{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T07:04:24Z","timestamp":1757574264520,"version":"3.28.0"},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,11,9]]},"DOI":"10.1109\/ipta50016.2020.9286639","type":"proceedings-article","created":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T21:57:10Z","timestamp":1607983030000},"page":"1-6","source":"Crossref","is-referenced-by-count":7,"title":["Literature Review of Deep Learning Models for Liver Vessels Reconstruction"],"prefix":"10.1109","author":[{"given":"Abir","family":"AFFANE","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marie-Ange","family":"LEBRE","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Utkarsh","family":"MITTAL","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antoine","family":"VACAVANT","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref32","article-title":"Linking Convolutional Neural Networks with graph Convolutional Networks: application in Pulmonary Artery-Vein separation","author":"zhai","year":"2019","journal-title":"MICCAI"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref10","article-title":"U-Net: Convolutional Networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"MICCAI"},{"key":"ref11","first-page":"168","article-title":"Dense U-Net based on patch-based Learning for retinal vessel segmentation","volume":"21","author":"wang","year":"2014","journal-title":"Entropy 2019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2890045"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2016.04.003"},{"key":"ref15","first-page":"1013711","article-title":"Robust hepatic vessel segmentation using multi Deep Convolutional Network","volume":"10137","author":"kitrungrotsakul","year":"2017","journal-title":"Medical Imaging 2017 SPIE"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa9262"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.05.002"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8802951"},{"key":"ref19","article-title":"Hepatic vessel segmentation using a reduced filter 3D U-Net in Ultrasound imaging","author":"thomson","year":"0","journal-title":"MIDL 2019 Medical Imaging with Deep Learning"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.02.001"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/BFb0029240","article-title":"3D multi-scale line filter for segmentation and visualizationof curvilinear structures in medicale images","author":"sato","year":"1997","journal-title":"CVRMed-MRCAS’97"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8214975"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2009.07.011"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s12194-008-0031-4"},{"article-title":"Fully Convolutional Networks for semantic segmentation","year":"2014","author":"long","key":"ref29"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1002\/ima.10059"},{"key":"ref8","first-page":"793","article-title":"3D plane cuts and cubic Beizier curve for CT liver volume segmentation according to Couinaud’s classification","volume":"39","author":"butdee","year":"2017","journal-title":"Songklanakarin J Sci Technol"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/1475-925X-10-30"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1037\/1089-2680.1.3.311"},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-46493-0_22","article-title":"A unified multi-scale Deep Convolutional Neural Network for fast object detection","author":"cai","year":"2016"},{"year":"0","key":"ref1","article-title":"EASL clinical practice guidelines: vascular disease of the liver"},{"article-title":"DEEPVESSEL: Retinal vessel segmentation via Deep Learning and conditional Random field","year":"0","author":"fu","key":"ref20"},{"key":"ref22","article-title":"Segmenting retinal bllod vessels with Deep Neural Networks","author":"liskowski","year":"0","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.06.034"},{"article-title":"DEEPVESSELNET: Vessel segmentation, centerlines prediction, and bifurcations detection in 3D Angiographic volumes","year":"2018","author":"tetteh","key":"ref24"},{"article-title":"M2U-NET: Effective and efficient retinal vessel segmentation for resource-Constrained environments","year":"2019","author":"laibacher","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2018.8351049"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101556"}],"event":{"name":"2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)","start":{"date-parts":[[2020,11,9]]},"location":"Paris, France","end":{"date-parts":[[2020,11,12]]}},"container-title":["2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9286598\/9286601\/09286639.pdf?arnumber=9286639","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:50:37Z","timestamp":1656453037000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9286639\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/ipta50016.2020.9286639","relation":{},"subject":[],"published":{"date-parts":[[2020,11,9]]}}}