{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:45Z","timestamp":1740108045322,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100010124","name":"Erich und Gertrud Roggenbuck-Stiftung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010124","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011835","name":"Nieders\u00e4chsischen Krebsgesellschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011835","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003042","name":"Else Kr\u00f6ner-Fresenius-Stiftung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003042","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s00521-022-07599-2","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T11:06:10Z","timestamp":1661339170000},"page":"19629-19638","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated distinction of neoplastic from healthy liver parenchyma based on machine learning"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3441-0529","authenticated-orcid":false,"given":"Olympia","family":"Giannou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anastasios D.","family":"Giannou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitra E.","family":"Zazara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgios","family":"Pavlidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"7599_CR1","unstructured":"Stewart B, Wild CP, et al. World cancer report 2014 2019"},{"key":"7599_CR2","doi-asserted-by":"crossref","unstructured":"Malhotra, et al. \"Histological, molecular and functional subtypes of breast cancers.\" Cancer biology and therapy no10 (2010): 955\u2013960","DOI":"10.4161\/cbt.10.10.13879"},{"key":"7599_CR3","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neucom.2016.01.034","volume":"191","author":"J Xu","year":"2016","unstructured":"Xu J et al (2016) A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191:214\u2013223","journal-title":"Neurocomputing"},{"key":"7599_CR4","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Thijs K, Babak Ehteshami B, Arnaud Arindra AS, Francesco C, Mohsen G, Jeroen AVDL, Bram VG, Clara IS (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"7599_CR5","doi-asserted-by":"crossref","unstructured":"LeCun Y (2019) 1.1 deep learning hardware: Past, present, and future. In 2019 IEEE International Solid-State Circuits Conference-(ISSCC), pages 12\u201319. IEEE","DOI":"10.1109\/ISSCC.2019.8662396"},{"key":"7599_CR6","first-page":"871","volume":"25","author":"N Hawkes","year":"2019","unstructured":"Hawkes N (2019) \"Cancer survival data emphasise importance of early diagnosis. BMJ 25:871","journal-title":"BMJ"},{"issue":"24","key":"7599_CR7","doi-asserted-by":"publisher","first-page":"7887","DOI":"10.3748\/wjg.v20.i24.7887","volume":"20","author":"D Dimitroulis","year":"2014","unstructured":"Dimitroulis D, Tsaparas P, Valsami S et al (2014) Indications, limitations and maneuvers to enable extended hepatectomy: current trends. World J Gastroenterol 20(24):7887\u20137893. https:\/\/doi.org\/10.3748\/wjg.v20.i24.7887","journal-title":"World J Gastroenterol"},{"issue":"8","key":"7599_CR8","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1002\/jso.21654","volume":"102","author":"EK Abdalla","year":"2010","unstructured":"Abdalla EK (2010) \u201cPortal vein embolization (prior to major hepatectomy) effects on regeneration, resectability, and outcome. J Surg Oncol 102(8):960\u2013967","journal-title":"J Surg Oncol"},{"key":"7599_CR9","unstructured":"LiTS dataset: https:\/\/competitions.codalab.org\/competitions\/17094, 3D-IRCADb-01: https:\/\/www.ircad.fr\/research\/data-sets\/liver-segmentation-3d-ircadb-01\/"},{"issue":"12","key":"7599_CR10","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li X, Hao C et al (2018) \"H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imag 37(12):2663\u20132674","journal-title":"IEEE Trans Med Imag"},{"key":"7599_CR11","doi-asserted-by":"crossref","unstructured":"Christ PF, Elshaer MEA, et al. (2016) \u201cAutomatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields,\u201d in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp.415\u2013423","DOI":"10.1007\/978-3-319-46723-8_48"},{"key":"7599_CR12","doi-asserted-by":"crossref","unstructured":"Ben-Cohen A, Diamant I, Klang E, et al (2016) \u201cFully convolutional network for liver segmentation and lesions detection,\u201d in International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Springer, pp. 77\u201385","DOI":"10.1007\/978-3-319-46976-8_9"},{"issue":"2","key":"7599_CR13","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s11548-016-1467-3","volume":"12","author":"F Lu","year":"2017","unstructured":"Lu F, Wu F, Hu P, Peng Z, Kong D (2017) Automatic 3d liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 12(2):171\u2013182","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"7599_CR14","unstructured":"Jin Q, Meng Z, Sun C, et al (2018) \"RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans. arXiv 2018.\" arXiv preprint arXiv:1811.01328."},{"key":"7599_CR15","doi-asserted-by":"crossref","unstructured":"Mulay S, Deepika G, Jeevakala S, Keerthi R, and Mohanasankar S (2019) \"Liver segmentation from multimodal images using HED-mask R-CNN.\" In International Workshop on Multiscale Multimodal Medical Imaging, pp. 68\u201375. Springer, Cham","DOI":"10.1007\/978-3-030-37969-8_9"},{"key":"7599_CR16","unstructured":"Grzegorz C, Andrea S, and Jan HM (2018) \u201cDeep learning based automatic liver tumor segmentation in ct with shape-based post-processing\u201d"},{"key":"7599_CR17","unstructured":"Wen JL, Fucang J, and Qingmao H (2015) \u201cAutomatic segmentation of liver tumor in ct images with deep convolutional neural networks\u201d"},{"key":"7599_CR18","unstructured":"Patrick FC, Florian E et al (2017) \"Automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks.\" CoRR, abs\/1702.05970"},{"key":"7599_CR19","unstructured":"Zhengxin Z, Qingjie L, and Yunhong W (2017) \u201cRoad extraction by deep residual u-net.\u201dCoRR,abs\/1711.10684"},{"key":"7599_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, and Sun J (2016) \u201cDeep residual learning for image recognition,\u201d in Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"7599_CR21","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, and Brox T (2015) \u201cU-net: Convolutional networks for biomedical image segmentation,\u201d in Proceedings of International Conference on Medical image computing and computer-assisted intervention. Springer, pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"7599_CR22","doi-asserted-by":"crossref","unstructured":"Jha D, Pia HS, Michael AR, et al (2019) \"Resunet++: An advanced architecture for medical image segmentation.\" In 2019 IEEE International Symposium on Multimedia (ISM), pp. 225\u20132255. IEEE","DOI":"10.1109\/ISM46123.2019.00049"},{"issue":"2","key":"7599_CR23","doi-asserted-by":"publisher","first-page":"185","DOI":"10.7326\/0003-4819-90-2-185","volume":"90","author":"SB Heymsfield","year":"1979","unstructured":"Heymsfield SB, Timothy F, Bernard N et al (1979) Accurate measurement of liver, kidney, and spleen volume and mass by computerized axial tomography. Annals Internal Med 90(2):185\u2013187","journal-title":"Annals Internal Med"},{"issue":"11","key":"7599_CR24","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1007\/s00268-007-9197-x","volume":"31","author":"AWG Dello Simon","year":"2007","unstructured":"Dello Simon AWG, van Dam RM et al (2007) Liver volumetry plug and play: do it yourself with ImageJ. World Journal Surg 31(11):2215\u20132221","journal-title":"World Journal Surg"},{"issue":"3","key":"7599_CR25","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1177\/1457496915607802","volume":"105","author":"B Bergthor","year":"2016","unstructured":"Bergthor B, Lundgren L (2016) A personal computer freeware as a tool for surgeons to plan liver resections. Scandinavian Surg J 105(3):153\u2013157","journal-title":"Scandinavian Surg J"},{"issue":"3","key":"7599_CR26","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1055\/s-0040-1721534","volume":"4","author":"S Kulkarni Suyash","year":"2020","unstructured":"Kulkarni Suyash S, Nitin Sudhakar S et al (2020) A Validation study of liver volumetry estimation by a semiautomated software in patients undergoing hepatic resections. J Clin Interventional Radiol ISVIR 4(3):154\u2013158","journal-title":"J Clin Interventional Radiol ISVIR"},{"key":"7599_CR27","unstructured":"Jin Gyo J, Young Jae K, Kwang GK, and Won SL (2021)\"Deep 3D attention U-Net based whole liver segmentation for anatomical volume analysis in abdominal CT images\", Proc. SPIE 11792, International Forum on Medical Imaging in Asia 1179204"},{"key":"7599_CR28","unstructured":"DICOM: https:\/\/en.wikipedia.org\/wiki\/DICOM"},{"key":"7599_CR29","unstructured":"Ir J De Backer, Ir W Vos, et al. \u201cCombining mimics and computational uid dynamics (cfd) to assess the efficiency of a mandibular advancement device (mad) to treat obstructive sleep apnea (osa)\u201d (2019)"},{"key":"7599_CR30","unstructured":"Hounsfield Unit (HU): https:\/\/en.wikipedia.org\/wiki\/Hounsfield_scale"},{"key":"7599_CR31","volume-title":"CT angiography and CT perfusion imaging. in brain mapping: the methods","author":"MH Lev","year":"2002","unstructured":"Lev MH, Gonzalez RG (2002) CT angiography and CT perfusion imaging. in brain mapping: the methods. Academic Press"},{"issue":"5","key":"7599_CR32","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual unet. IEEE Geosci Remote Sens Lett 15(5):749\u2013753","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"3","key":"7599_CR33","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/s10278-017-0037-8","volume":"31","author":"Z Yaniv","year":"2018","unstructured":"Yaniv Z et al (2018) SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research. J Digital Imag 31(3):290\u2013303","journal-title":"J Digital Imag"},{"key":"7599_CR34","doi-asserted-by":"publisher","first-page":"104497","DOI":"10.1016\/j.compbiomed.2021.104497","volume":"134","author":"Y-H Nai","year":"2021","unstructured":"Nai Y-H et al (2021) Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset. Computers Biol Med 134:104497","journal-title":"Computers Biol Med"},{"key":"7599_CR35","doi-asserted-by":"crossref","unstructured":"Giannou O, Anastasios DG, Dimitra EZ, D\u00f6rte K, Tobias M, Bj\u00f6rn OS, Michael GK, Gerhard A, Samuel H, and Georgios P (2021) \"Liver cancer trait detection and classification through machine learning on smart mobile devices.\" In\u00a0International Conference on Engineering Applications of Neural Networks, pp. 95\u2013108. Springer, Cham","DOI":"10.1007\/978-3-030-80568-5_8"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07599-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07599-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07599-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T21:05:03Z","timestamp":1666299903000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07599-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,24]]},"references-count":35,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["7599"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07599-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2022,8,24]]},"assertion":[{"value":"2 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}