{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T21:40:57Z","timestamp":1775943657930,"version":"3.50.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/P001009\/1"],"award-info":[{"award-number":["EP\/P001009\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["102431"],"award-info":[{"award-number":["102431"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"name":"School of Biomedical Engineering and Imaging Sciences"},{"DOI":"10.13039\/501100000764","name":"Kings College London","doi-asserted-by":"publisher","award":["WT 203148\/Z\/16\/Z"],"award-info":[{"award-number":["WT 203148\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100000764","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004941","name":"Guy's and St Thomas' NHS Foundation Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004941","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000764","name":"Kings College London","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000764","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022,12,1]]},"DOI":"10.1109\/tpami.2020.3013679","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T21:28:35Z","timestamp":1599254915000},"page":"8766-8778","source":"Crossref","is-referenced-by-count":164,"title":["A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9135-0545","authenticated-orcid":false,"given":"James R.","family":"Clough","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3401-9570","authenticated-orcid":false,"given":"Nicholas","family":"Byrne","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6478-0534","authenticated-orcid":false,"given":"Ilkay","family":"Oksuz","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5093-5854","authenticated-orcid":false,"given":"Veronika A.","family":"Zimmer","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6107-3009","authenticated-orcid":false,"given":"Julia A.","family":"Schnabel","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9965-7015","authenticated-orcid":false,"given":"Andrew P.","family":"King","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32875-7_20"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1177\/2048004016645467"},{"key":"ref33","first-page":"424","article-title":"3D U-Net: Learning dense volumetric segmentation from sparse annotation","author":"\u00e7i\u00e7ek","year":"2016","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Intervention"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66185-8_29"},{"key":"ref31","first-page":"109","article-title":"Efficient inference in fully connected CRFs with Gaussian edge potentials","author":"kr\u00e4henb\u00fchl","year":"2011","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-016-0227-4"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2016.12.001"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2012.03.009"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2006.887364"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23175-9_7"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S1361-8415(96)80007-7"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00931-1_68"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.2000.892133"},{"key":"ref12","first-page":"2573","article-title":"TopoReg: A topological regularizer for classifiers","volume":"89","author":"chen","year":"0"},{"key":"ref13","article-title":"Neural persistence: A complexity measure for deep neural networks using algebraic topology","author":"rieck","year":"2019"},{"key":"ref14","first-page":"1553","article-title":"A topology layer for machine learning","author":"br\u00fcel-gabrielsson","year":"2020"},{"key":"ref15","first-page":"2751","article-title":"Connectivity-optimized representation learning via persistent homology","author":"hofer","year":"2019"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/SDS.2019.000-1"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.07.033"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICABME.2017.8167531"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38868-2_16"},{"key":"ref28","article-title":"The MNIST database of handwritten digits","author":"lecun","year":"1998"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20351-1_2"},{"key":"ref27","year":"2015","journal-title":"GUDHI User and Reference Manual"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref6","first-page":"3136","article-title":"Beyond the pixel-wise loss for topology-aware delineation","author":"mosinska","year":"2018","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref5","first-page":"6965","article-title":"A probabilistic U-net for segmentation of ambiguous images","author":"kohl","year":"2018","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2743464"},{"key":"ref7","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2905990"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2837502"},{"key":"ref20","first-page":"5658","article-title":"Topology-preserving deep image segmentation","author":"hu","year":"2019","journal-title":"Proc 33rd Conf Neural Inf Process Syst"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.93.052138"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1162\/netn_a_00073"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1023\/A:1026135101267"},{"key":"ref24","article-title":"Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis","author":"han","year":"2016"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOAS886"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-017-0109-5"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1090\/conm\/453\/08802"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9940447\/09186664.pdf?arnumber=9186664","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T23:13:44Z","timestamp":1670282024000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9186664\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":42,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.3013679","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]}}}