{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T19:54:46Z","timestamp":1775246086389,"version":"3.50.1"},"reference-count":219,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100016152","name":"Yayasan Universiti Teknologi Petronas","doi-asserted-by":"publisher","award":["YUTP-FRG 015LC0-292"],"award-info":[{"award-number":["YUTP-FRG 015LC0-292"]}],"id":[{"id":"10.13039\/501100016152","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Imagerie et Vision Artificielle (ImViA) \/ Imagerie Fonctionnelle et mol\u00e9culaire et Traitement des Images M\u00e9dicales (IFTIM) Research Grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3090825","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T20:05:51Z","timestamp":1624305951000},"page":"97878-97905","source":"Crossref","is-referenced-by-count":60,"title":["Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2515-4220","authenticated-orcid":false,"given":"Zia","family":"Khan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9812-0435","authenticated-orcid":false,"given":"Norashikin","family":"Yahya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6180-1691","authenticated-orcid":false,"given":"Khaled","family":"Alsaih","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7861-7412","authenticated-orcid":false,"given":"Mohammed Isam","family":"Al-Hiyali","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8656-9913","authenticated-orcid":false,"given":"Fabrice","family":"Meriaudeau","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759300"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1117\/12.2550448"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2935018"},{"key":"ref174","article-title":"Shape-aware meta-learning for generalizing prostate MRI segmentation to unseen domains","author":"liu","year":"2020","journal-title":"arXiv 2007 02035"},{"key":"ref173","doi-asserted-by":"crossref","first-page":"3183","DOI":"10.3390\/s20113183","article-title":"Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI","volume":"20","author":"khan","year":"2020","journal-title":"SENSORS"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105572"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI45749.2020.9098643"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2020.07.011"},{"key":"ref177","doi-asserted-by":"crossref","first-page":"6678","DOI":"10.3390\/app10196678","article-title":"CDA-Net for automatic prostate segmentation in MR images","volume":"10","author":"lu","year":"2020","journal-title":"Appl Sci"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab2f47"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-019-01967-5"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.168.2.3393682"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.4261\/1305-3825.DIR.4708-11.2"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2015.02.009"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/61\/13\/4796"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1097\/MOU.0b013e32835481c2"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/nrurol.2009.27"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-011-2377-y"},{"key":"ref36","article-title":"Computer-aided diagnosis for prostate cancer using multi-parametric magnetic resonance imaging","author":"lemaitre","year":"2016"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23618"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.21819"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1007\/s00066-020-01607-x"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2974574"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.3390\/app10010338"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2020.01.005"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.06.257"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/4185279"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2939389"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2928056"},{"key":"ref187","article-title":"Adversarial networks for the detection of aggressive prostate cancer","author":"kohl","year":"2017","journal-title":"arXiv 1702 08014"},{"key":"ref186","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2014.10.121"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/pcan.2016.72"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.116"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2342031887"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1002\/aja.1000130303"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/S0887-2171(98)90025-7"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1002\/pros.2990020105"},{"key":"ref24","year":"2019","journal-title":"Surveillance Epidemiology and End Results Program Cancer Stat Facts Prostate Cancer"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S0033-8389(05)70150-0","article-title":"Imaging prostate cancer","volume":"38","author":"kyle","year":"2000","journal-title":"Radiologic Clinics North Amer"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/s41391-017-0008-7"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djp001"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICSPC.2007.4728462"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ISCCSP.2008.4537440"},{"key":"ref154","first-page":"1","article-title":"Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images","author":"yu","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1117\/12.2512551"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1145\/3319619.3326864"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2019.102649"},{"key":"ref152","article-title":"Automatic segmentation of prostate zones","author":"mooij","year":"2018","journal-title":"arXiv 1806 07146"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363549"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.07.006"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.3390\/app10072601"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-020-02199-5"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.110"},{"key":"ref59","author":"bankman","year":"2008","journal-title":"Handbook of Medical Image Processing and Analysis"},{"key":"ref58","article-title":"The effectiveness of data augmentation in image classification using deep learning","author":"perez","year":"2017","journal-title":"arXiv 1712 04621"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref56","author":"gonzalez","year":"2004","journal-title":"Digital Image Processing Using Matlab"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1117\/12.2513089"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1137\/18M1169655"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22225"},{"key":"ref52","author":"mallat","year":"2009","journal-title":"A Wavelet Tour of Signal Processing The Sparse Way"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.11091822"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0160-1"},{"key":"ref166","article-title":"A transfer learning approach for automated segmentation of prostate whole gland and transition zone in diffusion weighted MRI","author":"motamed","year":"2019","journal-title":"arXiv 1909 09541"},{"key":"ref165","article-title":"Prostate cancer diagnosis using deep learning with 3D multiparametric MRI","volume":"10134","author":"liu","year":"2017","journal-title":"Proc SPIE"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759554"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.12.031"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101558"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1109\/SCORED.2019.8896248"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1080\/24699322.2019.1649069"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21551"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2015.08.052"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2009.1348"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21392"},{"key":"ref8","year":"2021","journal-title":"The National Cancer Institute (NCI) Prostate Cancer Treatment Stages Prostate Cancer"},{"key":"ref159","first-page":"110","article-title":"HD-Net: Hybrid discriminative network for prostate segmentation in MR images","author":"jia","year":"2019","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1097\/00000478-198812000-00001"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2018.02.033"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26337"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s10552-009-9477-x"},{"key":"ref158","first-page":"132","article-title":"Segmentation of prostate in MRI images using depth separable convolution operations","author":"khan","year":"2020","journal-title":"Proc Int Conf Intell Hum Comput Interact"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910340618"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2013.10.007"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2009.5192986"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2008.02.004"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(10)70146-7"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.mric.2008.07.002"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-003-1843-6"},{"key":"ref43","author":"behrens","year":"2009","journal-title":"Diffusion MRI From Quantitative Measurement to In-vivo Neuroanatomy"},{"key":"ref73","first-page":"801","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","author":"chen","year":"2018","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref72","first-page":"586","article-title":"Semantic road segmentation via multi-scale ensembles of learned features","author":"alvarez","year":"2012","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref71","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"chen","year":"2017","journal-title":"arXiv 1706 05587"},{"key":"ref70","article-title":"Semantic image segmentation with deep convolutional nets and fully connected CRFs","author":"chen","year":"2014","journal-title":"arXiv 1412 7062"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref77","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0620-5"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1118\/1.2842076"},{"key":"ref60","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref61","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref67","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":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383157"},{"key":"ref197","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.1016\/j.cct.2015.02.008"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.12.8510"},{"key":"ref193","article-title":"3D global convolutional adversarial network for prostate MR volume segmentation","author":"jia","year":"2018","journal-title":"arXiv 1807 06742"},{"key":"ref194","article-title":"ProstateGAN: Mitigating data bias via prostate diffusion imaging synthesis with generative adversarial networks","author":"hu","year":"2018","journal-title":"arXiv 1811 05817"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging6090083"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2020.10.014"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2211377"},{"key":"ref190","first-page":"15","article-title":"Using a conditional generative adversarial network (cGAN) for prostate segmentation","author":"grall","year":"2019","journal-title":"Proc Annu Conf Med Image Understand Anal"},{"key":"ref94","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1117\/12.431093","article-title":"Statistical models of appearance for medical image analysis and computer vision","volume":"4322","author":"cootes","year":"2001","journal-title":"Proc SPIE"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01321-2"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-008-0281-y"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32486-5_15"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2052065"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1117\/12.812433"},{"key":"ref90","first-page":"346","article-title":"A hybrid ASM approach for sparse volumetric data segmentation","volume":"15","author":"zhu","year":"2005","journal-title":"Pattern Recognit Image Anal Adv Math Theory Appl"},{"key":"ref98","first-page":"1","article-title":"Prostate MR image segmentation using 3D active appearance models","volume":"2012","author":"maan","year":"2012","journal-title":"MICCAI Prostate MR Image Segmentat Challenge"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1117\/12.911253"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2201498"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1117\/12.911758"},{"key":"ref82","first-page":"10","article-title":"Fast automatic multi-atlas segmentation of the prostate from 3D MR images","author":"dowling","year":"2011","journal-title":"Proc Int Workshop Prostate Cancer Imag"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2057442"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.02.009"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33418-4_51"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1118\/1.3315367"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.828354"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.09.001"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.15353\/vsnl.v2i1.113"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-016-9890-0"},{"key":"ref88","article-title":"Patch-based label fusion for automatic multi-atlas-based prostate segmentation in MR images","volume":"9786","author":"yang","year":"2016","journal-title":"Proc SPIE"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1111\/j.1464-410X.2009.08457.x"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.3390\/info8020049"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-015-9844-y"},{"key":"ref209","article-title":"Segmentation loss odyssey","author":"ma","year":"2020","journal-title":"arXiv 2005 13449"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5374-6"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)32401-1"},{"key":"ref201","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2017.00259"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.4.044501"},{"key":"ref207","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1801993"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.12.002"},{"key":"ref211","article-title":"Distance-IoU loss: Faster and better learning for bounding box regression","author":"zheng","year":"2019","journal-title":"arXiv 1911 08287"},{"key":"ref210","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref212","first-page":"285","article-title":"Boundary loss for highly unbalanced segmentation","author":"kervadec","year":"2019","journal-title":"Proc Int Conf Med Imag Deep Learn"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.1007\/11744078_32"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13300"},{"key":"ref215","first-page":"1","article-title":"Generative feature matching networks","author":"dos santos","year":"2019","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.1.015006"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-015-0068-x"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2012.02.001"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2303821"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-018-1841-4"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2876796"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105821"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.08.006"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2901928"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803043"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759498"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.6.1.014501"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.2.021208"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2903284"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759584"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7965852"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1145\/3364836.3364854"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-26763-6_46"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1145\/3364836.3364855"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1109\/UBMYK48245.2019.8965456"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759314"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1145\/3364836.3364906"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2019.37.15_suppl.e16600"},{"key":"ref2","year":"2017","journal-title":"Atlanta American Cancer Society"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-34110-7_45"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21166"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8297005"},{"key":"ref108","article-title":"Inception-v4, inception-ResNet and the impact of residual connections on learning","author":"szegedy","year":"2016","journal-title":"arXiv 1602 07261"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.4.041307"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2010.07.002"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950630"},{"key":"ref104","article-title":"Active appearance model and deep learning for more accurate prostate segmentation on MRI","volume":"9784","author":"cheng","year":"2016","journal-title":"Proc SPIE"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2508280"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40763-5_32"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2008.918330"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-018-1785-8"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_59"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/nrurol.2010.102"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1186\/1475-2891-9-50"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1080\/15398285.2012.701177"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.22788"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.EPI-06-0754"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1002\/pros.2990170409"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-020-10303-x"},{"key":"ref118","article-title":"Pyramid attention network for semantic segmentation","author":"li","year":"2018","journal-title":"arXiv 1805 10180"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3109\/00365597909181168"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2952534"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/aja.2012.127"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1148\/rg.313105139"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/IST48021.2019.9010552"},{"key":"ref114","article-title":"Densely dilated spatial pooling convolutional network using benign loss functions for imbalanced volumetric prostate segmentation","author":"liu","year":"2018","journal-title":"arXiv 1801 10517"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2789181"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.6.2.024007"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26047"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13994"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07008-z"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.19.22254"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0096(199605)24:4<169::AID-JCU2>3.0.CO;2-D"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09461197.pdf?arnumber=9461197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T20:11:56Z","timestamp":1643227916000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9461197\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":219,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3090825","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}