{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:14:19Z","timestamp":1743012859647,"version":"3.40.3"},"publisher-location":"Cham","reference-count":97,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031163630"},{"type":"electronic","value":"9783031163647"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16364-7_15","type":"book-chapter","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T09:03:38Z","timestamp":1664355818000},"page":"184-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Perspective Review on Deep Learning Models to Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"H. Heartlin","family":"Maria","sequence":"first","affiliation":[]},{"given":"A. Maria","family":"Jossy","sequence":"additional","affiliation":[]},{"given":"S.","family":"Malarvizhi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Van Hiep Phung, E.J.: A high\u2010accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl. Sci. 9, 4500 (2019)","DOI":"10.3390\/app9214500"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Ke, Q., Boussaid, F.: Computer vision for human\u2013machine interaction. Comput. Vis. Assist. Heathcare (2018)","DOI":"10.1016\/B978-0-12-813445-0.00005-8"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Yang, B., Guo, H.: Design of cyber-physical-social systems with forensic-awareness based on deep learning. Adv. Comput. 120, 39\u201379 (2020)","DOI":"10.1016\/bs.adcom.2020.09.001"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Thillaikkarasi, R., Saravanan, S.: An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM. J. Med. Syst. 43, 1\u20137 (2019)","DOI":"10.1007\/s10916-019-1223-7"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Sajid, S., Hussain, S.: Brain tumor detection and segmentation in MR images using deep learning. Arab. J. Sci. Eng. 44, 9249\u20139261 (2019)","DOI":"10.1007\/s13369-019-03967-8"},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"103697","DOI":"10.1109\/ACCESS.2020.2998901","volume":"8","author":"F Ramzan","year":"2020","unstructured":"Ramzan, F., Khan, M.U.G., Iqbal, S., Saba, T., Rehman, A.: Volumetric segmentation of brain regions from MRI scans using 3D convolutional neural networks. IEEE Access 8, 103697\u2013103709 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2998901","journal-title":"IEEE Access"},{"issue":"5","key":"15_CR7","doi-asserted-by":"publisher","first-page":"439","DOI":"10.3103\/S0146411618050048","volume":"52","author":"G Anand Kumar","year":"2018","unstructured":"Anand Kumar, G., Sridevi, P.V.: 3D deep learning for automatic brain MR tumor segmentation with T-spline intensity inhomogeneity correction. Autom. Control Comput. Sci. 52(5), 439\u2013450 (2018). https:\/\/doi.org\/10.3103\/S0146411618050048","journal-title":"Autom. Control Comput. Sci."},{"key":"15_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101692","volume":"63","author":"M Ben Naceur","year":"2020","unstructured":"Ben Naceur, M., Akil, M., Saouli, R., Kachouri, R.: Fully automatic brain tumour segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Med. Image Anal. 63, 101692 (2020). https:\/\/doi.org\/10.1016\/j.media.2020.101692. Epub   29 Apr 2020. PMID: 32417714","journal-title":"Med. Image Anal."},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"60505","DOI":"10.1109\/ACCESS.2020.2982197","volume":"8","author":"N Feng","year":"2020","unstructured":"Feng, N., Geng, X., Qin, L.: Study on MRI medical image segmentation technology based on CNN-CRF model. IEEE Access 8, 60505\u201360514 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2982197","journal-title":"IEEE Access"},{"issue":"2","key":"15_CR10","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/TMI.2018.2866845","volume":"38","author":"Z Xiong","year":"2019","unstructured":"Xiong, Z., Fedorov, V.V., Fu, X., Cheng, E., Macleod, R., Zhao, J.: Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Trans. Med. Imaging 38(2), 515\u2013524 (2019). https:\/\/doi.org\/10.1109\/TMI.2018.2866845. PMID: 30716023; PMCID: PMC6364320","journal-title":"IEEE Trans. Med. Imaging"},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.asoc.2019.02.036","volume":"78","author":"M Mittal","year":"2019","unstructured":"Mittal, M., Goyal, L.M., Kaur, S., Kaur, I., Amit Verma, D., Hemanth, J.: Deep learning based enhanced tumour segmentation approach for MR brain images. Appl. Soft Comput. 78, 346\u2013354 (2019)","journal-title":"Appl. Soft Comput."},{"key":"15_CR12","doi-asserted-by":"publisher","first-page":"26665","DOI":"10.1109\/ACCESS.2020.2966879","volume":"8","author":"W Deng","year":"2020","unstructured":"Deng, W., Shi, Q., Wang, M., Zheng, B., Ning, N.: Deep learning-based HCNN and CRF-RRNN model for brain tumor segmentation. IEEE Access 8, 26665\u201326675 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2966879","journal-title":"IEEE Access"},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1016\/j.bbe.2021.05.007","volume":"41","author":"AE Ilesanmi","year":"2021","unstructured":"Ilesanmi, A.E., Chaumrattanakul, U., Makhanov, S.S.: A method for segmentation of tumours in breast ultrasound images using the variant enhanced deep learning. Biocybern. Biomed. Eng. 41, 802\u2013818 (2021)","journal-title":"Biocybern. Biomed. Eng."},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ijmedinf.2018.06.003","volume":"117","author":"MA Al-antari","year":"2018","unstructured":"Al-antari, M.A., Al-masni, M.A., Choi, M.-T., Han, S.-M., Kim, T.-S.: A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inform. 117, 44\u201354 (2018)","journal-title":"Int. J. Med. Inform."},{"key":"15_CR15","doi-asserted-by":"publisher","first-page":"5119","DOI":"10.1109\/ACCESS.2020.3045906","volume":"9","author":"JM Webb","year":"2021","unstructured":"Webb, J.M., Meixner, D.D., Adusei, S.A., Polley, E.C., Fatemi, M., Alizad, A.: Automatic deep learning semantic segmentation of ultrasound thyroid cineclips using recurrent fully convolutional networks. IEEE Access 9, 5119\u20135127 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2020.3045906","journal-title":"IEEE Access"},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"63482","DOI":"10.1109\/ACCESS.2020.2982390","volume":"8","author":"V Kumar","year":"2020","unstructured":"Kumar, V., et al.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access 8, 63482\u201363496 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2982390","journal-title":"IEEE Access"},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"33795","DOI":"10.1109\/ACCESS.2019.2904094","volume":"7","author":"N Nguyen","year":"2019","unstructured":"Nguyen, N., Lee, S.: Robust boundary segmentation in medical images using a consecutive deep encoder-decoder network. IEEE Access 7, 33795\u201333808 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2904094","journal-title":"IEEE Access"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Al-Louzi, O.: Progressive multifocal leukoencephalopathy lesion and brain parenchymal segmentation from MRI using serial deep convolutional neural networks. NeuroImage Clin. 28, 102499 (2020)","DOI":"10.1016\/j.nicl.2020.102499"},{"key":"15_CR19","doi-asserted-by":"publisher","unstructured":". Chen, Y, Wang, Y., Hu, F., Wang, D.: A lung dense deep convolution neural network for robust lung parenchyma segmentation. IEEE Access 8, 93527\u201393547 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2993953","DOI":"10.1109\/ACCESS.2020.2993953"},{"issue":"15","key":"15_CR20","doi-asserted-by":"publisher","first-page":"9677","DOI":"10.1007\/s00521-021-05732-1","volume":"33","author":"J Ramya","year":"2021","unstructured":"Ramya, J., Rajakumar, M.P., Uma Maheswari, B.: HPWO-LS-based deep learning approach with S-ROA-optimized optic cup segmentation for fundus image classification. Neural Comput. Appl. 33(15), 9677\u20139690 (2021). https:\/\/doi.org\/10.1007\/s00521-021-05732-1","journal-title":"Neural Comput. Appl."},{"key":"15_CR21","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.media.2019.07.005","volume":"57","author":"D Karimi","year":"2019","unstructured":"Karimi, D., et al.: Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Med. Image Anal. 57, 186\u2013196 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.07.005","journal-title":"Med. Image Anal."},{"key":"15_CR22","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.cmpb.2018.12.031","volume":"170","author":"K Yan","year":"2019","unstructured":"Yan, K., Wang, X., Kim, J., Khadra, M., Fulham, M., Feng, D.: A propagation-DNN: deep combination learning of multi-level features for MR prostate segmentation. Comput. Methods Programs Biomed. 170, 11\u201321 (2019)","journal-title":"Comput. Methods Programs Biomed."},{"key":"15_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102076","volume":"115","author":"M Salvi","year":"2021","unstructured":"Salvi, M., et al.: A hybrid deep learning approach for gland segmentation in prostate histopathological images. Artif. Intell. Med. 115, 102076 (2021)","journal-title":"Artif. Intell. Med."},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Hu, H., et al.: Automatic segmentation of left and right ventricles in cardiac MRI using 3D-ASM and deep learning. Signal Process. Image Commun. 96, 116303, 101902 (2021)","DOI":"10.1016\/j.image.2021.116303"},{"key":"15_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101717","volume":"81","author":"H Abdeltawab","year":"2021","unstructured":"Abdeltawab, H., et al.: A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images. Comput. Med. Imaging Graph. 81, 101717 (2021)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"12","key":"15_CR26","doi-asserted-by":"publisher","first-page":"2742","DOI":"10.1007\/s00259-020-04800-3","volume":"47","author":"X Tang","year":"2020","unstructured":"Tang, X., et al.: Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT. Eur. J. Nucl. Med. Mol. Imaging 47(12), 2742\u20132752 (2020). https:\/\/doi.org\/10.1007\/s00259-020-04800-3","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"11","key":"15_CR27","doi-asserted-by":"publisher","first-page":"8733","DOI":"10.1007\/s00330-021-07850-9","volume":"31","author":"H Ryu","year":"2021","unstructured":"Ryu, H., Shin, S.Y., Lee, J.Y., Lee, K.M., Kang, H.-J., Yi, J.: Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning. Eur. Radiol. 31(11), 8733\u20138742 (2021). https:\/\/doi.org\/10.1007\/s00330-021-07850-9","journal-title":"Eur. Radiol."},{"key":"15_CR28","doi-asserted-by":"publisher","first-page":"27047","DOI":"10.1109\/ACCESS.2020.2971391","volume":"8","author":"T Apiparakoon","year":"2020","unstructured":"Apiparakoon, T., et al.: MaligNet: semisupervised learning for bone lesion instance segmentation using bone scintigraphy. IEEE Access 8, 27047\u201327066 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2971391","journal-title":"IEEE Access"},{"key":"15_CR29","doi-asserted-by":"publisher","unstructured":"Allehaibi, K.H.S., et al.: Segmentation and classification of cervical cells using deep learning. IEEE Access 7, 116925\u2013116941 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2936017","DOI":"10.1109\/ACCESS.2019.2936017"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Lee, J.: Segmentation of coronary calcified plaque in intravascular OCT images using a two-step deep learning approach. IEEE Access 8, 225581\u2013225593 (2020)","DOI":"10.1109\/ACCESS.2020.3045285"},{"key":"15_CR31","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ijmedinf.2019.01.005","volume":"124","author":"N Nida","year":"2019","unstructured":"Nida, N., Irtaza, A., Javed, A., Yousaf, M.H., Mahmood, M.T.: Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Int. J. Med. Inform. 124, 37\u201348 (2019)","journal-title":"Int. J. Med. Inform."},{"key":"15_CR32","doi-asserted-by":"publisher","first-page":"131257","DOI":"10.1109\/ACCESS.2020.3008899","volume":"8","author":"TM Khan","year":"2020","unstructured":"Khan, T.M., Alhussein, M., Aurangzeb, K., Arsalan, M., Naqvi, S.S., Nawaz, S.J.: Residual connection-based encoder decoder network (RCED-Net) for retinal vessel segmentation. IEEE Access 8, 131257\u2013131272 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3008899","journal-title":"IEEE Access"},{"key":"15_CR33","doi-asserted-by":"crossref","unstructured":"Veena, H.: A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images. J. King Saud Univ. (2021)","DOI":"10.1016\/j.jksuci.2021.02.003"},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Vaishnavi, J.: An efficient adaptive histogram based segmentation and extraction model for the classification of severities on diabetic retinopathy. Multimedia Tools Appl. 79, 30439\u201330452 (2020)","DOI":"10.1007\/s11042-020-09288-5"},{"issue":"17","key":"15_CR35","doi-asserted-by":"publisher","first-page":"10799","DOI":"10.1007\/s00521-020-05082-4","volume":"33","author":"S Lu","year":"2020","unstructured":"Lu, S., Wang, S.-H., Zhang, Y.-D.: Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm. Neural Comput. Appl. 33(17), 10799\u201310811 (2020). https:\/\/doi.org\/10.1007\/s00521-020-05082-4","journal-title":"Neural Comput. Appl."},{"key":"15_CR36","doi-asserted-by":"crossref","unstructured":"Chen, J.: Medical image segmentation and reconstruction of prostate tumor based on 3D AlexNet. Comput. Methods Programs Biomed. 200, 105878 (2021)","DOI":"10.1016\/j.cmpb.2020.105878"},{"key":"15_CR37","doi-asserted-by":"crossref","unstructured":"Mansour, R.F.: Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed. Eng. Lett. 8, 41\u201357 (2018)","DOI":"10.1007\/s13534-017-0047-y"},{"key":"15_CR38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X.: Deep residual learning for image recognition. arXiv (2015)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"15_CR39","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.1007\/s11548-020-02237-5","volume":"15","author":"S Jeevakala","year":"2020","unstructured":"Jeevakala, S., Sreelakshmi, C., Ram, K., Rangasami, R., Sivaprakasam, M.: Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques. Int. J. Comput. Assist. Radiol. Surg. 15(11), 1859\u20131867 (2020). https:\/\/doi.org\/10.1007\/s11548-020-02237-5","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"15_CR40","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.ijmedinf.2019.03.015","volume":"126","author":"S Guo","year":"2019","unstructured":"Guo, S., Wang, K., Kang, H., Zhang, Y., Gao, Y., Li, T.: BTS-DSN: deeply supervised neural network with short connections for retinal vessel segmentation. Int. J. Med. Inform. 126, 105\u2013113 (2019)","journal-title":"Int. J. Med. Inform."},{"key":"15_CR41","unstructured":"Zhao, X.: EBioMedicine (2020)"},{"key":"15_CR42","doi-asserted-by":"crossref","unstructured":"Liu, Y.: Automatic segmentation of cervical nuclei based on deep learning and a conditional random field. IEEE Access 6, 53709\u201353721 (2018)","DOI":"10.1109\/ACCESS.2018.2871153"},{"key":"15_CR43","doi-asserted-by":"crossref","unstructured":"Ding, L.: A lightweight U-Net architecture multi-scale convolutional network for pediatric hand bone segmentation in X-ray image. IEEE Access 7, 68436\u201368445 (2019)","DOI":"10.1109\/ACCESS.2019.2918205"},{"key":"15_CR44","doi-asserted-by":"crossref","unstructured":"Pan, X.: A fundus retinal vessels segmentation scheme based on the improved deep learning U-Net model. IEEE Access 7, 122634\u2013122643 (2019)","DOI":"10.1109\/ACCESS.2019.2935138"},{"key":"15_CR45","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Ou, C., Qian, Y., Rehan, R., Yong, A.: Coronary vessel segmentation using multiresolution and multiscale deep learning. Inform. Med. Unlocked 24, 100602 (2021)","DOI":"10.1016\/j.imu.2021.100602"},{"issue":"2","key":"15_CR46","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/TMI.2018.2866845","volume":"38","author":"Z Xiong","year":"2019","unstructured":"Xiong, Z., Fedorov, V.V., Fu, X., Cheng, E., Macleod, R., Zhao, J.: Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Trans. Med Imaging 38(2), 515\u2013524 (2019). https:\/\/doi.org\/10.1109\/TMI.2018.2866845","journal-title":"IEEE Trans. Med Imaging"},{"key":"15_CR47","doi-asserted-by":"publisher","first-page":"64739","DOI":"10.1109\/ACCESS.2020.2985095","volume":"8","author":"SY Han","year":"2020","unstructured":"Han, S.Y., Kwon, H.J., Kim, Y., Cho, N.I.: Noise-robust pupil center detection through CNN-based segmentation with shape-prior loss. IEEE Access 8, 64739\u201364749 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2985095","journal-title":"IEEE Access"},{"key":"15_CR48","doi-asserted-by":"crossref","unstructured":"Daoud, B., Morooka, K., Kurazume, R., Leila, F., Mnejja, W., Daoud, J.: 3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning. Comput. Med. Imaging Graph. 77, 101644 (2019)","DOI":"10.1016\/j.compmedimag.2019.101644"},{"key":"15_CR49","doi-asserted-by":"publisher","first-page":"152452","DOI":"10.1109\/ACCESS.2020.3017449","volume":"8","author":"K Alsaih","year":"2020","unstructured":"Alsaih, K., Yusoff, M.Z., Faye, I., Tang, T.B., Meriaudeau, F.: Retinal fluid segmentation using ensembled 2-dimensionally and 2.5-dimensionally deep learning networks. IEEE Access 8, 152452\u2013152464 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3017449","journal-title":"IEEE Access"},{"issue":"20","key":"15_CR50","doi-asserted-by":"publisher","first-page":"30143","DOI":"10.1007\/s11042-020-10430-6","volume":"80","author":"PS Mangipudi","year":"2021","unstructured":"Mangipudi, P.S., Pandey, H.M., Choudhary, A.: Improved optic disc and cup segmentation in Glaucomatic images using deep learning architecture. Multimedia Tools Appl. 80(20), 30143\u201330163 (2021). https:\/\/doi.org\/10.1007\/s11042-020-10430-6","journal-title":"Multimedia Tools Appl."},{"key":"15_CR51","doi-asserted-by":"publisher","first-page":"29299","DOI":"10.1109\/ACCESS.2020.2972318","volume":"8","author":"BJ Bhatkalkar","year":"2020","unstructured":"Bhatkalkar, B.J., Reddy, D.R., Prabhu, S., Bhandary, S.V.: Improving the performance of convolutional neural network for the segmentation of optic disc in fundus images using attention gates and conditional random fields. IEEE Access 8, 29299\u201329310 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2972318","journal-title":"IEEE Access"},{"key":"15_CR52","doi-asserted-by":"publisher","first-page":"219322","DOI":"10.1109\/ACCESS.2020.3041519","volume":"8","author":"M Sardar","year":"2020","unstructured":"Sardar, M., Banerjee, S., Mitra, S.: Iris segmentation using interactive deep learning. IEEE Access 8, 219322\u2013219330 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3041519","journal-title":"IEEE Access"},{"key":"15_CR53","doi-asserted-by":"crossref","unstructured":"Lu, Y.: Automatic tumor segmentation by means of deep convolutional U-Net with pre-trained encoder in PET images. IEEE Access 8, 113636\u2013113648 (2020)","DOI":"10.1109\/ACCESS.2020.3003138"},{"key":"15_CR54","doi-asserted-by":"publisher","first-page":"113636","DOI":"10.1109\/ACCESS.2020.3003138","volume":"8","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Lin, J., Chen, S., He, H., Cai, Y.: Automatic tumor segmentation by means of deep convolutional U-Net with pre-trained encoder in PET images. IEEE Access 8, 113636\u2013113648 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3003138","journal-title":"IEEE Access"},{"key":"15_CR55","doi-asserted-by":"publisher","first-page":"153589","DOI":"10.1109\/ACCESS.2020.3018160","volume":"8","author":"M Ali","year":"2020","unstructured":"Ali, M., Gilani, S.O., Waris, A., Zafar, K., Jamil, M.: Brain tumour image segmentation using deep networks. IEEE Access 8, 153589\u2013153598 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3018160","journal-title":"IEEE Access"},{"key":"15_CR56","doi-asserted-by":"crossref","unstructured":"Naser, M.A., Jamal Deen, M.: Brain tumour segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput. Biol. Med. 121, 103758 (2020)","DOI":"10.1016\/j.compbiomed.2020.103758"},{"key":"15_CR57","doi-asserted-by":"crossref","unstructured":"Tran, S.-T.: A multiple layer U-Net, Un-Net, for liver and liver tumor segmentation in CT. IEEE Access 9, 3752\u20133764 (2020)","DOI":"10.1109\/ACCESS.2020.3047861"},{"key":"15_CR58","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2021.03.050","volume":"446","author":"Z Zhang","year":"2021","unstructured":"Zhang, Z., Li, J., Tian, C., Zhong, Z., Jiao, Z., Gao, X.: Quality-driven deep active learning method for 3D brain MRI segmentation. Neurocomputing 446, 106\u2013117 (2021)","journal-title":"Neurocomputing"},{"key":"15_CR59","doi-asserted-by":"publisher","unstructured":"Lei, T., Wang, R., Zhang, Y., Wan, Y., Liu, C., Nandi, A.K.: DefED-Net: deformable encoder-decoder network for liver and liver tumor segmentation. IEEE Trans. Radiat. Plasma Med. Sci. (2021). https:\/\/doi.org\/10.1109\/TRPMS.2021.3059780","DOI":"10.1109\/TRPMS.2021.3059780"},{"key":"15_CR60","doi-asserted-by":"crossref","unstructured":"Gegundez-Arias, M.E., Marin-Santos, D., Perez-Borrero, I., Vasallo-Vazquez, M.J.: A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model. Comput. Methods Programs Biomed. 205, 106081 (2021)","DOI":"10.1016\/j.cmpb.2021.106081"},{"key":"15_CR61","doi-asserted-by":"crossref","unstructured":"Boudegga, H., Elloumi, Y., Akil, M., Bedoui, M.H., Kachouri, R., Abdallah, A.B.: Fast and efficient retinal blood vessel segmentation method based on deep learning network. Comput. Med. Imaging Graph. 90, 101902 (2021)","DOI":"10.1016\/j.compmedimag.2021.101902"},{"key":"15_CR62","doi-asserted-by":"crossref","unstructured":"Gurpreet, S., et al.: Deep learning based automatic segmentation of cardiac computed tomography. J. Am. Coll. Cardiol. 73, 1643\u20131643 (2019)","DOI":"10.1016\/S0735-1097(19)32249-1"},{"key":"15_CR63","doi-asserted-by":"publisher","first-page":"140108","DOI":"10.1109\/ACCESS.2020.3010800","volume":"8","author":"C Xiao","year":"2020","unstructured":"Xiao, C., Li, Y., Jiang, Y.: Heart coronary artery segmentation and disease risk warning based on a deep learning algorithm. IEEE Access 8, 140108\u2013140121 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3010800","journal-title":"IEEE Access"},{"key":"15_CR64","doi-asserted-by":"publisher","unstructured":"Baskaran, L., et al.: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning (2020). https:\/\/doi.org\/10.1371\/journal.pone.0232573","DOI":"10.1371\/journal.pone.0232573"},{"key":"15_CR65","doi-asserted-by":"publisher","first-page":"172871","DOI":"10.1109\/ACCESS.2019.2956550","volume":"7","author":"L Lu","year":"2019","unstructured":"Lu, L., Jian, L., Luo, J., Xiao, B.: Pancreatic segmentation via ringed residual U-Net. IEEE Access 7, 172871\u2013172878 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2956550","journal-title":"IEEE Access"},{"key":"15_CR66","doi-asserted-by":"publisher","first-page":"82153","DOI":"10.1109\/ACCESS.2020.2991424","volume":"8","author":"T Liu","year":"2020","unstructured":"Liu, T., Tian, Y., Zhao, S., Huang, X., Wang, Q.: Residual convolutional neural network for cardiac image segmentation and heart disease diagnosis. IEEE Access 8, 82153\u201382161 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2991424","journal-title":"IEEE Access"},{"key":"15_CR67","doi-asserted-by":"publisher","first-page":"51557","DOI":"10.1109\/ACCESS.2019.2910348","volume":"7","author":"SC Van De Leemput","year":"2019","unstructured":"Van De Leemput, S.C., Meijs, M., Patel, A., Meijer, F.J.A., Van Ginneken, B., Manniesing, R.: Multiclass brain tissue segmentation in 4D CT using convolutional neural networks. IEEE Access 7, 51557\u201351569 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2910348","journal-title":"IEEE Access"},{"key":"15_CR68","doi-asserted-by":"publisher","first-page":"120946","DOI":"10.1109\/ACCESS.2020.3006317","volume":"8","author":"N Yamanakkanavar","year":"2020","unstructured":"Yamanakkanavar, N., Lee, B.: Using a patch-wise M-Net convolutional neural network for tissue segmentation in brain MRI images. IEEE Access 8, 120946\u2013120958 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3006317","journal-title":"IEEE Access"},{"key":"15_CR69","doi-asserted-by":"crossref","unstructured":"Zhang, F., et al.: Deep learning based segmentation of brain tissue from diffusion MRI. Neuroimage 233, 117934 (2021)","DOI":"10.1016\/j.neuroimage.2021.117934"},{"key":"15_CR70","doi-asserted-by":"crossref","unstructured":"Jonmohamadi, Y.: Automatic segmentation of multiple structures in knee arthroscopy using deep learning. IEEE Access 8, 51853\u201351861 (2020)","DOI":"10.1109\/ACCESS.2020.2980025"},{"key":"15_CR71","doi-asserted-by":"publisher","unstructured":"Hariyani, Y.S., Eom, H., Park, C.: DA-CapNet: dual attention deep learning based on U-Net for nailfold capillary segmentation. IEEE Access 8, 10543\u201310553 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2965651","DOI":"10.1109\/ACCESS.2020.2965651"},{"key":"15_CR72","doi-asserted-by":"crossref","unstructured":"Chen, S.: U-Net plus: deep semantic segmentation for esophagus and esophageal cancer in computed tomography images. IEEE Access 7, 82867\u201382877 (2019)","DOI":"10.1109\/ACCESS.2019.2923760"},{"key":"15_CR73","doi-asserted-by":"crossref","unstructured":"Li, S.: Attention dense-U-net for automatic breast mass segmentation in digital mammogram. IEEE Access 7, 59037\u201359047 (2019)","DOI":"10.1109\/ACCESS.2019.2914873"},{"issue":"2","key":"15_CR74","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1109\/JBHI.2020.2994970","volume":"25","author":"S Stenman","year":"2021","unstructured":"Stenman, S., et al.: Antibody supervised training of a deep learning based algorithm for leukocyte segmentation in papillary thyroid carcinoma. IEEE J. Biomed. Health Inform. 25(2), 422\u2013428 (2021). https:\/\/doi.org\/10.1109\/JBHI.2020.2994970","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"15_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104075","volume":"128","author":"S Lal","year":"2021","unstructured":"Lal, S., Das, D., Alabhya, K., Kanfade, A., Kumar, A., Kini, J.: NucleiSegNet: robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images. Comput. Biol. Med. 128, 104075 (2021)","journal-title":"Comput. Biol. Med."},{"key":"15_CR76","doi-asserted-by":"crossref","unstructured":"Gonzalez, Y., et al.: Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach. Med. Image Anal. 68, 101896 (2021)","DOI":"10.1016\/j.media.2020.101896"},{"key":"15_CR77","doi-asserted-by":"publisher","first-page":"84040","DOI":"10.1109\/ACCESS.2019.2924744","volume":"7","author":"X Li","year":"2019","unstructured":"Li, X., Wang, Y., Tang, Q., Fan, Z., Yu, J.: Dual U-Net for the segmentation of overlapping glioma nuclei. IEEE Access 7, 84040\u201384052 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2924744","journal-title":"IEEE Access"},{"key":"15_CR78","doi-asserted-by":"crossref","unstructured":"Cheng, J., Tian, S., Yu, L., Ma, X., Xing, Y.: A deep learning algorithm using contrast-enhanced computed tomography (CT) images for segmentation and rapid automatic detection of aortic dissection. Biomed. Signal Process. Control 62, 102145 (2020)","DOI":"10.1016\/j.bspc.2020.102145"},{"key":"15_CR79","doi-asserted-by":"publisher","first-page":"110189","DOI":"10.1109\/ACCESS.2020.3001571","volume":"8","author":"C Huang","year":"2020","unstructured":"Huang, C., Ding, H., Liu, C.: Segmentation of cell images based on improved deep learning approach. IEEE Access 8, 110189\u2013110202 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3001571","journal-title":"IEEE Access"},{"key":"15_CR80","doi-asserted-by":"publisher","unstructured":"Zheng, B., et al.: MSD-Net: multi-scale discriminative network for COVID-19 lung infection segmentation on CT. IEEE Access 8, 185786\u2013185795 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3027738","DOI":"10.1109\/ACCESS.2020.3027738"},{"key":"15_CR81","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar, A., Modzelewski, R., Li, H., Ruan, S.: Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: classification and segmentation. Comput. Biol. Med 126, 104037 (2020). https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104037","journal-title":"Comput. Biol. Med"},{"key":"15_CR82","doi-asserted-by":"crossref","unstructured":"Jayapandian, C.P., Chen, Y., Janowczyk, A.R., Palmer, M.B.: Development and evaluation of deep learning\u2013based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney Int. 99(1), 86\u2013101 (2021)","DOI":"10.1016\/j.kint.2020.07.044"},{"key":"15_CR83","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhang, T., Li, M., Bueno, R., Jayender, J.: 3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation. Comput. Med. Imaging Graph. 88, 101814 (2021)","DOI":"10.1016\/j.compmedimag.2020.101814"},{"key":"15_CR84","doi-asserted-by":"crossref","unstructured":"Pham, V.-T., Tran, T.-T., Wang, P.-C., Chen, P.-Y., Lo, M.-T.: EAR-UNet: a deep learning-based approach for segmentation of tympanic membranes from otoscopic images. Artif. Intell. Med. 115, 102065 (2021)","DOI":"10.1016\/j.artmed.2021.102065"},{"key":"15_CR85","doi-asserted-by":"crossref","unstructured":"Zhang, Q.: Automatic epicardial fat segmentation and quantification of CT scans using dual U-Nets with a morphological processing layer. IEEE Access 8, 128032\u2013128041 (2020)","DOI":"10.1109\/ACCESS.2020.3008190"},{"key":"15_CR86","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Zhou, J., Zhang, B., Jia, W., Wu, E.: Automatic epicardial fat segmentation and quantification of CT scans using dual U-nets with a morphological processing layer. IEEE Access 8, 128032\u2013128041 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3008190","DOI":"10.1109\/ACCESS.2020.3008190"},{"key":"15_CR87","doi-asserted-by":"crossref","unstructured":"Marzola, F., van Alfen, N., Doorduin, J., Meiburger, K.M.: Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment. Comput. Biol. Med. 135, 104623 (2021)","DOI":"10.1016\/j.compbiomed.2021.104623"},{"key":"15_CR88","doi-asserted-by":"publisher","unstructured":"Ding, L., Zhao, K., Zhang, X., Wang, X., Zhang, J.: A lightweight U-Net architecture multi-scale convolutional network for pediatric hand bone segmentation in X-ray image. IEEE Access 7, 68436\u201368445 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2918205","DOI":"10.1109\/ACCESS.2019.2918205"},{"key":"15_CR89","doi-asserted-by":"crossref","unstructured":"Ding, Y.: A stacked multi-connection simple reducing net for brain tumor segmentation. IEEE Access 7, 104011\u2013104024 (2019)","DOI":"10.1109\/ACCESS.2019.2926448"},{"key":"15_CR90","doi-asserted-by":"publisher","unstructured":"Civit-Masot, J., Luna-Perej\u00f3n, F., Vicente-D\u00edaz, S., Rodr\u00edguez Corral, J.M., Civit, A.: TPU cloud-based generalized U-Net for eye fundus image segmentation. IEEE Access 7,142379\u2013142387 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2944692","DOI":"10.1109\/ACCESS.2019.2944692"},{"key":"15_CR91","doi-asserted-by":"publisher","unstructured":"Rahman, T., et al.: Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access 8, 191586\u2013191601  (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3031384","DOI":"10.1109\/ACCESS.2020.3031384"},{"key":"15_CR92","doi-asserted-by":"publisher","unstructured":"Zeng, G., et al.: MRI-based 3D models of the hip joint enables radiation-free computer-assisted planning of periacetabular osteotomy for treatment of hip dysplasia using deep learning for automatic segmentation. Eur. J. Radiol. Open 8, 100303 (2020). https:\/\/doi.org\/10.1016\/j.ejro.2020.100303","DOI":"10.1016\/j.ejro.2020.100303"},{"key":"15_CR93","doi-asserted-by":"crossref","unstructured":"Al-Kofahi, Y.: A deep learning-based algorithm for 2-D cell segmentation in microscopy images . BMC Inform.  19, 1\u201311 (2018)","DOI":"10.1186\/s12859-018-2375-z"},{"key":"15_CR94","doi-asserted-by":"crossref","unstructured":"Milletari, F.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultra-sound. Comput. Vis. Image Underst. 164, 92\u2013102 (2017)","DOI":"10.1016\/j.cviu.2017.04.002"},{"key":"15_CR95","doi-asserted-by":"crossref","unstructured":"Milletari, F., et al.:  Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultra-sound Comput. Vis. Image Underst. 164, 92\u2013102 (2017)","DOI":"10.1016\/j.cviu.2017.04.002"},{"key":"15_CR96","doi-asserted-by":"crossref","unstructured":"Gibson, E.: Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans. Medi. Imaging. IEEE Trans. Med. Imaging,\u00a037(8), 1822\u20131834 (2018)","DOI":"10.1109\/TMI.2018.2806309"},{"issue":"1","key":"15_CR97","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/s10278-020-00410-5","volume":"34","author":"Y Zeng","year":"2021","unstructured":"Zeng, Y., Tsui, P.-H., Wu, W., Zhou, Z., Wu, S.: Fetal ultrasound image segmentation for automatic head circumference biometry using deeply supervised attention-gated V-Net. J. Digit. Imaging 34(1), 134\u2013148 (2021). https:\/\/doi.org\/10.1007\/s10278-020-00410-5","journal-title":"J. Digit. Imaging"}],"container-title":["IFIP Advances in Information and Communication Technology","Computational Intelligence in Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16364-7_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T09:09:08Z","timestamp":1664356148000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16364-7_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031163630","9783031163647"],"references-count":97,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16364-7_15","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"29 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Intelligence in Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 March 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 March 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccids2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccids.in\/ICCIDS2022\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"96","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}