1313_CR40","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/TPAMI.2015.2496141","volume":"38","author":"A Dosovitskiy","year":"2016","unstructured":"A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, T. Brox. Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 9, pp. 1734\u20131747, 2016. DOI: https:\/\/doi.org\/10.1109\/TPAMI.2015.2496141.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1313_CR41","doi-asserted-by":"publisher","unstructured":"S. Li, G. K. F. Tso, K. J. He. Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation. Expert Systems with Applications, vol. 145, Article number 113131, 2020. DOI: https:\/\/doi.org\/10.1016\/j.eswa.2019.113131.","DOI":"10.1016\/j.eswa.2019.113131"},{"key":"1313_CR42","doi-asserted-by":"publisher","first-page":"107097","DOI":"10.1109\/ACCESS.2019.2917932","volume":"7","author":"Y Deng","year":"2019","unstructured":"Y. Deng, Y. Sun, Y. P. Zhu, Y. Xu, Q. X. Yang, S. Zhang, Z. Y. Wang, J. R. Sun, W. L. Zhao, X. B. Zhou, K. H. Yuan. A new framework to reduce doctor\u2019s workload for medical image annotation. IEEE Access, vol. 7, pp. 107097\u2013107104, 2019. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2019.2917932.","journal-title":"IEEE Access"},{"issue":"1","key":"1313_CR43","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TBME.2014.2344660","volume":"62","author":"C Y Li","year":"2015","unstructured":"C. Y. Li, X. Y. Wang, S. Eberl, M. Fulham, Y. Yin, D. D. Feng. Supervised variational model with statistical inference and its application in medical image segmentation. IEEE Transactions on Biomedical Engineering, vol. 62, no. 1, pp. 196\u2013207, 2015. DOI: https:\/\/doi.org\/10.1109\/TBME.2014.2344660.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"4","key":"1313_CR44","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1109\/TMI.2017.2787685","volume":"37","author":"E Kozegar","year":"2018","unstructured":"E. Kozegar, M. Soryani, H. Behnam, M. Salamati, T. Tan. Mass segmentation in automated 3-D breast ultrasound using adaptive region growing and supervised edge-based deformable model. IEEE Transactions on Medical Imaging, vol. 37, no. 4, pp. 918\u2013928, 2018. DOI: https:\/\/doi.org\/10.1109\/TMI.2017.2787685.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1313_CR45","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.patcog.2018.02.012","volume":"79","author":"M Xian","year":"2018","unstructured":"M. Xian, Y. T. Zhang, H. D. Cheng, F. Xu, B. Y. Zhang, J. Ding. R. Automatic breast ultrasound image segmentation: A survey. Pattern Recognition, vol. 79, pp. 340\u2013355, 2018. DOI: https:\/\/doi.org\/10.1016\/j.patcog.2018.02.012.","journal-title":"Pattern Recognition"},{"key":"1313_CR46","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","volume":"54","author":"V Cheplygina","year":"2019","unstructured":"V. Cheplygina, M. de Bruijne, J. P. W. Pluim. Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Medical Image Analysis, vol. 54, pp. 280\u2013296, 2019. DOI: https:\/\/doi.org\/10.1016\/j.media.2019.03.009.","journal-title":"Medical Image Analysis"},{"issue":"12","key":"1313_CR47","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1109\/TPAMI.2016.2645157","volume":"39","author":"Z Y Shi","year":"2017","unstructured":"Z. Y. Shi, Y. X. Yang, T. M. Hospedales, T. Xiang. Weakly-supervised image annotation and segmentation with objects and attributes. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 12, pp. 2525\u20132538, 2017. DOI: https:\/\/doi.org\/10.1109\/TPAMI.2016.2645157.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1313_CR48","doi-asserted-by":"publisher","first-page":"11093","DOI":"10.1109\/ACCESS.2019.2891970","volume":"7","author":"J Enguehard","year":"2019","unstructured":"J. Enguehard, P. O\u2019Halloran, A. Gholipour. Semi-supervised learning with deep embedded clustering for image classification and segmentation. IEEE Access, vol. 7, pp. 11093\u201311104, 2019. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2019.2891970.","journal-title":"IEEE Access"},{"key":"1313_CR49","doi-asserted-by":"publisher","first-page":"1752","DOI":"10.1109\/ISBI45749.2020.9098546","volume-title":"Proceedings of the 17th IEEE International Symposium on Biomedical Imaging","author":"Q Chang","year":"2020","unstructured":"Q. Chang, Z. N. Yan, Y. X. Lou, L. Axel, D. N. Metaxas. Soft-Label guided semi-supervised learning for Bi-ventricle segmentation in cardiac cine MRI. In Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, IEEE, Iowa City, USA, pp. 1752\u20131755, 2020. DOI: https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098546."},{"key":"1313_CR50","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.media.2018.02.001","volume":"45","author":"B Oliveira","year":"2018","unstructured":"B. Oliveira, S. Queir\u00f3s, P. Morais, H. R. Torres, J. Gomes-Fonseca, J. C. Fonseca, J. L. Vila\u00e7a. A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography. Medical Image Analysis, vol. 45, pp. 108\u2013120, 2018. DOI: https:\/\/doi.org\/10.1016\/j.media.2018.02.001.","journal-title":"Medical Image Analysis"},{"key":"1313_CR51","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/WACV.2019.00020","volume-title":"Proceedings of IEEE Winter Conference on Applications of Computer Vision","author":"Y Y Zhou","year":"2019","unstructured":"Y. Y. Zhou, Y. Wang, P. Tang, S. Bai, W. Shen, E. Fishman, A. Yuille. Semi-supervised 3D abdominal multi-organ segmentation via deep multi-planar Co-Training. In Proceedings of IEEE Winter Conference on Applications of Computer Vision, IEEE, Waikoloa, USA, pp. 121\u2013140, 2019. DOI: https:\/\/doi.org\/10.1109\/WACV.2019.00020."},{"issue":"2","key":"1313_CR52","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s11633-020-1260-1","volume":"18","author":"T W Utomo","year":"2021","unstructured":"T. W. Utomo, A. I. Cahyadi, I. Ardiyanto. Suction-based grasp point estimation in cluttered environment for robotic manipulator using deep learning-based affordance map. International Journal of Automation and Computing, vol. 18, no. 2, pp. 277\u2013287, 2021. DOI: https:\/\/doi.org\/10.1007\/s11633-020-1260-1.","journal-title":"International Journal of Automation and Computing"},{"issue":"4","key":"1313_CR53","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/s11633-019-1175-x","volume":"16","author":"J H Tao","year":"2019","unstructured":"J. H. Tao, J. Huang, Y. Li, Z. Lian, M. Y. Niu. Semi-supervised ladder networks for speech emotion recognition. International Journal of Automation and Computing, vol. 16, no. 4, pp. 437\u2013448, 2019. DOI: https:\/\/doi.org\/10.1007\/s11633-019-1175-x.","journal-title":"International Journal of Automation and Computing"},{"issue":"1","key":"1313_CR54","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/nsr\/nwx106","volume":"5","author":"Z H Zhou","year":"2018","unstructured":"Z. H. Zhou. A brief introduction to weakly supervised learning. National Science Review, vol. 5, no. 1, pp. 44\u201353, 2018. DOI: https:\/\/doi.org\/10.1093\/nsr\/nwx106.","journal-title":"National Science Review"},{"key":"1313_CR55","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.knosys.2018.11.034","volume":"165","author":"K Y Liu","year":"2019","unstructured":"K. Y. Liu, X. B. Yang, H. L. Yu, J. S. Mi, P. X. Wang, X. J. Chen. Rough set based semi-supervised feature selection via ensemble selector. Knowledge-based Systems, vol. 165, pp. 282\u2013296, 2019. DOI: https:\/\/doi.org\/10.1016\/j.knosys.2018.11.034.","journal-title":"Knowledge-based Systems"},{"key":"1313_CR56","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.neucom.2019.12.130","volume":"480","author":"Y W Chong","year":"2020","unstructured":"Y. W. Chong, Y. Ding, Q. Yan, S. M. Pan. Graph-based semi-supervised learning: A review. Neurocomputing, vol. 480, pp. 216\u2013230, 2020.","journal-title":"Neurocomputing"},{"key":"1313_CR57","doi-asserted-by":"publisher","first-page":"8535","DOI":"10.1109\/CVPR.2019.00874","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"A Zhao","year":"2019","unstructured":"A. Zhao, G. Balakrishnan, F. Durand, J. V. Guttag, A. V. Dalca. Data augmentation using learned transformations for one-shot medical image segmentation. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Long Beach, USA, pp. 8535\u20138545, 2019. DOI: https:\/\/doi.org\/10.1109\/CVPR.2019.00874."},{"key":"1313_CR58","doi-asserted-by":"publisher","unstructured":"A. Meyer, S. Ghosh, D. Schindele, M. Schostak, S. Stober, C. Hansen, M. Rak. Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyond. Artificial Intelligence in Medicine, vol. 116, Article number 102073, 2021. DOI: https:\/\/doi.org\/10.1016\/j.artmed.2021.102073.","DOI":"10.1016\/j.artmed.2021.102073"},{"key":"1313_CR59","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1145\/3219819.3220092","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"B Gu","year":"2018","unstructured":"B. Gu, X. T. Yuan, S. C. Chen, H. Huang. New incremental learning algorithm for semi-supervised support vector machine. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, London, UK, pp. 1475\u20131484, 2018. DOI: https:\/\/doi.org\/10.1145\/3219819.3220092."},{"issue":"5","key":"1313_CR60","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1007\/s00521-015-2113-7","volume":"28","author":"S F Ding","year":"2017","unstructured":"S. F. Ding, Z. B. Zhu, X. K. Zhang. An overview on semi-supervised support vector machine. Neural Computing and Applications, vol. 28, no. 5, pp. 969\u2013978, 2017. DOI: https:\/\/doi.org\/10.1007\/s00521-015-2113-7.","journal-title":"Neural Computing and Applications"},{"issue":"12","key":"1313_CR61","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.1007\/s11548-020-02265-1","volume":"15","author":"S Yagasaki","year":"2020","unstructured":"S. Yagasaki, N. Koizumi, Y. Nishiyama, R. Kondo, T. Imaizumi, N. Matsumoto, M. Ogawa, K. Numata. Estimating 3-dimensional liver motion using deep learning and 2-dimensional ultrasound images. International Journal of Computer Assisted Radiology and Surgery, vol. 15, no. 12, pp. 1989\u20131995, 2020. DOI: https:\/\/doi.org\/10.1007\/s11548-020-02265-1.","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"1313_CR62","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1109\/ISBI.2016.7493296","volume-title":"Proceedings of the 13th IEEE International Symposium on Biomedical Imaging","author":"P H Conze","year":"2016","unstructured":"P. H. Conze, V. Noblet, F. Rousseau, F. Heitz, R. Memeo, P. Pessaux. Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced CT scans. In Proceedings of the 13th IEEE International Symposium on Biomedical Imaging, IEEE, Prague, Czech Republic, pp. 416\u2013419, 2016. DOI: https:\/\/doi.org\/10.1109\/ISBI.2016.7493296."},{"key":"1313_CR63","doi-asserted-by":"publisher","unstructured":"M. Chung, J. Lee, M. Lee, J. Lee, Y. G. Shin. Deeply self-supervised contour embedded neural network applied to liver segmentation. Computer Methods and Programs in Biomedicine, vol. 192, Article number 105447, 2020. DOI: https:\/\/doi.org\/10.1016\/j.cmpb.2020.105447.","DOI":"10.1016\/j.cmpb.2020.105447"},{"key":"1313_CR64","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1109\/ISBI45749.2020.9098509","volume-title":"Proceedings of the 17th IEEE International Symposium on Biomedical Imaging","author":"M F Xu","year":"2020","unstructured":"M. F. Xu, Y. Wang, Y. Chi, X. S. Hua. Training liver vessel segmentation deep neural networks on noisy labels from contrast CT imaging. In Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, IEEE, Iowa City, USA, pp. 1552\u20131555, 2020. DOI: https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098509."},{"issue":"24","key":"1313_CR65","doi-asserted-by":"publisher","first-page":"18591","DOI":"10.1007\/s00500-020-05094-1","volume":"24","author":"R M Devi","year":"2020","unstructured":"R. M. Devi, V. Seenivasagam. Automatic segmentation and classification of liver tumor from CT image using feature difference and SVM based classifier-soft computing technique. Soft Computing, vol. 24, no. 24, pp. 18591\u201318598, 2020. DOI: https:\/\/doi.org\/10.1007\/s00500-020-05094-1.","journal-title":"Soft Computing"},{"issue":"5","key":"1313_CR66","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1109\/TMI.2019.2948320","volume":"39","author":"H Seo","year":"2020","unstructured":"H. Seo, C. Huang, M. Bassenne, R. X. Xiao, L. Xing. Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images. IEEE Transactions on Medical Imaging, vol. 39, no. 5, pp. 1316\u20131325, 2020. DOI: https:\/\/doi.org\/10.1109\/TMI.2019.2948320.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"6","key":"1313_CR67","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1007\/s11548-020-02147-6","volume":"15","author":"X Fang","year":"2020","unstructured":"X. Fang, S. Xu, B. J. Wood, P. K. Yan. Deep learning-based liver segmentation for fusion-guided intervention. International Journal of Computer Assisted Radiology and Surgery, vol. 15, no. 6, pp. 963\u2013972, 2020. DOI: https:\/\/doi.org\/10.1007\/s11548-020-02147-6.","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"issue":"12","key":"1313_CR68","doi-asserted-by":"publisher","first-page":"2742","DOI":"10.1007\/s00259-020-04800-3","volume":"47","author":"X K Tang","year":"2020","unstructured":"X. K. Tang, E. Jafargholi Rangraz, W. Coudyzer, J. Bertels, D. Robben, G. Schramm, W. Deckers, G. Maleux, K. Baete, C. Verslype, M. J. Gooding, C. M. Deroose, J. Nuyts. Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT. European Journal of Nuclear Medicine and Molecular Imaging, vol. 47, no. 12, pp. 2742\u20132752, 2020. DOI: https:\/\/doi.org\/10.1007\/s00259-020-04800-3.","journal-title":"European Journal of Nuclear Medicine and Molecular Imaging"},{"issue":"2","key":"1313_CR69","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1097\/RCT.0000000000000987","volume":"44","author":"Y S Ng","year":"2020","unstructured":"Y. S. Ng, Y. Xi, Y. X. Qian, L. Ananthakrishnan, T. C. Soesbe, M. Lewis, R. Lenkinski, J. R. Fielding. Use of spectral detector computed tomography to improve liver segmentation and volumetry. Journal of Computer Assisted Tomography, vol. 44, no. 2, pp. 197\u2013203, 2020. DOI: https:\/\/doi.org\/10.1097\/RCT.0000000000000987.","journal-title":"Journal of Computer Assisted Tomography"},{"issue":"5","key":"1313_CR70","doi-asserted-by":"publisher","DOI":"10.3390\/s20051516","volume":"20","year":"2020","unstructured":"S. Almotairi, G. Kareem, M. Aouf, B. Almutairi, M. A. M. Salem. Liver tumor segmentation in CT scans using modified segnet. Sensors, vol. 20, no. 5, Article number 1516, 2020. DOI: https:\/\/doi.org\/10.3390\/s20051516.","journal-title":"Sensors"},{"key":"1313_CR71","doi-asserted-by":"publisher","unstructured":"G. M. Cunha, K. A. Hasenstab, A. Higaki, K. Wang, T. Delgado, R. L. Brunsing, A. Schlein, A. Schwartzman, A. Hsiao, C. B. Sirlin, K. J. Fowler. Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time. European Journal of Radiology, vol. 124, Article number 108837, 2020. DOI: https:\/\/doi.org\/10.1016\/j.ejrad.2020.108837.","DOI":"10.1016\/j.ejrad.2020.108837"},{"key":"1313_CR72","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM47256.2019.8983266","volume-title":"Proceedings of IEEE International Conference on Bioinformatics and Biomedicine","author":"A A Albishri","year":"2019","unstructured":"A. A. Albishri, S. J. H. Shah, Y. Lee. CU-Net: Cascaded U-Net model for automated liver and lesion segmentation and summarization. In Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, IEEE, San Diego, USA, pp. 1416\u20131423, 2019. DOI: https:\/\/doi.org\/10.1109\/BIBM47256.2019.8983266."},{"key":"1313_CR73","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1109\/ICIP.2019.8802942","volume-title":"Proceedings of IEEE International Conference on Image Processing","author":"Y C Wu","year":"2019","unstructured":"Y. C. Wu, Q. Zhou, H. J. Hu, G. H. Rong, Y. W. Li, S. Y. Wang. Hepatic lesion segmentation by combining plain and contrast-enhanced CT images with modality weighted U-Net. In Proceedings of IEEE International Conference on Image Processing, IEEE, Taipei, China, pp. 255\u2013259, 2019. DOI: https:\/\/doi.org\/10.1109\/ICIP.2019.8802942."},{"key":"1313_CR74","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1109\/ISBI.2019.8759484","volume-title":"Proceedings of the 16th IEEE International Symposium on Biomedical Imaging","author":"I Aganj","year":"2019","unstructured":"I. Aganj, B. Fischl. Expected label value computation for atlas-based image segmentation. In Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, IEEE, Venice, Italy, pp. 334\u2013338, 2019. DOI: https:\/\/doi.org\/10.1109\/ISBI.2019.8759484."},{"key":"1313_CR75","doi-asserted-by":"publisher","DOI":"10.1117\/12.2506770","volume-title":"Proceedings of SPIE 10949, Medical Imaging 2019","author":"M J A Jansen","year":"2019","unstructured":"M. J. A. Jansen, H. J. Kuijf, J. P. W. Pluim. Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation. In Proceedings of SPIE 10949, Medical Imaging 2019, SPIE, San Diego, USA, Article number 109491V, 2019. DOI: https:\/\/doi.org\/10.1117\/12.2506770."},{"key":"1313_CR76","doi-asserted-by":"publisher","DOI":"10.1117\/12.2521631","volume-title":"Proceedings of SPIE 11050, International Forum on Medical Imaging in Asia 2019","author":"T Y Su","year":"2019","unstructured":"T. Y. Su, W. T. Yang, T. C. Cheng, Y. F. He, C. J. Yang, Y. H. Fang. Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm. In Proceedings of SPIE 11050, International Forum on Medical Imaging in Asia 2019, SPIE, Singapore, Article number 1105011, 2019. DOI: https:\/\/doi.org\/10.1117\/12.2521631."},{"issue":"4","key":"1313_CR77","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1007\/s10044-017-0666-z","volume":"21","author":"E Dura","year":"2018","unstructured":"E. Dura, J. Domingo, E. G\u00f6\u00e7eri, L. Mart\u00ed-Bonmat\u00ed. A method for liver segmentation in perfusion MR images using probabilistic atlases and viscous reconstruction. Pattern Analysis and Applications, vol. 21, no. 4, pp. 1083\u20131095, 2018. DOI: https:\/\/doi.org\/10.1007\/s10044-017-0666-z.","journal-title":"Pattern Analysis and Applications"},{"key":"1313_CR78","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-01421-6_14","volume-title":"Proceedings of the 27th International Conference on Artificial Neural Networks and Machine Learning","author":"W Tang","year":"2018","unstructured":"W. Tang, D. S. Zou, S. Yang, J. Shi. DSL: Automatic liver segmentation with faster R-CNN and deeplab. In Proceedings of the 27th International Conference on Artificial Neural Networks and Machine Learning, Springer, Rhodes, Greece, pp. 137\u2013147, 2018. DOI: https:\/\/doi.org\/10.1007\/978-3-030-01421-6_14."},{"key":"1313_CR79","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.media.2017.05.001","volume":"41","author":"Q Dou","year":"2017","unstructured":"Q. Dou, L. Q. Yu, H. Chen, Y. M. Jin, X. Yang, J. Qin, P. A. Heng. 3D deeply supervised network for automated segmentation of volumetric medical images. Medical Image Analysis, vol. 41, pp. 40\u201354, 2017. DOI: https:\/\/doi.org\/10.1016\/j.media.2017.05.001.","journal-title":"Medical Image Analysis"},{"key":"1313_CR80","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/978-3-319-46976-8_9","volume-title":"Proceedings of the 1st International Workshop on Deep Learning and Data Labeling for Medical Applications","author":"A Ben-Cohen","year":"2016","unstructured":"A. Ben-Cohen, I. Diamant, E. Klang, M. Amitai, H. Greenspan. Fully convolutional network for liver segmentation and lesions detection. In Proceedings of the 1st International Workshop on Deep Learning and Data Labeling for Medical Applications, Springer, Athens, Greece, pp. 77\u201385, 2016. DOI: https:\/\/doi.org\/10.1007\/978-3-319-46976-8_9."},{"key":"1313_CR81","doi-asserted-by":"publisher","unstructured":"B. C. Anil, P. Dayananda. Automatic liver tumor segmentation based on multi-level deep convolutional networks and fractal residual network. IETE Journal of Research, to be published. DOI: https:\/\/doi.org\/10.1080\/03772063.2021.1878066.","DOI":"10.1080\/03772063.2021.1878066"},{"issue":"1","key":"1313_CR82","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1016\/j.aej.2020.10.046","volume":"60","author":"N Alalwan","year":"2021","unstructured":"N. Alalwan, A. Abozeid, A. A. ElHabshy, A. Alzahrani. Efficient 3D deep learning model for medical image semantic segmentation. Alexandria Engineering Journal, vol. 60, no. 1, pp. 1231\u20131239, 2021. DOI: https:\/\/doi.org\/10.1016\/j.aej.2020.10.046.","journal-title":"Alexandria Engineering Journal"},{"key":"1313_CR83","doi-asserted-by":"publisher","first-page":"103906","DOI":"10.1016\/j.compbiomed.2020.103906","volume":"123","author":"L B da Cruz","year":"2020","unstructured":"L. B. da Cruz, J. D. L. Ara\u00fajo, J. L. Ferreira, J. O. B. Diniz, A. C. Silva, J. D. S. De Almeida, A. C. De Paiva, M. Gattass. Kidney segmentation from computed tomography images using deep neural network. Computers in Biology and Medicine, vol. 123, pp. 103906, 2020. DOI: https:\/\/doi.org\/10.1016\/J.COMPBIOMED.2020.103906.","journal-title":"Computers in Biology and Medicine"},{"issue":"6","key":"1313_CR84","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/TMI.2015.2512606","volume":"35","author":"C Jin","year":"2016","unstructured":"C. Jin, F. Shi, D. H. Xiang, X. Q. Jiang, B. Zhang, X. M. Wang, W. F. Zhu, E. T. Gao, X. J. Chen. 3D fast automatic segmentation of kidney based on modified AAM and random forest. IEEE Transactions on Medical Imaging, vol. 35, no. 6, pp. 1395\u20131407, 2016. DOI: https:\/\/doi.org\/10.1109\/TMI.2015.2512606.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1313_CR85","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1109\/ICIP.2019.8802924","volume-title":"Proceedings of IEEE International Conference on Image Processing","author":"T Pan","year":"2019","unstructured":"T. Pan, G. Y. Yang, C. X. Wang, Z. W. Lu, Z. W. Zhou, Y. Y. Kong, L. J. Tang, X. M. Zhu, J. L. Dillenseger, H. Z. Shu, J. L. Coatrieux. A Multi-task convolutional neural network for renal tumor segmentation and classification using multi-phasic CT images. In Proceedings of IEEE International Conference on Image Processing, IEEE, Taipei, China, pp. 80\u2013813, 2019. DOI: https:\/\/doi.org\/10.1109\/ICIP.2019.8802924."},{"issue":"9","key":"1313_CR86","doi-asserted-by":"publisher","first-page":"4032","DOI":"10.1002\/mp.14193","volume":"47","author":"Z Fatemeh","year":"2020","unstructured":"Z. Fatemeh, S. Nicola, K. Satheesh, U. Eranga. Ensemble U-net-based method for fully automated detection and segmentation of renal masses on computed tomography images. Medical Physics, vol. 47, no. 9, pp. 4032\u20134044, 2020. DOI: https:\/\/doi.org\/10.1002\/mp.14193.","journal-title":"Medical Physics"},{"key":"1313_CR87","doi-asserted-by":"publisher","unstructured":"S. Yin, Q. M. Peng, H. M. Li, Z. Q. Zhang, X. G. You, K. Fischer, S. L. Furth, G. E. Tasian, Y. Fan. Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks. Medical Image Analysis, vol. 60, Article number 101602, 2020. DOI: https:\/\/doi.org\/10.1016\/j.media.2019.101602.","DOI":"10.1016\/j.media.2019.101602"},{"issue":"1","key":"1313_CR88","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-40710-7","volume":"9","year":"2019","unstructured":"J. Park, S. Bae, S. Seo, S. Park, J. I. Bang, J. H. Han, W. W. Lee, J. S. Lee. Measurement of glomerular filtration rate using quantitative SPECT\/CT and deep-learning-based kidney segmentation. Scientific Reports, vol. 9, no. 1, Article number 4223, 2019. DOI: https:\/\/doi.org\/10.1038\/s41598-019-40710-7.","journal-title":"Scientific Reports"},{"key":"1313_CR89","doi-asserted-by":"publisher","first-page":"3898","DOI":"10.1109\/ICPR.2018.8545713","volume-title":"Proceedings of the 24th International Conference on Pattern Recognition","author":"H Abdeltawab","year":"2018","unstructured":"H. Abdeltawab, M. Shehatal, A. Shalaby, S. Mesbah, M. El-Baz, M. Ghazal, Y. Alkhali, M. Abouel-Ghar, A. C. Dwyer, M. El-Melegy, A. El-Baz. A new 3D CNN-based CAD system for early detection of acute renal transplant rejection. In Proceedings of the 24th International Conference on Pattern Recognition, IEEE, Beijing, China, pp. 3898\u20133903, 2018. DOI: https:\/\/doi.org\/10.1109\/ICPR.2018.8545713."},{"key":"1313_CR90","doi-asserted-by":"publisher","first-page":"1534","DOI":"10.1109\/ISBI.2018.8363865","volume-title":"Proceedings of the 15th IEEE International Symposium on Biomedical Imaging","author":"M Haghighi","year":"2018","unstructured":"M. Haghighi, S. K. Warfield, S. Kurugol. Automatic renal segmentation in DCE-MRI using convolutional neural networks. In Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, IEEE, Washington DC, USA, pp. 1534\u20131537, 2018. DOI: https:\/\/doi.org\/10.1109\/ISBI.2018.8363865."},{"key":"1313_CR91","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-319-59050-9_49","volume-title":"Proceedings of the 25th International Conference on Information Processing in Medical Imaging","author":"H Ravishankar","year":"2017","unstructured":"H. Ravishankar, S. Thiruvenkadam, R. Venkataramani, V. Vaidya. Joint deep learning of foreground, background and shape for robust contextual segmentation. In Proceedings of the 25th International Conference on Information Processing in Medical Imaging, Springer, Boone, USA, pp. 622\u2013632, 2017. DOI: https:\/\/doi.org\/10.1007\/978-3-319-59050-9_49."},{"key":"1313_CR92","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/ISBI.2018.8363779","volume-title":"Proceedings of the 15th IEEE International Symposium on Biomedical Imaging","author":"P R Tabrizi","year":"2018","unstructured":"P. R. Tabrizi, A. Mansoor, J. J. Cerrolaza, J. Jago, M. G. Linguraru. Automatic kidney segmentation in 3D pediatric ultrasound images using deep neural networks and weighted fuzzy active shape model. In Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, IEEE, Washington, USA, pp. 1170\u20131173, 2018. DOI: https:\/\/doi.org\/10.1109\/ISBI.2018.8363779."},{"key":"1313_CR93","doi-asserted-by":"publisher","first-page":"3399","DOI":"10.1109\/ICIP.2016.7532990","volume-title":"Proceedings of IEEE International Conference on Image Processing","author":"F Khalifa","year":"2016","unstructured":"F. Khalifa, A. Soliman, A. C. Dwyer, G. Gimel\u2019Farb, A. El-Baz. A random forest-based framework for 3D kidney segmentation from dynamic contrast-enhanced CT images. In Proceedings of IEEE International Conference on Image Processing, IEEE, Phoenix, USA, pp. 3399\u20133403, 2016. DOI: https:\/\/doi.org\/10.1109\/ICIP.2016.7532990."},{"issue":"6","key":"1313_CR94","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0217647","volume":"14","year":"2019","unstructured":"S. C. Pang, T. Ding, S. B. Qiao, F. Meng, S. Wang, P. B. Li, X. Wang. A novel YOLOv3-arch model for identifying cholelithiasis and classifying gallstones on CT images. PLoS One, vol. 14, no. 6, Article number e0217647, 2019. DOI: https:\/\/doi.org\/10.1371\/journal.pone.0217647.","journal-title":"PLoS One"},{"issue":"4","key":"1313_CR95","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1016\/j.gie.2020.04.071","volume":"92","author":"J Zhang","year":"2020","unstructured":"J. Zhang, L. R. Zhu, L. W. Yao, X. W. Ding, D. Chen, H. L. Wu, Z. H. Lu, W. Zhou, L. H. Zhang, P. An, B. Xu, W. Tan, S. Hu, F. Cheng, H. G. Yu. Deep learning-based pancreas segmentation and station recognition system in EUS: Development and validation of a useful training tool (with video). Gastrointestinal Endoscopy, vol. 92, no. 4, pp. 874\u2013885, 2020. DOI: https:\/\/doi.org\/10.1016\/j.gie.2020.04.071.","journal-title":"Gastrointestinal Endoscopy"},{"issue":"10","key":"1313_CR96","doi-asserted-by":"publisher","DOI":"10.3390\/app10103360","volume":"10","year":"2020","unstructured":"M. Nishio, S. Noguchi, K. Fujimoto. Automatic pancreas segmentation using coarse-scaled 2D model of deep learning: Usefulness of data augmentation and deep U-net. Applied Sciences, vol. 10, no. 10, Article number 3360, 2020. DOI: https:\/\/doi.org\/10.3390\/app10103360.","journal-title":"Applied Sciences"},{"key":"1313_CR97","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.mri.2020.01.008","volume":"68","author":"H Y Zheng","year":"2020","unstructured":"H. Y. Zheng, Y. F. Chen, X. D. Yue, C. Ma, X. H. Liu, P. P. Yang, J. P. Lu. Deep pancreas segmentation with uncertain regions of shadowed sets. Magnetic Resonance Imaging, vol. 68, pp. 45\u201352, 2020. DOI: https:\/\/doi.org\/10.1016\/j.mri.2020.01.008.","journal-title":"Magnetic Resonance Imaging"},{"key":"1313_CR98","doi-asserted-by":"publisher","unstructured":"F. Y. Li, W. S. Li, Y. C. Shu, S. Qin, B. Xiao, Z. W. Zhan. Multiscale receptive field based on residual network for pancreas segmentation in CT images. Biomedical Signal Processing and Control, vol. 57, Article number 101828, 2020. DOI: https:\/\/doi.org\/10.1016\/j.bspc.2019.101828.","DOI":"10.1016\/j.bspc.2019.101828"},{"key":"1313_CR99","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1109\/ISBI45749.2020.9098388","volume-title":"Proceedings of the IEEE 17th International Symposium on Biomedical Imaging","author":"Y Zhang","year":"2020","unstructured":"Y. Zhang, J. Wu, S. M. Wang, Y. L. Liu, Y. F. Chen, E. X. Wu, X. Y. Tang. Liver guided pancreas segmentation. In Proceedings of the IEEE 17th International Symposium on Biomedical Imaging, IEEE, Iowa City, USA, pp. 1201\u20131204, 2020. DOI: https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098388."},{"key":"1313_CR100","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1145\/3357254.3357259","volume-title":"Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition","author":"W H Yu","year":"2019","unstructured":"W. H. Yu, H. Chen, L. S. Wang. Dense attentional network for pancreas segmentation in abdominal CT scans. In Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition, ACM, Beijing, China, pp. 83\u201387, 2019. DOI: https:\/\/doi.org\/10.1145\/3357254.3357259."},{"key":"1313_CR101","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1109\/ISBI45749.2020.9098473","volume-title":"Proceedings of the 17th IEEE International Symposium on Biomedical Imaging","author":"W Z Wang","year":"2020","unstructured":"W. Z. Wang, Q. Y. Song, R. W. Feng, T. T. Chen, J. T. Chen, D. Z. Chen, J. Wu. A fully 3D cascaded framework for pancreas segmentation. In Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, IEEE, Iowa City, USA, pp. 207\u2013211, 2020. DOI: https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098473."},{"issue":"8","key":"1313_CR102","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1109\/TMI.2019.2911588","volume":"38","author":"Y Z Man","year":"2019","unstructured":"Y. Z. Man, Y. S. B. Huang, J. Y. Feng, X. Li, F. Wu. Deep Q learning driven CT pancreas segmentation with geometry-aware U-Net. IEEE Transactions on Medical Imaging, vol. 38, no. 8, pp. 1971\u20131980, 2019. DOI: https:\/\/doi.org\/10.1109\/TMI.2019.2911588.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"1313_CR103","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TIP.2016.2624198","volume":"26","author":"A Farag","year":"2017","unstructured":"A. Farag, L. Lu, H. R. Roth, J. M. Liu, E. Turkbey, R. M. Summers. A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 386\u2013399, 2017. DOI: https:\/\/doi.org\/10.1109\/TIP.2016.2624198.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1313_CR104","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/978-3-030-32245-8_23","volume-title":"Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention","author":"N N Zhao","year":"2019","unstructured":"N. N. Zhao, N. Tong, D. Ruan, K. Sheng. Fully automated pancreas segmentation with two-stage 3D convolutional neural networks. In Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, Shenzhen, China, pp. 201\u2013209, 2019. DOI: https:\/\/doi.org\/10.1007\/978-3-030-32245-8_23."},{"issue":"9","key":"1313_CR105","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s11548-018-1797-4","volume":"13","author":"M P Heinrich","year":"2018","unstructured":"M. P. Heinrich, M. Blendowski, O. Oktay. TernaryNet: Faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions. International Journal of Computer Assisted Radiology and Surgery, vol. 13, no. 9, pp. 1311\u20131320, 2018. DOI: https:\/\/doi.org\/10.1007\/s11548-018-1797-4.","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"1313_CR106","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.compbiomed.2019.01.018","volume":"107","author":"H Moon","year":"2019","unstructured":"H. Moon, Y. K. Huo, R. G. Abramson, R. A. Peters, A. Assad, T. K. Moyo, M. R. Savona, B. A. Landman. Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline. Computers in Biology and Medicine, vol. 107, pp. 109\u2013117, 2019. DOI: https:\/\/doi.org\/10.1016\/j.compbiomed.2019.01.018.","journal-title":"Computers in Biology and Medicine"},{"key":"1313_CR107","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/978-3-030-32692-0_70","volume-title":"Proceedings of the 10th International Workshop on Machine Learning in Medical Imaging","author":"H Wang","year":"2019","unstructured":"H. Wang, G. T. Wang, Z. H. Xu, W. H. Lei, S. T. Zhang. High- and low-level feature enhancement for medical image segmentation. In Proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, Springer, Shenzhen, China, pp. 611\u2013619, 2019. DOI: https:\/\/doi.org\/10.1007\/978-3-030-32692-0_70."},{"key":"1313_CR108","doi-asserted-by":"publisher","DOI":"10.1117\/12.2254437","volume-title":"Proceedings of SPIE 10133, Medical Imaging 2017","author":"J Q Liu","year":"2017","unstructured":"J. Q. Liu, Y. K. Huo, Z. B. Xu, A. Assad, R. G. Abramson, B. A. Landman. Multi-atlas spleen segmentation on CT using adaptive context learning. In Proceedings of SPIE 10133, Medical Imaging 2017, SPIE, Orlando, USA, Article number 1013309, 2017. DOI: https:\/\/doi.org\/10.1117\/12.2254437."},{"issue":"9","key":"1313_CR109","doi-asserted-by":"publisher","first-page":"2782","DOI":"10.1109\/TMI.2020.2975347","volume":"39","author":"L Zhang","year":"2020","unstructured":"L. Zhang, J. M. Zhang, P. Y. Shen, G. M. Zhu, P. Li, X. Y. Lu, H. Zhang, S. A. Shah, M. Bennamoun. Block level skip connections across cascaded V-Net for multi-organ segmentation. IEEE Transactions on Medical Imaging, vol. 39, no. 9, pp. 2782\u20132793, 2020. DOI: https:\/\/doi.org\/10.1109\/TMI.2020.2975347.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"1313_CR110","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.diii.2019.05.008","volume":"101","author":"S Park","year":"2020","unstructured":"S. Park, L. C. Chu, E. K. Fishman, A. L. Yuille, B. Vogelstein, K. W. Kinzler, K. M. Horton, R. H. Hruban, E. S. Zinreich, D. Fadaei Fouladi, S. Shayesteh, J. Graves, S. Kawamoto. Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation. Diagnostic and Interventional Imaging, vol. 101, no. 1, pp. 35\u201344, 2020. DOI: https:\/\/doi.org\/10.1016\/j.diii.2019.05.008.","journal-title":"Diagnostic and Interventional Imaging"},{"issue":"10","key":"1313_CR111","doi-asserted-by":"publisher","first-page":"4971","DOI":"10.1002\/mp.14429","volume":"47","author":"Y H Chen","year":"2020","unstructured":"Y. H. Chen, D. Ruan, J. Y. Xiao, L. X. Wang, B. Sun, R. Saouaf, W. S. Yang, D. B. Li, Z. Y. Fan. Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks. Medical Physics, vol. 47, no. 10, pp. 4971\u20134982, 2020. DOI: https:\/\/doi.org\/10.1002\/mp.14429.","journal-title":"Medical Physics"},{"issue":"11","key":"1313_CR112","doi-asserted-by":"publisher","first-page":"3619","DOI":"10.1109\/TMI.2020.3001036","volume":"39","author":"X Fang","year":"2020","unstructured":"X. Fang, P. K. Yan. Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Transactions on Medical Imaging, vol. 39, no. 11, pp. 3619\u20133629, 2020. DOI: https:\/\/doi.org\/10.1109\/TMI.2020.3001036.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"8","key":"1313_CR113","doi-asserted-by":"publisher","first-page":"987","DOI":"10.3348\/kjr.2020.0237","volume":"21","author":"Y Ahn","year":"2020","unstructured":"Y. Ahn, J. S. Yoon, S. S. Lee, H. I. Suk, J. H. Son, Y. S. Sung, Y. Lee, B. K. Kang, H. S. Kim. Deep learning algorithm for automated segmentation and volume measurement of the liver and spleen using portal venous phase computed tomography images. Korean Journal of Radiology, vol. 21, no. 8, pp. 987\u2013997, 2020. DOI: https:\/\/doi.org\/10.3348\/kjr.2020.0237.","journal-title":"Korean Journal of Radiology"},{"key":"1313_CR114","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2019.02.006","volume":"54","author":"M P Heinrich","year":"2019","unstructured":"M. P. Heinrich, O. Oktay, N. Bouteldja. OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions. Medical Image Analysis, vol. 54, pp. 1\u20139, 2019. DOI: https:\/\/doi.org\/10.1016\/j.media.2019.02.006.","journal-title":"Medical Image Analysis"},{"key":"1313_CR115","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/978-3-030-00937-3_49","volume-title":"Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention","author":"H Kakeya","year":"2018","unstructured":"H. Kakeya, T. Okada, Y. Oshiro. 3D U-JAPA-Net: Mixture of convolutional networks for abdominal multi-organ CT segmentation. In Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, Granada, Spain, pp. 352\u2013360, 2018. DOI: https:\/\/doi.org\/10.1007\/978-3-030-00937-3_49."},{"key":"1313_CR116","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/978-981-32-9515-5_10","volume-title":"Proceedings of Conference on Computing in Engineering and Technology","author":"R G Bisen","year":"2019","unstructured":"R. G. Bisen, A. M. Rajrkar, R. R. Manthalkar. Segmentation, detection, and classification of liver tumors for designing a CAD system. In Proceedings of Conference on Computing in Engineering and Technology, Springer, Singapore, pp. 103\u2013111, 2019. DOI: https:\/\/doi.org\/10.1007\/978-981-32-9515-5_10."},{"key":"1313_CR117","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1007\/978-3-030-26763-6_45","volume-title":"Proceedings of the 15th International Conference on Intelligent Computing Theories and Application","author":"Y X Chen","year":"2019","unstructured":"Y. X. Chen, S. Y. Li, S. Yang, W. Y. Luo. Liver Segmentation in CT Images with Adversarial Learning. In Proceedings of the 15th International Conference on Intelligent Computing Theories and Application, Springer, Nanchang, China, pp. 470\u2013480, 2019. DOI: https:\/\/doi.org\/10.1007\/978-3-030-26763-6_45."},{"issue":"1","key":"1313_CR118","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.jhep.2018.09.014","volume":"70","author":"S K Asrani","year":"2019","unstructured":"S. K. Asrani, H. Devarbhavi, J. Eaton, P. S. Kamath. Burden of liver diseases in the world. Journal of Hepatology, vol. 70, no. 1, pp. 151\u2013171, 2019. DOI: https:\/\/doi.org\/10.1016\/j.jhep.2018.09.014.","journal-title":"Journal of Hepatology"},{"key":"1313_CR119","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.eswa.2019.01.055","volume":"126","author":"Z Liu","year":"2019","unstructured":"Z. Liu, Y. Q. Song, V. S. Sheng, L. M. Wang, R. Jiang, X. L. Zhang, D. Q. Yuan. Liver CT sequence segmentation based with improved U-Net and graph cut. Expert Systems with Applications, vol. 126, pp. 54\u201363, 2019. DOI: https:\/\/doi.org\/10.1016\/j.eswa.2019.01.055.","journal-title":"Expert Systems with Applications"},{"key":"1313_CR120","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1109\/ISBI45749.2020.9098701","volume-title":"Proceedings of the 17th IEEE International Symposium on Biomedical Imaging","author":"Y Zhang","year":"2020","unstructured":"Y. Zhang, J. Wu, B. X. Jiang, D. C. Ji, Y. F. Chen, E. X. Wu, X. Y. Tang. Deep learning and unsupervised fuzzy C-means based level-set segmentation for liver tumor. In Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, IEEE, Iowa City, USA, pp. 1193\u20131196, 2020. DOI: https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098701."},{"key":"1313_CR121","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/ISBI45749.2020.9098656","volume-title":"Proceedings of the 17th IEEE International Symposium on Biomedical Imaging","author":"R Dey","year":"2020","unstructured":"R. Dey, Y. Hong. Hybrid cascaded neural network for liver lesion segmentation. In Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, IEEE, Iowa City, USA, pp. 1173\u20131177, 2020. DOI: https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098656."},{"key":"1313_CR122","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.clinimag.2017.07.004","volume":"47","author":"Z Farooq","year":"2018","unstructured":"Z. Farooq, A. H. Behzadi, J. D. Blumenfeld, Y. Z. Zhao, M. R. Prince. Comparison of MRI segmentation techniques for measuring liver cyst volumes in autosomal dominant polycystic kidney disease. Clinical Imaging, vol. 47, pp. 41\u201346, 2018. DOI: https:\/\/doi.org\/10.1016\/j.clinimag.2017.07.004.","journal-title":"Clinical Imaging"},{"key":"1313_CR123","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1109\/ISBI45749.2020.9098609","volume-title":"Proceedings of the 17th IEEE International Symposium on Biomedical Imaging","author":"X S Hou","year":"2020","unstructured":"X. S. Hou, C. M. Xie, F. Y. Li, J. P. Wang, C. F. Lv, G. T. Xie, Y. Nan. A triple-stage self-guided network for kidney tumor segmentation. In Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, IEEE, Iowa City, USA, pp. 341\u2013344, 2020. DOI: https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098609."},{"issue":"5","key":"1313_CR124","doi-asserted-by":"publisher","DOI":"10.1007\/s40134-016-0148-x","volume":"4","year":"2016","unstructured":"M. Tubay, S. Zelasko. Multimodality imaging of the gallbladder: Spectrum of pathology and associated imaging findings. Current Radiology Reports, vol. 4, no. 5, Article number 21, 2016. DOI: https:\/\/doi.org\/10.1007\/s40134-016-0148-x.","journal-title":"Current Radiology Reports"},{"issue":"6","key":"1313_CR125","doi-asserted-by":"publisher","first-page":"3158","DOI":"10.1007\/s11227-017-2230-4","volume":"75","author":"V Muneeswaran","year":"2019","unstructured":"V. Muneeswaran, M. P. Rajasekaran. Automatic segmentation of gallbladder using bio-inspired algorithm based on a spider web construction model. The Journal of Supercomputing, vol. 75, no. 6, pp. 3158\u20133183, 2019. DOI: https:\/\/doi.org\/10.1007\/s11227-017-2230-4.","journal-title":"The Journal of Supercomputing"},{"issue":"2","key":"1313_CR126","doi-asserted-by":"publisher","first-page":"297","DOI":"10.7863\/ultra.14.10033","volume":"35","author":"C Serra","year":"2016","unstructured":"C. Serra, F. Pallotti, M. Bortolotti, C. Caputo, C. Felicani, R. D. Giorgio, G. Barbara, E. Nardi, A. M. M. Labate. A new reliable method for evaluating gallbladder dynamics: The 3-dimensional sonographic examination. Journal of Ultrasound in Medicine, vol. 35, no. 2, pp. 297\u2013304, 2016. DOI: https:\/\/doi.org\/10.7863\/ultra.14.10033.","journal-title":"Journal of Ultrasound in Medicine"},{"key":"1313_CR127","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/USBEREIT.2019.8736587","volume-title":"Proceedings of Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology","author":"G V Timokhov","year":"2019","unstructured":"G. V. Timokhov, E. A. Semenova. The decision support algorithm for a surgeon in preoperative planning of mini-laparotomy gallbladder surgery from an arbitrary incision site. In Proceedings of Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, IEEE, Yekaterinburg, Russia, pp. 74\u201377, 2019. DOI: https:\/\/doi.org\/10.1109\/USBEREIT.2019.8736587."},{"key":"1313_CR128","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/BIOROB.2018.8488152","volume-title":"Proceedings of the 7th IEEE International Conference on Biomedical Robotics and Biomechatronics","author":"S Tognarelli","year":"2018","unstructured":"S. Tognarelli, M. Brancadoro, V. Dolosor, A. Menciassi. Soft tool for gallbladder retraction in minimally invasive surgery based on layer jamming. In Proceedings of the 7th IEEE International Conference on Biomedical Robotics and Biomechatronics, IEEE, Enschede, Netherlands, pp. 67\u201372, 2018. DOI: https:\/\/doi.org\/10.1109\/BIOROB.2018.8488152."},{"issue":"8","key":"1313_CR129","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1002\/jso.24797","volume":"116","author":"L L Cong","year":"2017","unstructured":"L. L. Cong, Z. Q. Cai, P. Guo, C. Chen, D. C. Liu, W. Z. Li, L. Wang, Y. L. Zhao, S. B. Si, Z. M. Geng. Decision of surgical approach for advanced gallbladder adenocarcinoma based on a Bayesian network. Journal of Surgical Oncology, vol. 116, no. 8, pp. 1123\u20131131, 2017. DOI: https:\/\/doi.org\/10.1002\/jso.24797.","journal-title":"Journal of Surgical Oncology"},{"key":"1313_CR130","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/IEEM.2018.8607352","volume-title":"Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management","author":"Z Zhang","year":"2018","unstructured":"Z. Zhang, N. Li, H. Y. Gao, Z. Q. Cai, S. B. Si, Z. M. Geng. Preoperative analysis for clinical features of unsuspected gallbladder cancer based on random forest. In Proceedings of IEEE International Conference on Industrial Engineering and Engineering Management, IEEE, Bangkok, Thailand, pp. 1160\u20131164, 2018. DOI: https:\/\/doi.org\/10.1109\/IEEM.2018.8607352."},{"key":"1313_CR131","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.clinimag.2018.04.010","volume":"50","author":"A P Wasnik","year":"2018","unstructured":"A. P. Wasnik, M. S. Davenport, R. K. Kaza, W. J. Weadock, A. Udager, N. Keshavarzi, B. Nan, K. E. Maturen. Diagnostic accuracy of MDCT in differentiating gallbladder cancer from acute and xanthogranulomatous cholecystitis. Clinical Imaging, vol. 50, pp. 223\u2013228, 2018. DOI: https:\/\/doi.org\/10.1016\/j.clinimag.2018.04.010.","journal-title":"Clinical Imaging"},{"issue":"1","key":"1313_CR132","doi-asserted-by":"publisher","DOI":"10.1186\/s40824-018-0128-8","volume":"22","year":"2018","unstructured":"B. J. Ha, S. Park. Classification of gallstones using Fourier-transform infrared spectroscopy and photography. Biomaterials Research, vol. 22, no. 1, Article number 18, 2018. DOI: https:\/\/doi.org\/10.1186\/s40824-018-0128-8.","journal-title":"Biomaterials Research"},{"key":"1313_CR133","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1109\/ACCESS.2019.2961125","volume":"8","author":"S Liu","year":"2020","unstructured":"S. Liu, Q. Liu, X. R. Yuan, R. Y. Hu, S. J. Liang, S. H. Feng, Y. H. Ai, Y. Zhang. Automatic pancreas segmentation via coarse location and ensemble learning. IEEE Access, vol. 8, pp. 2906\u20132914, 2020. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2019.2961125.","journal-title":"IEEE Access"},{"issue":"5","key":"1313_CR134","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1109\/JBHI.2020.3023462","volume":"25","author":"P J Hu","year":"2021","unstructured":"P. J. Hu, X. Li, Y. Tian, T. Y. Tang, T. S. Zhou, X. L. Bai, S. Q. Zhu, T. B. Liang, J. S. Li. Automatic pancreas segmentation in CT images with distance-based saliency-aware DenseASPP network. IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 5, pp. 1601\u20131611, 2021. DOI: https:\/\/doi.org\/10.1109\/JBHI.2020.3023462.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"1","key":"1313_CR135","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TVCG.2016.2598463","volume":"23","author":"I Gutenko","year":"2017","unstructured":"I. Gutenko, K. Dmitriev, A. E. Kaufman, M. A. Barish. AnaFe: Visual analytics of image-derived temporal features - focusing on the spleen. IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 171\u2013180, 2017. DOI: https:\/\/doi.org\/10.1109\/TVCG.2016.2598463.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"issue":"5","key":"1313_CR136","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/TMI.2018.2881110","volume":"38","author":"Y K Huo","year":"2019","unstructured":"Y. K. Huo, Z. B. Xu, S. X. Bao, C. Bermudez, H. Moon, P. Parvathaneni, T. K. Moyo, M. R. Savona, A. Assad, R. G. Abramson, B. A. Landman. Splenomegaly segmentation on multi-modal MRI using deep convolutional networks. IEEE Transactions on Medical Imaging, vol. 38, no. 5, pp. 1185\u20131196, 2019. DOI: https:\/\/doi.org\/10.1109\/TMI.2018.2881110.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"1313_CR137","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/EMBC.2018.8512182","volume-title":"Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","author":"A Wood","year":"2018","unstructured":"A. Wood, S. M. R. Soroushmehr, N. Farzaneh, D. Fessell, K. R. Ward, J. Gryak, D. Kahrobaei, K. Na. Fully automated spleen localization and segmentation using machine learning and 3D active contours. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Honolulu, USA, pp. 53\u201356, 2018. DOI: https:\/\/doi.org\/10.1109\/EMBC.2018.8512182."},{"key":"1313_CR138","doi-asserted-by":"publisher","first-page":"3498","DOI":"10.1109\/ICIP.2018.8451205","volume-title":"Proceedings of the 25th IEEE International Conference on Image Processing","author":"T K\u00fcstner","year":"2018","unstructured":"T. K\u00fcstner, S. M\u00fcller, M. Fischer, J. Weiss, K. Nikolaou, F. Bamberg, B. Yang, F. Schick, S. Gatidis. Semantic organ segmentation in 3D whole-body MR images. In Proceedings of the 25th IEEE International Conference on Image Processing, IEEE, Athens, Greece, pp. 3498\u20133502, 2018. DOI: https:\/\/doi.org\/10.1109\/ICIP.2018.8451205."},{"key":"1313_CR139","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/978-3-030-32226-7_17","volume-title":"Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention","author":"H Zheng","year":"2019","unstructured":"H. Zheng, L. F. Lin, H. J. Hu, Q. W. Zhang, Q. Q. Chen, Y. Iwamoto, X. H. Han, Y. W. Chen, R. F. Tong, J. Wu. Semi-supervised segmentation of liver using adversarial learning with deep atlas prior. In Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, Shenzhen, China, pp. 148\u2013156, 2019. DOI: https:\/\/doi.org\/10.1007\/978-3-030-32226-7_17."},{"issue":"2","key":"1313_CR140","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11548-016-1467-3","volume":"12","author":"F Lu","year":"2017","unstructured":"F. Lu, F. Wu, P. J. Hu, Z. Peng, D. X. Kong. Automatic 3D liver location and segmentation via convolutional neural network and graph cut. International Journal of Computer Assisted Radiology and Surgery, vol. 12, no. 2, pp. 171\u2013182, 2017. DOI: https:\/\/doi.org\/10.1007\/s11548-016-1467-3.","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"1313_CR141","first-page":"2540","volume-title":"Proceedings of the 3rd International Conference on Electrical, Computer, Electronics & Biomedical Engineering & 3rd International Conference on Business, Economics, and Environment Issues","author":"S Sangewar","year":"2017","unstructured":"S. Sangewar, P. Daigavane, G. Somulu. A comparative study of k-means and graph cut method of liver segmentation. In Proceedings of the 3rd International Conference on Electrical, Computer, Electronics & Biomedical Engineering & 3rd International Conference on Business, Economics, and Environment Issues, Bangkok, Thailand, pp. 2540\u20132543, 2017."},{"key":"1313_CR142","doi-asserted-by":"crossref","unstructured":"W. W. Wu, Z. H. Zhou, S. C. Wu, Y. H. Zhang. Automatic liver segmentation on volumetric CT images using supervoxel-based graph cuts. Computational and Mathematical Methods in Medicine, vol. 2016, Article number 9093721, 2016.","DOI":"10.1155\/2016\/9093721"},{"key":"1313_CR143","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cmpb.2017.02.015","volume":"143","author":"M Liao","year":"2017","unstructured":"M. Liao, Y. Q. Zhao, X. Y. Liu, Y. Z. Zeng, B. J. Zou, X. F. Wang, F. Y. Shih. Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. Computer Methods and Programs in Biomedicine, vol. 143, pp. 1\u201312, 2017. DOI: https:\/\/doi.org\/10.1016\/j.cmpb.2017.02.015.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"1313_CR144","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.compbiomed.2018.02.012","volume":"95","author":"Q Huang","year":"2018","unstructured":"Q. Huang, H. Ding, X. D. Wang, G. Z. Wang. Fully automatic liver segmentation in CT images using modified graph cuts and feature detection. Computers in Biology and Medicine, vol. 95, pp. 198\u2013208, 2018. DOI: https:\/\/doi.org\/10.1016\/j.compbiomed.2018.02.012.","journal-title":"Computers in Biology and Medicine"},{"key":"1313_CR145","doi-asserted-by":"publisher","unstructured":"C. L. Wang, H. R. Roth, T. Kitasaka, M. Oda, Y. Hayashi, Y. Yoshino, T. Yamamoto, N. Sassa, M. Goto, K. Mori. Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams. Computerized Medical Imaging and Graphics, vol. 77, Article number 101642, 2019. DOI: https:\/\/doi.org\/10.1016\/j.compmedimag.2019.101642.","DOI":"10.1016\/j.compmedimag.2019.101642"},{"issue":"3","key":"1313_CR146","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1002\/mrm.26806","volume":"79","author":"U Yoruk","year":"2018","unstructured":"U. Yoruk, B. A. Hargreaves, S. S. Vasanawala. Automatic renal segmentation for MR urography using 3D-GrabCut and random forests. Magnetic Resonance in Medicine, vol. 79, no. 3, pp. 1696\u20131707, 2018. DOI: https:\/\/doi.org\/10.1002\/mrm.26806.","journal-title":"Magnetic Resonance in Medicine"},{"issue":"9","key":"1313_CR147","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1016\/j.acra.2018.01.004","volume":"25","author":"Q Zheng","year":"2018","unstructured":"Q. Zheng, S. Warner, G. Tasian, Y. Fan. A dynamic graph cuts method with integrated multiple feature maps for segmenting kidneys in 2D ultrasound images. Academic Radiology, vol. 25, no. 9, pp. 1136\u20131145, 2018. DOI: https:\/\/doi.org\/10.1016\/j.acra.2018.01.004.","journal-title":"Academic Radiology"},{"key":"1313_CR148","doi-asserted-by":"publisher","first-page":"101766","DOI":"10.1016\/j.media.2020.101766","volume":"65","author":"Y D Xia","year":"2020","unstructured":"Y. D. Xia, D. Yang, Z. D. Yu, F. Z. Liu, J. Z. Cai, L. Q. Yu, Z. T. Zhu, D. G. Xu, A. Yuille, H. Roth. Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation. Medical Image Analysis, vol. 65, pp. 101766, 2020. DOI: https:\/\/doi.org\/10.1016\/j.media.2020.101766.","journal-title":"Medical Image Analysis"},{"key":"1313_CR149","doi-asserted-by":"publisher","first-page":"101934","DOI":"10.1016\/j.media.2020.101934","volume":"68","author":"K Chaitanya","year":"2021","unstructured":"K. Chaitanya, N. Karani, C. F. Baumgartner, E. Erdil, A. Becker, O. Donati, E. Konukoglu. Semi-supervised task-driven data augmentation for medical image segmentation. Medical Image Analysis, vol. 68, pp. 101934, 2021. DOI: https:\/\/doi.org\/10.1016\/j.media.2020.101934.","journal-title":"Medical Image Analysis"},{"key":"1313_CR150","doi-asserted-by":"publisher","first-page":"3635","DOI":"10.1109\/WACV45572.2020.9093608","volume-title":"Proceedings of IEEE Winter Conference on Applications of Computer Vision","author":"Y D Xia","year":"2020","unstructured":"Y. D. Xia, F. Z. Liu, D. Yang, J. Z. Cai, L. Q. Yu, Z. T. Zhu, D. G. Xu, A. Yuille, H. Roth. 3D semi-supervised learning with uncertainty-aware multi-view Co-training. In Proceedings of IEEE Winter Conference on Applications of Computer Vision, IEEE, Snowmass, USA, pp. 3635\u20133644, 2020. DOI: https:\/\/doi.org\/10.1109\/WACV45572.2020.9093608."},{"key":"1313_CR151","first-page":"755","volume-title":"Proceedings of International Conference on Medical Imaging with Deep Learning","author":"R D Soberanis-Mukul","year":"2020","unstructured":"R. D. Soberanis-Mukul, N. Navab, S. Albarqouni. Uncertainty-based graph convolutional networks for organ segmentation refinement. In Proceedings of International Conference on Medical Imaging with Deep Learning, Montr\u00e9al, Canada, pp. 755\u2013769, 2020."},{"key":"1313_CR152","doi-asserted-by":"publisher","DOI":"10.1117\/12.2512842","volume-title":"Proceedings of SPIE 10949, Medical Imaging 2019: Image Processing","author":"Y C Tang","year":"2019","unstructured":"Y. C. Tang, Y. K. Huo, Y. X. Xiong, H. Moon, A. Assad, T. K. Moyo, M. R. Savona, R. Abramson, B. A. Landman. Improving splenomegaly segmentation by learning from heterogeneous multi-source labels. In Proceedings of SPIE 10949, Medical Imaging 2019: Image Processing, SPIE, San Diego, USA, Article number 1094908, 2019. DOI: https:\/\/doi.org\/10.1117\/12.2512842."},{"key":"1313_CR153","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-030-59719-1_15","volume-title":"Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention","author":"R Huang","year":"2020","unstructured":"R. Huang, Y. J. Zheng, Z. Q. Hu, S. T. Zhang, H. S. Li. Multi-organ segmentation via Co-training weight-averaged models from few-organ datasets. In Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, Lima, Peru, pp. 146\u2013155, 2020. DOI: https:\/\/doi.org\/10.1007\/978-3-030-59719-1_15."},{"key":"1313_CR154","doi-asserted-by":"publisher","first-page":"424","DOI":"10.23919\/MVA.2017.7986891","volume-title":"Proceedings of the 15th IAPR International Conference on Machine Vision Applications","author":"T Takaoka","year":"2017","unstructured":"T. Takaoka, Y. Mochizuki, H. Ishikawa. Multiple-organ segmentation by graph cuts with supervoxel nodes. In Proceedings of the 15th IAPR International Conference on Machine Vision Applications, IEEE, Nagoya, Japan, pp. 424\u2013427, 2017. DOI: https:\/\/doi.org\/10.23919\/MVA.2017.7986891."},{"issue":"12","key":"1313_CR155","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.1109\/TMI.2018.2851780","volume":"37","author":"R K\u00e9chichian","year":"2018","unstructured":"R. K\u00e9chichian, S. Valette, M. Desvignes. Automatic multiorgan segmentation via multiscale registration and graph cut. IEEE Transactions on Medical Imaging, vol. 37, no. 12, pp. 2739\u20132749, 2018. DOI: https:\/\/doi.org\/10.1109\/TMI.2018.2851780.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"5","key":"1313_CR156","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/s11548-017-1571-z","volume":"12","author":"A Saito","year":"2017","unstructured":"A. Saito, S. Nawano, A. Shimizu. Fast approximation for joint optimization of segmentation, shape, and location priors, and its application in gallbladder segmentation. International Journal of Computer Assisted Radiology and Surgery, vol. 12, no. 5, pp. 743\u2013756, 2017. DOI: https:\/\/doi.org\/10.1007\/s11548-017-1571-z.","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"1313_CR157","doi-asserted-by":"publisher","DOI":"10.1117\/12.2254147","volume-title":"Proceedings of SPIE 10133, Medical Imaging 2017: Image Processing","author":"Y K Huo","year":"2017","unstructured":"Y. K. Huo, J. Q. Liu, Z. B. Xu, R. L. Harrigan, A. Assad, R. G. Abramson, B. A. Landman. Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly. In Proceedings of SPIE 10133, Medical Imaging 2017: Image Processing, SPIE, Orlando, USA, Article number 101330A, 2017. DOI: https:\/\/doi.org\/10.1117\/12.2254147."},{"issue":"9","key":"1313_CR158","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.1109\/TMM.2017.2729400","volume":"19","author":"H M\u00fcller","year":"2017","unstructured":"H. M\u00fcller, D. Unay. Retrieval from and understanding of large-scale multi-modal medical datasets: A review. Transactions on Multimedia, vol. 19, no. 9, pp. 2093\u20132104, 2017. DOI: https:\/\/doi.org\/10.1109\/TMM.2017.2729400.","journal-title":"Transactions on Multimedia"},{"key":"1313_CR159","doi-asserted-by":"crossref","unstructured":"N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. H. Wu, X. W. Ding. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis, vol. 63, no. 101693, 2020.","DOI":"10.1016\/j.media.2020.101693"},{"key":"1313_CR160","doi-asserted-by":"publisher","first-page":"269","DOI":"10.2991\/978-94-6239-255-7_48","volume-title":"Proceedings of the 23rd International Conference on Industrial Engineering and Engineering Management 2016: Theory and Application of Industrial Engineering","author":"Z Q Cai","year":"2017","unstructured":"Z. Q. Cai, P. Guo, S. Li, L. L. Cong, Z. M. Geng. Gallbladder diagnosis and importance analysis based on bayesian network. In Proceedings of the 23rd International Conference on Industrial Engineering and Engineering Management 2016: Theory and Application of Industrial Engineering, pp. 269\u2013273, 2017. DOI: https:\/\/doi.org\/10.2991\/978-94-6239-255-7_48."},{"issue":"3","key":"1313_CR161","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s10278-016-9934-5","volume":"30","author":"N Jain","year":"2017","unstructured":"N. Jain, V. Kumar. Liver ultrasound image segmentation using region-difference filters. Journal of Digital Imaging, vol. 30, no. 3, pp. 376\u2013390, 2017. DOI: https:\/\/doi.org\/10.1007\/s10278-016-9934-5.","journal-title":"Journal of Digital Imaging"},{"key":"1313_CR162","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.patcog.2015.09.001","volume":"50","author":"C F Shi","year":"2016","unstructured":"C. F. Shi, Y. Z. Cheng, F. Liu, Y. D. Wang, J. Bai, S. Tamura. A hierarchical local region-based sparse shape composition for liver segmentation in CT scans. Pattern Recognition, vol. 50, pp. 88\u2013106, 2016. DOI: https:\/\/doi.org\/10.1016\/j.patcog.2015.09.001.","journal-title":"Pattern Recognition"},{"issue":"11","key":"1313_CR163","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1016\/j.ejmp.2016.10.002","volume":"32","author":"M Liao","year":"2016","unstructured":"M. Liao, Y. Q. Zhao, W. Wang, Y. Z. Zeng, Q. Yang, F. Y. Shih, B. J. Zou. Efficient liver segmentation in CT images based on graph cuts and bottleneck detection. Physica Medica, vol. 32, no. 11, pp. 1383\u20131396, 2016. DOI: https:\/\/doi.org\/10.1016\/j.ejmp.2016.10.002.","journal-title":"Physica Medica"},{"key":"1313_CR164","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/978-981-15-5232-8_50","volume-title":"Proceedings of the 2nd International Conference on Intelligent Technologies and Applications","author":"M A Azam","year":"2020","unstructured":"M. A. Azam, K. B. Khan, M. Aqeel, A. R. Chishti, M. N. Abbasi. Analysis of the MIDAS and OASIS biomedical databases for the application of multimodal image processing. In Proceedings of the 2nd International Conference on Intelligent Technologies and Applications, Springer, Bahawalpur, Pakistan, pp. 581\u2013592, 2020. DOI: https:\/\/doi.org\/10.1007\/978-981-15-5232-8_50."},{"key":"1313_CR165","doi-asserted-by":"publisher","unstructured":"A. Qayyum, A. Lalande, F. Meriaudeau. Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging. Computers in Biology and Medicine, vol. 127, Article number 104097, 2020. DOI: https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104097.","DOI":"10.1016\/j.compbiomed.2020.104097"},{"key":"1313_CR166","volume-title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","author":"A L Simpson","year":"2019","unstructured":"A. L. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken, A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, O. Ronneberger, R. M. Summers, P. Bilic, P. F. Christ, R. K. G. Do, M. Gollub, J. Golia-Pernicka, S. H. Heckers, W. R. Jarnagin, M. K. McHugo, S. Napel, E. Vorontsov, L. Maier-Hein, M. J. Cardoso. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. [Online], Available: http:\/\/arxiv.org\/abs\/1902.09063, 2019."},{"key":"1313_CR167","doi-asserted-by":"crossref","unstructured":"A. E. Kavur, N. S. Gezer, M. Baris, S. Aslan, P. H. Conze, V. Groza, D. D. Pham, S. Chatterjee, P. Ernst, S. \u00d6zkan, B. Baydar, D. Lachinov, S. Han, J. Pauli, F. Isensee, M. Perkonigg, R. Sathish, R. Rajan, D. Sheet, G. Dovletov, O. Speck, A. N\u00fcrnberger, K. H. Maier-Hein, G. B. Akar, G. \u00dcnal, O. Dicle, M. A. Selver. CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis, vol. 69, Article number 101950, 2020.","DOI":"10.1016\/j.media.2020.101950"},{"key":"1313_CR168","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/978-3-319-49644-3_9","volume-title":"Cloud-Based Benchmarking of Medical Image Analysis","author":"A B Spanier","year":"2017","unstructured":"A. B. Spanier, L. Joskowicz. Automatic atlas-free multi-organ segmentation of contrast-enhanced CT scans. Cloud-Based Benchmarking of Medical Image Analysis, Springer, Cham, Germany, pp. 145\u2013164, 2017. DOI: https:\/\/doi.org\/10.1007\/978-3-319-49644-3_9."},{"issue":"1","key":"1313_CR169","volume":"4","year":"2014","unstructured":"F. Prior, K. Smith, A. Sharma, J. Kirby, L. Tarbox, K. Clark, W. Bennett, T. Nolan, J. Freymann. The public cancer radiology imaging collections of the Cancer Imaging Archive. Scientific Data, vol. 4, no. 1, Article number 170124, 2014.","journal-title":"Scientific Data"},{"key":"1313_CR170","doi-asserted-by":"publisher","unstructured":"N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. H. Wu, X. W. Ding. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis, vol. 63, Article number 101693, 2020. DOI: https:\/\/doi.org\/10.1016\/j.media.2020.101693.","DOI":"10.1016\/j.media.2020.101693"},{"key":"1313_CR171","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.cmpb.2017.07.002","volume":"150","author":"Y Z Zeng","year":"2017","unstructured":"Y. Z. Zeng, Y. Q. Zhao, P. Tang, M. Liao, Y. X. Liang, S. H. Liao, B. J. Zou. Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts. Computer Methods and Programs in Biomedicine, vol. 150, pp. 31\u201339, 2017. DOI: https:\/\/doi.org\/10.1016\/j.cmpb.2017.07.002.","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"1","key":"1313_CR172","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-020-00660-9","volume":"10","year":"2020","unstructured":"V. Verma, R. K. Aggarwal. A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: Empirical and theoretical perspective. Social Network Analysis and Mining, vol. 10, no. 1, Article number 43, 2020. DOI: https:\/\/doi.org\/10.1007\/s13278-020-00660-9.","journal-title":"Social Network Analysis and Mining"},{"key":"1313_CR173","doi-asserted-by":"publisher","unstructured":"I. Rizwan I Haque, J. Neubert. Deep learning approaches to biomedical image segmentation. Informatics in Medicine Unlocked, vol. 18, Article number 100297, 2020. DOI: https:\/\/doi.org\/10.1016\/j.imu.2020.100297.","DOI":"10.1016\/j.imu.2020.100297"},{"issue":"6","key":"1313_CR174","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.1007\/s00261-020-02892-x","volume":"46","author":"D Dreizin","year":"2021","unstructured":"D. Dreizin, T. N. Chen, Y. Y. Liang, Y. Y. Zhou, F. Paes, Y. Wang, A. L. Yuille, P. Roth, K. Champ, G. Li, A. McLenithan, J. J. Morrison. Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: A decision tree analysis. Abdominal Radiology, vol. 46, no. 6, pp. 2556\u20132566, 2021. DOI: https:\/\/doi.org\/10.1007\/s00261-020-02892-x.","journal-title":"Abdominal Radiology"},{"issue":"6","key":"1313_CR175","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1007\/s11760-020-01835-9","volume":"15","author":"T L Fan","year":"2021","unstructured":"T. L. Fan, G. L. Wang, X. Wang, Y. Li, H. R. Wang. MSN-Net: A multi-scale context nested U-Net for liver segmentation. Signal, Image and Video Processing, vol. 15, no. 6, pp. 1089\u20131097, 2021. DOI: https:\/\/doi.org\/10.1007\/s11760-020-01835-9.","journal-title":"Signal, Image and Video Processing"},{"key":"1313_CR176","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1007\/978-3-319-46723-8_51","volume-title":"Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"J Z Cai","year":"2016","unstructured":"J. Z. Cai, L. Lu, Z. Z. Zhang, F. Y. Xing, L. Yang, Q. Yin. Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks. In Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Athens, Greece, pp. 442\u2013450, 2016. DOI: https:\/\/doi.org\/10.1007\/978-3-319-46723-8_51."},{"key":"1313_CR177","doi-asserted-by":"publisher","first-page":"76056","DOI":"10.1109\/ACCESS.2020.2988647","volume":"8","author":"Y Zhang","year":"2020","unstructured":"Y. Zhang, B. X. Jiang, J. Wu, D. C. Ji, Y. L. Liu, Y. F. Chen, E. X. Wu, X. Y. Tang. Deep learning initialized and gradient enhanced level-set based segmentation for liver tumor from CT images. IEEE Access, vol. 8, pp. 76056\u201376068, 2020. DOI: https:\/\/doi.org\/10.1109\/ACCESS.2020.2988647.","journal-title":"IEEE Access"},{"key":"1313_CR178","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.neucom.2021.01.091","volume":"437","author":"H Y Li","year":"2021","unstructured":"H. Y. Li, Z. X. Sun, Y. J. Wu, Y. C. Song. Semi-supervised point cloud segmentation using self-training with label confidence prediction. Neurocomputing, vol. 437, pp. 227\u2013237, 2021. DOI: https:\/\/doi.org\/10.1016\/j.neucom.2021.01.091.","journal-title":"Neurocomputing"},{"key":"1313_CR179","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/978-981-13-5802-9_56","volume-title":"Emerging Research in Electronics, Computer Science and Technology","author":"T M Geethanjali","year":"2019","unstructured":"T. M. Geethanjali, Minavathi. Review on recent methods for segmentation of liver using computed tomography and magnetic resonance imaging modalities. In Emerging Research in Electronics, Computer Science and Technology, Springer, Singapore, pp. 631\u2013647, 2019. DOI: https:\/\/doi.org\/10.1007\/978-981-13-5802-9_56."}],"container-title":["International Journal of Automation and Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-021-1313-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11633-021-1313-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-021-1313-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T15:42:00Z","timestamp":1667058120000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11633-021-1313-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,25]]},"references-count":179,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["1313"],"URL":"https:\/\/doi.org\/10.1007\/s11633-021-1313-0","relation":{},"ISSN":["1476-8186","1751-8520"],"issn-type":[{"value":"1476-8186","type":"print"},{"value":"1751-8520","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,25]]},"assertion":[{"value":"7 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}