{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:22:25Z","timestamp":1770888145277,"version":"3.50.1"},"reference-count":90,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"crossref","award":["R01CA272991"],"award-info":[{"award-number":["R01CA272991"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,2,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    . Accurate segmentation of the prostate and dominant intraprostatic lesions (DILs) on magnetic resonance imaging (MRI) is important for prostate cancer radiation therapy treatment planning and targeted dose escalation. However, DIL segmentation remains challenging due to small datasets, institutional bias, and variable imaging protocols. Although the segment anything model (SAM) has shown promise in medical image segmentation, most prior work depends on manual prompts. This study developed a fully automated pipeline that combines localization with a fine-tuned SAM model to segment the prostate and DIL.\n                    <jats:italic>Approach<\/jats:italic>\n                    . Two datasets were utilized: the PI-CAI dataset, comprising 1476 patients, and the cancer imaging archive dataset, comprising 803 patients. The pipeline consisted of two stages: (1) a reinforcement learning-based localization network predicted bounding boxes as segmentation inputs, and (2) a fine-tuned SAM model performed segmentation. Model performance was evaluated using the dice similarity coefficient (DSC), intersection over union (IoU), and detection rates, with additional analysis based on lesion volumes.\n                    <jats:italic>Main results<\/jats:italic>\n                    . The proposed method achieved a mean and median DSC of 0.896 \u00b1 0.070 and 0.915, and an IoU of 0.818 \u00b1 0.100 and 0.844 for prostate segmentation. For DIL segmentation, the mean and median DSC were 0.592 \u00b1 0.192 and 0.636, IoU of 0.446 \u00b1 0.190 and 0.466, with a detection rate of 89%. Four DIL groups were created based on lesion volume percentile. The mean\/median DSC and IoU for each volume group are as follows: 0.5\u20131.0 cubic centimeters (cc): 0.555 \u00b1 0.201\/0.562 &amp; 0.414 \u00b1 0.205\/0.391; 1.0\u20131.8 cc: 0.603 \u00b1 0.185\/0.660 &amp; 0.454 \u00b1 0.180\/0.492; 1.8\u20134.0 cc: 0.588 \u00b1 0.183\/0.627 &amp; 0.439 \u00b1 0.174\/0.456; &gt;4.0 cc: 0.621 \u00b1 0.197\/0.669 &amp; 0.477 \u00b1 0.197\/0.503.\n                    <jats:italic>Significance<\/jats:italic>\n                    . This study presented a fully automated prostate and DIL segmentation framework on MRI by integrating a localization network with fine-tuned SAM. The method achieved robust performance across large multi-institutional datasets and diverse lesion shapes. It shows strong potential for application to clinical workflows for prostate cancer radiation therapy planning and treatment.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae4287","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T22:54:27Z","timestamp":1770332067000},"page":"035020","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Reinforcement learning-guided segment anything model for MRI prostate and dominant intraprostatic lesions auto-segmentation"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3021-9149","authenticated-orcid":true,"given":"Jingchu","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7523-8927","authenticated-orcid":false,"given":"Mingzhe","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3295-328X","authenticated-orcid":true,"given":"Mojtaba","family":"Safari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan","family":"Sanford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1676-7778","authenticated-orcid":true,"given":"Jie","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beth","family":"Ghavidel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Elder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1729-4444","authenticated-orcid":false,"given":"Justin","family":"Roper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7877-1900","authenticated-orcid":true,"given":"Richard L J","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9023-5855","authenticated-orcid":true,"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"pmbae4287bib1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2024.102473","type":"journal-article","article-title":"A review of the segment anything model (sam) for medical image analysis: accomplishments and perspectives","volume":"119","author":"Ali","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"pmbae4287bib2","doi-asserted-by":"publisher","first-page":"W134","DOI":"10.2214\/AJR.19.21365","type":"journal-article","article-title":"Apparent diffusion coefficient (ADC) ratio versus conventional ADC for detecting clinically significant prostate cancer with 3-T MRI","volume":"213","author":"Bajgiran","year":"2019","journal-title":"Am. J. Roentgenol."},{"key":"pmbae4287bib3","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230031","type":"journal-article","article-title":"Semisupervised learning with report-guided pseudo labels for deep learning\u2013based prostate cancer detection using biparametric MRI","volume":"5","author":"Bosma","year":"2023","journal-title":"Radiology"},{"key":"pmbae4287bib4","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2478\/raon-2019-0021","type":"journal-article","article-title":"Multiparametric MRI-local staging of prostate cancer and beyond","volume":"53","author":"Caglic","year":"2019","journal-title":"Radiol. Oncol."},{"key":"pmbae4287bib5","first-page":"1900","type":"conference-proceedings","article-title":"Prostate cancer detection and segmentation in multi-parametric MRI via CNN and conditional random field","author":"Cao","year":"2019"},{"key":"pmbae4287bib6","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1117\/12.3047416","type":"journal-article","article-title":"MRI prostate and tumor segmentation with fine-tuned SAM: a path to enhanced treatment planning","volume":"13410","author":"Chen","year":"2025","journal-title":"Proc. SPIE"},{"key":"pmbae4287bib7","article-title":"Sam on medical images: a comprehensive study on three prompt modes","author":"Cheng","year":"2023","type":"preprint"},{"key":"pmbae4287bib8","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1002\/jmri.27585","type":"journal-article","article-title":"Deep learning whole\u2010gland and zonal prostate segmentation on a public MRI dataset","volume":"54","author":"Cuocolo","year":"2021","journal-title":"J. Magn. Reson. Imaging"},{"key":"pmbae4287bib9","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.adro.2020.01.005","type":"journal-article","article-title":"Segmentation of the prostatic gland and the intraprostatic lesions on multiparametic magnetic resonance imaging using mask region-based convolutional neural networks","volume":"5","author":"Dai","year":"2020","journal-title":"Adv. Radiat. Oncol."},{"key":"pmbae4287bib10","doi-asserted-by":"publisher","first-page":"5216","DOI":"10.1002\/mp.15687","type":"journal-article","article-title":"MRI\u2010based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network","volume":"49","author":"Eidex","year":"2022","journal-title":"Med. Phys."},{"key":"pmbae4287bib11","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1056\/NEJMoa2100852","type":"journal-article","article-title":"MRI-targeted or standard biopsy in prostate cancer screening","volume":"385","author":"Eklund","year":"2021","journal-title":"New Engl. J. Med."},{"key":"pmbae4287bib12","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230138","type":"journal-article","article-title":"Deep learning prostate MRI segmentation accuracy and robustness: a systematic review","volume":"6","author":"Fassia","year":"2024","journal-title":"Radiology"},{"key":"pmbae4287bib13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.110930","type":"journal-article","article-title":"Prostate MRI and image quality: the radiologist\u2019s perspective","volume":"165","author":"F\u00fctterer","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"pmbae4287bib14","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17854","type":"journal-article","article-title":"A segmentation method for oral CBCT image based on segment anything model and semi\u2010supervised teacher\u2010student model","volume":"52","author":"Gan","year":"2025","journal-title":"Med. Phys."},{"key":"pmbae4287bib15","doi-asserted-by":"publisher","first-page":"229","DOI":"10.5152\/dir.2014.13322","type":"journal-article","article-title":"Clinical value of prostate segmentation and volume determination on MRI in benign prostatic hyperplasia","volume":"20","author":"Garvey","year":"2014","journal-title":"Diagn. Interv. Radiol."},{"key":"pmbae4287bib16","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.cmpb.2012.04.006","type":"journal-article","article-title":"A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images","volume":"108","author":"Ghose","year":"2012","journal-title":"Comput. Methods Prog. Biomed."},{"key":"pmbae4287bib17","article-title":"Deep learning in magnetic resonance prostate segmentation: a review and a new perspective","author":"Gillespie","year":"2020","type":"preprint"},{"key":"pmbae4287bib18","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","type":"journal-article","article-title":"Region-based convolutional networks for accurate object detection and segmentation","volume":"38","author":"Girshick","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"pmbae4287bib19","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.adro.2019.09.004","type":"journal-article","article-title":"Dosimetric evaluation of PSMA PET-delineated dominant intraprostatic lesion simultaneous infield boosts","volume":"5","author":"Goodman","year":"2020","journal-title":"Adv. Radiat. Oncol."},{"key":"pmbae4287bib20","article-title":"How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with segment anything model","author":"Gu","year":"2024","type":"preprint"},{"key":"pmbae4287bib21","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1016\/j.bbe.2020.07.011","type":"journal-article","article-title":"Prostate lesion segmentation in MR images using radiomics based deeply supervised U-Net","volume":"40","author":"Hambarde","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"pmbae4287bib22","article-title":"Breastsam: a study of segment anything model for breast tumor detection in ultrasound images","author":"Hu","year":"2023a","type":"preprint"},{"key":"pmbae4287bib23","article-title":"Skinsam: empowering skin cancer segmentation with segment anything model","author":"Hu","year":"2023b","type":"preprint"},{"key":"pmbae4287bib24","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13898","type":"journal-article","article-title":"Reinforcement learning in medical image analysis: concepts, applications, challenges, and future directions","volume":"24","author":"Hu","year":"2023c","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"pmbae4287bib25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103061","type":"journal-article","article-title":"Segment anything model for medical images?","volume":"92","author":"Huang","year":"2024","journal-title":"Med. Image Anal."},{"key":"pmbae4287bib26","doi-asserted-by":"publisher","first-page":"3309","DOI":"10.3390\/biomedicines11123309","type":"journal-article","article-title":"Inter-rater variability of prostate lesion segmentation on multiparametric prostate MRI","volume":"11","author":"Jeganathan","year":"2023","journal-title":"Biomedicines"},{"key":"pmbae4287bib27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104160","type":"journal-article","article-title":"3D PBV-Net: an automated prostate MRI data segmentation method","volume":"128","author":"Jin","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"pmbae4287bib28","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1148\/radiol.2019181749","type":"journal-article","article-title":"VERDICT MRI for prostate cancer: intracellular volume fraction versus apparent diffusion coefficient","volume":"291","author":"Johnston","year":"2019","journal-title":"Radiology"},{"key":"pmbae4287bib29","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.ejro.2018.03.002","type":"journal-article","article-title":"Correlation of apparent diffusion coefficient ratio on 3.0 T MRI with prostate cancer Gleason score","volume":"5","author":"Jyoti","year":"2018","journal-title":"Eur. J. Radiol. Open"},{"key":"pmbae4287bib30","doi-asserted-by":"publisher","first-page":"97878","DOI":"10.1109\/ACCESS.2021.3090825","type":"journal-article","article-title":"Recent automatic segmentation algorithms of MRI prostate regions: a review","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"pmbae4287bib31","first-page":"4015","type":"conference-proceedings","article-title":"Segment anything","author":"Kirillov","year":"2023"},{"key":"pmbae4287bib32","first-page":"118","type":"conference-proceedings","article-title":"Asps: augmented segment anything model for polyp segmentation","author":"Li","year":"2024a"},{"key":"pmbae4287bib33","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1117\/12.3006809","type":"journal-article","article-title":"Polyp-sam: transfer sam for polyp segmentation","volume":"12927","author":"Li","year":"2024"},{"key":"pmbae4287bib34","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1002\/mp.15895","type":"journal-article","article-title":"Automatic segmentation of prostate MRI based on 3D pyramid pooling Unet","volume":"50","author":"Li","year":"2023a","journal-title":"Med. Phys."},{"key":"pmbae4287bib35","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107374","type":"journal-article","article-title":"Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI","volume":"165","author":"Li","year":"2023b","journal-title":"Comput. Biol. Med."},{"key":"pmbae4287bib36","doi-asserted-by":"publisher","first-page":"4806","DOI":"10.1007\/s00330-020-06786-w","type":"journal-article","article-title":"Manual prostate cancer segmentation in MRI: interreader agreement and volumetric correlation with transperineal template core needle biopsy","volume":"30","author":"Liechti","year":"2020","journal-title":"Eur. Radiol."},{"key":"pmbae4287bib37","first-page":"2980","type":"conference-proceedings","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"pmbae4287bib38","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.801876","type":"journal-article","article-title":"Deep learning enables prostate MRI segmentation: a large cohort evaluation with inter-rater variability analysis","volume":"11","author":"Liu","year":"2021","journal-title":"Front. Oncol."},{"key":"pmbae4287bib39","first-page":"3431","type":"conference-proceedings","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015"},{"key":"pmbae4287bib40","first-page":"678","type":"conference-proceedings","article-title":"Superpixel-guided segment anything model for liver tumor segmentation with couinaud Segment prompt","author":"Lyu","year":"2024"},{"key":"pmbae4287bib41","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","type":"journal-article","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"pmbae4287bib42","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac3c13","type":"journal-article","article-title":"Prostate and dominant intraprostatic lesion segmentation on PET\/CT using cascaded regional-net","volume":"66","author":"Matkovic","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"pmbae4287bib43","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102918","type":"journal-article","article-title":"Segment anything model for medical image analysis: an experimental study","volume":"89","author":"Mazurowski","year":"2023","journal-title":"Med. Image Anal."},{"key":"pmbae4287bib44","first-page":"565","type":"conference-proceedings","article-title":"V-net: fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"pmbae4287bib45","doi-asserted-by":"publisher","first-page":"412","DOI":"10.3390\/bioengineering10040412","type":"journal-article","article-title":"U-Net architecture for prostate segmentation: the impact of loss function on system performance","volume":"10","author":"Montazerolghaem","year":"2023","journal-title":"Bioengineering"},{"key":"pmbae4287bib46","author":"Natarajan","year":"2020","type":"book"},{"key":"pmbae4287bib47","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s00345-020-03182-3","type":"journal-article","article-title":"Review article: MRI-targeted biopsies for prostate cancer diagnosis and management","volume":"39","author":"Noureldin","year":"2021","journal-title":"World J. Urol."},{"key":"pmbae4287bib48","article-title":"An introduction to convolutional neural networks","author":"O\u2019shea","year":"2015","type":"preprint"},{"key":"pmbae4287bib49","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102927","type":"journal-article","article-title":"USE-evaluator: performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging","volume":"90","author":"Ostmeier","year":"2023","journal-title":"Med. Image Anal."},{"key":"pmbae4287bib50","doi-asserted-by":"publisher","first-page":"415","DOI":"10.21873\/invivo.11075","type":"journal-article","article-title":"Multiparametric MRI apparent diffusion coefficient (ADC) accuracy in diagnosing clinically significant prostate cancer","volume":"31","author":"Pepe","year":"2017","journal-title":"In Vivo"},{"key":"pmbae4287bib51","article-title":"Sam 2: segment anything in images and videos","author":"Ravi","year":"2024","type":"preprint"},{"key":"pmbae4287bib52","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.3390\/cancers15051467","type":"journal-article","article-title":"A comparative study of automated deep learning segmentation models for prostate MRI","volume":"15","author":"Rodrigues","year":"2023","journal-title":"Cancers"},{"key":"pmbae4287bib53","first-page":"234","type":"conference-proceedings","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"pmbae4287bib54","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","type":"journal-article","article-title":"Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study","volume":"25","author":"Saha","year":"2024","journal-title":"Lancet Oncol."},{"key":"pmbae4287bib55","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1016\/j.ijrobp.2020.06.076","type":"journal-article","article-title":"Machine segmentation of pelvic anatomy in MRI-assisted radiosurgery (MARS) for prostate cancer brachytherapy","volume":"108","author":"Sanders","year":"2020","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbae4287bib56","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1016\/j.ijrobp.2021.06.034","type":"journal-article","article-title":"Effects of exercise during radiation therapy on physical function and treatment-related side effects in men with prostate cancer: a systematic review and meta-analysis","volume":"111","author":"Schumacher","year":"2021","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbae4287bib57","doi-asserted-by":"publisher","first-page":"448","DOI":"10.5152\/tud.2021.21274","type":"journal-article","article-title":"MRI apparent diffusion coefficient (ADC): a biomarker for prostate cancer after radiation therapy","volume":"47","author":"Scialpi","year":"2021","journal-title":"Turk. J. Urol."},{"key":"pmbae4287bib58","article-title":"Interactive 3d medical image segmentation with sam 2","author":"Shen","year":"2024","type":"preprint"},{"key":"pmbae4287bib59","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3322\/caac.21871","type":"journal-article","article-title":"Cancer statistics, 2025","volume":"75","author":"Siegel","year":"2025","journal-title":"Ca"},{"key":"pmbae4287bib60","doi-asserted-by":"publisher","first-page":"4854","DOI":"10.1002\/mp.16320","type":"journal-article","article-title":"Deep learning\u2010based dominant index lesion segmentation for MR\u2010guided radiation therapy of prostate cancer","volume":"50","author":"Simeth","year":"2023","journal-title":"Med. Phys."},{"key":"pmbae4287bib61","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1117\/12.2580448","type":"journal-article","article-title":"ProGNet: prostate gland segmentation on MRI with deep learning","volume":"11596","author":"Soerensen","year":"2021","journal-title":"Proc. SPIE"},{"key":"pmbae4287bib62","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.mri.2023.01.015","type":"journal-article","article-title":"Prostate lesion segmentation based on a 3D end-to-end convolution neural network with deep multi-scale attention","volume":"99","author":"Song","year":"2023","journal-title":"Magn. Reson. Imaging"},{"key":"pmbae4287bib63","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5497120","type":"journal-article","article-title":"Light weighted healthcare CNN model to detect prostate cancer on multiparametric MRI","volume":"2022","author":"Soni","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"pmbae4287bib64","first-page":"240","type":"conference-proceedings","article-title":"Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations","author":"Sudre","year":"2017"},{"key":"pmbae4287bib65","author":"Sutton","year":"1998","type":"book"},{"key":"pmbae4287bib66","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/s10916-024-02105-8","type":"journal-article","article-title":"Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review","volume":"48","author":"Takahashi","year":"2024","journal-title":"J. Med. Syst."},{"key":"pmbae4287bib67","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102381","type":"journal-article","article-title":"SeqSeg: a sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance","volume":"78","author":"Tao","year":"2022","journal-title":"Med. Image Anal."},{"key":"pmbae4287bib68","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20210563","type":"journal-article","article-title":"Deep learning-based artificial intelligence applications in prostate MRI: brief summary","volume":"95","author":"Turkbey","year":"2022","journal-title":"Br. J. Radiol."},{"key":"pmbae4287bib69","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1016\/j.brachy.2016.09.004","type":"journal-article","article-title":"Image-guided high-dose-rate brachytherapy boost to the dominant intraprostatic lesion using multiparametric magnetic resonance imaging including spectroscopy: results of a prospective study","volume":"15","author":"Vigneault","year":"2016","journal-title":"Brachytherapy"},{"key":"pmbae4287bib70","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.ijrobp.2020.09.054","type":"journal-article","article-title":"Prostate stereotactic body radiation therapy: an overview of toxicity and dose response","volume":"110","author":"Wang","year":"2021a","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"pmbae4287bib71","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1117\/12.2581061","type":"journal-article","article-title":"MRI-based prostate and dominant lesion segmentation using deep neural network","volume":"11597","author":"Wang","year":"2021b","journal-title":"Proc. SPIE"},{"key":"pmbae4287bib72","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1274803","type":"journal-article","article-title":"Lesion segmentation on 18F-fluciclovine PET\/CT images using deep learning","volume":"13","author":"Wang","year":"2023a","journal-title":"Front. Oncol."},{"key":"pmbae4287bib73","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20190089","type":"journal-article","article-title":"Multiparametric MRI-guided dose boost to dominant intraprostatic lesions in CT-based high-dose-rate prostate brachytherapy","volume":"92","author":"Wang","year":"2019","journal-title":"Br. J. Radiol."},{"key":"pmbae4287bib74","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104610","type":"journal-article","article-title":"A two-stage CNN method for MRI image segmentation of prostate with lesion","volume":"82","author":"Wang","year":"2023b","journal-title":"Biomed. Signal Process. Control"},{"key":"pmbae4287bib75","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.eururo.2015.08.052","type":"journal-article","article-title":"PI-RADS prostate imaging\u2013reporting and data system: 2015, version 2","volume":"69","author":"Weinreb","year":"2016","journal-title":"Europ. Urol."},{"key":"pmbae4287bib76","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1186\/s13244-022-01340-2","type":"journal-article","article-title":"Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature","volume":"13","author":"Wu","year":"2022","journal-title":"Insights Imaging"},{"key":"pmbae4287bib77","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103547","type":"journal-article","article-title":"Medical sam adapter: adapting segment anything model for medical image segmentation","volume":"102","author":"Wu","year":"2025","journal-title":"Med. Image Anal."},{"key":"pmbae4287bib78","article-title":"Sam2-unet: segment anything 2 makes strong encoder for natural and medical image segmentation","author":"Xiong","year":"2024","type":"preprint"},{"key":"pmbae4287bib79","doi-asserted-by":"publisher","first-page":"22TR02","DOI":"10.1088\/1361-6560\/ac9cb3","type":"journal-article","article-title":"Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey","volume":"67","author":"Xu","year":"2022","journal-title":"Phys. Med. Biol."},{"key":"pmbae4287bib80","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103095","type":"journal-article","article-title":"Separated collaborative learning for semi-supervised prostate segmentation with multi-site heterogeneous unlabeled MRI data","volume":"93","author":"Xu","year":"2024","journal-title":"Med. Image Anal."},{"key":"pmbae4287bib81","doi-asserted-by":"publisher","DOI":"10.2196\/72109","type":"journal-article","article-title":"Using segment anything model 2 for zero-shot 3D segmentation of abdominal organs in computed tomography scans to adapt video tracking capabilities for 3D medical imaging: algorithm development and validation","volume":"4","author":"Yamagishi","year":"2025","journal-title":"JMIR AI"},{"key":"pmbae4287bib82","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106211","type":"journal-article","article-title":"PSP net-based automatic segmentation network model for prostate magnetic resonance imaging","volume":"207","author":"Yan","year":"2021","journal-title":"Comput. Methods Prog. Biomed."},{"key":"pmbae4287bib83","first-page":"623","type":"conference-proceedings","article-title":"The domain shift problem of medical image segmentation and vendor-adaptation by Unet-GAN","author":"Yan","year":"2019"},{"key":"pmbae4287bib84","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1007\/s10278-024-00981-7","type":"journal-article","article-title":"From cnn to transformer: a review of medical image segmentation models","volume":"37","author":"Yao","year":"2024","journal-title":"J. Imaging Inform. Med."},{"key":"pmbae4287bib85","article-title":"Customized segment anything model for medical image segmentation","author":"Zhang","year":"2023","type":"preprint"},{"key":"pmbae4287bib86","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","type":"journal-article","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"pmbae4287bib87","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113701","type":"journal-article","article-title":"WeakPolyp-SAM: segment anything model-driven weakly-supervised polyp segmentation","volume":"322","author":"Zhao","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"pmbae4287bib88","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102193","type":"journal-article","article-title":"Deep reinforcement learning in medical imaging: a literature review","volume":"73","author":"Zhou","year":"2021","journal-title":"Med. Image Anal."},{"key":"pmbae4287bib89","first-page":"3","type":"conference-proceedings","article-title":"Unet++: a nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"pmbae4287bib90","article-title":"Medical sam 2: segment medical images as video via segment anything model 2","author":"Zhu","year":"2024","type":"preprint"}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae4287","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae4287\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae4287","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae4287\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae4287\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae4287\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae4287\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae4287\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:41:11Z","timestamp":1770885671000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ae4287"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,12]]},"references-count":90,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2,12]]},"published-print":{"date-parts":[[2026,2,14]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/ae4287","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,12]]},"assertion":[{"value":"Reinforcement learning-guided segment anything model for MRI prostate and dominant intraprostatic lesions auto-segmentation","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-11-07","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-02-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-02-12","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}