{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:15:18Z","timestamp":1777655718201,"version":"3.51.4"},"reference-count":86,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Nvidia GPU Grant Program"},{"DOI":"10.13039\/100006785","name":"Google Cloud Research Award","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006785","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1109\/tmi.2019.2927182","type":"journal-article","created":{"date-parts":[[2019,7,7]],"date-time":"2019-07-07T19:09:38Z","timestamp":1562526578000},"page":"3257-3267","source":"Crossref","is-referenced-by-count":267,"title":["Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images"],"prefix":"10.1109","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7587-1562","authenticated-orcid":false,"given":"Faisal","family":"Mahmood","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1820-7950","authenticated-orcid":false,"given":"Daniel","family":"Borders","sequence":"additional","affiliation":[]},{"given":"Richard J.","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Gregory N.","family":"Mckay","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7251-1916","authenticated-orcid":false,"given":"Kevan J.","family":"Salimian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2397-3342","authenticated-orcid":false,"given":"Alexander","family":"Baras","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9808-7383","authenticated-orcid":false,"given":"Nicholas J.","family":"Durr","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2206041"},{"key":"ref72","article-title":"Rethinking monocular depth estimation with adversarial training","author":"chen","year":"2018","journal-title":"arXiv 1808 07528"},{"key":"ref71","article-title":"Semantic segmentation using adversarial networks","author":"luc","year":"2016","journal-title":"ArXiv 1611 08408"},{"key":"ref70","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/aada93","article-title":"Deep learning with cinematic rendering: Fine-tuning deep neural networks using photorealistic medical images","volume":"63","author":"mahmood","year":"2018","journal-title":"Phys Med Biol"},{"key":"ref76","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref77","article-title":"Empirical evaluation of rectified activations in convolutional network","author":"xu","year":"2015","journal-title":"arXiv 1505 00853"},{"key":"ref74","first-page":"1486","article-title":"Deep generative image models using a laplacian pyramid of adversarial networks","author":"denton","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-56537-7_89"},{"key":"ref75","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref38","first-page":"123","article-title":"Brain tumor segmentation using an adversarial network","author":"li","year":"2017","journal-title":"International MICCAI Brainlesion Workshop"},{"key":"ref78","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s12938-018-0518-0","article-title":"Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images","volume":"17","author":"salvi","year":"2018","journal-title":"Biomed Eng Online"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/34.232073"},{"key":"ref33","article-title":"A deep learning algorithm for one-step contour aware nuclei segmentation of histopathological images","author":"cui","year":"2018","journal-title":"arXiv 1803 02786"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2008.4540988"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2677499"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2525803"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/96"},{"key":"ref36","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-018-2285-0"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2865709"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2016.05.003"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2529665"},{"key":"ref61","first-page":"1107","article-title":"A method for normalizing histology slides for quantitative analysis","author":"macenko","year":"2009","journal-title":"Proc IEEE Int Symp Biomed Imag Nano Macro"},{"key":"ref63","article-title":"Unsupervised histopathology image synthesis","author":"hou","year":"2017","journal-title":"arXiv 1712 05021"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/BIBE.2013.6701556"},{"key":"ref64","first-page":"23","article-title":"Domain randomization for transferring deep neural networks from simulation to the real world","author":"tobin","year":"2017","journal-title":"Proc IEEE\/RSJ Int Conf Intell Robots Syst (IROS)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0070221"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005177"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2815013"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1111\/jmi.12043"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.06.021"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.06.005"},{"key":"ref2","author":"katz","year":"2008","journal-title":"Comprehensive Cytopathology"},{"key":"ref1","first-page":"1","article-title":"Histology nomenclature: Past, present and future biological Systems","volume":"2","author":"shostak","year":"2013","journal-title":"Systems Biology"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1097\/PAS.0000000000000381"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2013.2295804"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1093\/ppr\/114.1.s21"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2481436"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2016.2515127"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1109\/TIP.2011.2114358","article-title":"t -Tests, F -tests and Otsu&#x2019;s methods for image thresholding","volume":"20","author":"xue","year":"2011","journal-title":"IEEE Trans Image Process"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2006.884469"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2476509"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/38.946629"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2303294"},{"key":"ref56","article-title":"Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases","volume":"7","author":"janowczyk","year":"2016","journal-title":"J Pathol Inf"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-368"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1142\/9789814644730_0029"},{"key":"ref53","article-title":"Spectral normalization for generative adversarial networks","author":"miyato","year":"2018","journal-title":"arXiv 1802 05957"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2009.2034865"},{"key":"ref11","author":"lynch","year":"1990","journal-title":"Peripheral Blood Smear"},{"key":"ref40","first-page":"101330g","article-title":"Generative adversarial networks for brain lesion detection","volume":"10133","author":"alex","year":"2017","journal-title":"Proc SPIE Med Imag Image Process"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cll.2015.05.016"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21409"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/modpathol.3800161"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"554","DOI":"10.5858\/2002-126-0554-MCFTDO","article-title":"Morphologic criteria for the diagnosis of prostatic adenocarcinoma in needle biopsy specimens a study of 250 consecutive cases in a routine surgical pathology practice","volume":"126","author":"varma","year":"2002","journal-title":"Arch Pathol Lab Med"},{"key":"ref82","article-title":"A review on deep learning techniques applied to semantic segmentation","author":"garcia-garcia","year":"2017","journal-title":"arXiv 1704 06857"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1097\/PAP.0b013e3181cfb788"},{"key":"ref81","doi-asserted-by":"crossref","first-page":"100r","DOI":"10.1186\/gb-2006-7-10-r100","article-title":"CellProfiler: image analysis software for identifying and quantifying cell phenotypes","volume":"7","author":"carpenter","year":"2006","journal-title":"Genome Biol"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-08780-1"},{"key":"ref84","first-page":"2980","article-title":"Mask R-cnn","author":"he","year":"2017","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.5858\/arpa.2015-0093-SA"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.104908"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2019"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1002\/dc.21234","article-title":"Cancer nucleus: Morphology and beyond","volume":"38","author":"dey","year":"2010","journal-title":"Diagnostic Cytopathology"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijsu.2012.11.017"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3109\/10520299509108199"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1080\/10520290500138372"},{"key":"ref85","first-page":"834","article-title":"Nuclei segmentation of fluorescence microscopy images using three dimensional convolutional neural networks","author":"ho","year":"2017","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit Workshops (CVPRW)"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.5539\/gjhs.v8n3p72"},{"key":"ref86","first-page":"316","article-title":"Multi-channel deep transfer learning for nuclei segmentation in glioblastoma cell tissue images","author":"wollmann","year":"2018","journal-title":"Bildverarbeitung fur die Medizin"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1309\/LMXB668WDCBIAWJL"},{"key":"ref49","article-title":"The GAN landscape: Losses, architectures, regularization, and normalization","author":"kurach","year":"2018","journal-title":"arXiv 1807 04720"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2867350"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2842767"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_67"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00919-9_17"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363749"},{"key":"ref42","article-title":"Compressed sensing MRI reconstruction with cyclic loss in generative adversarial networks","author":"quan","year":"2017","journal-title":"arXiv 1709 00753"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref44","article-title":"Deep generative adversarial networks for compressed sensing automates mri","author":"mardani","year":"2017","journal-title":"arXiv 1706 00051"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.03.045"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/9242349\/08756037.pdf?arnumber=8756037","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T18:14:25Z","timestamp":1721499265000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8756037\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11]]},"references-count":86,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2019.2927182","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11]]}}}