{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T14:50:49Z","timestamp":1766415049569,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Cassa di Risparmio di Cuneo (CRC, Italy)","award":["CRC_2016-0707"],"award-info":[{"award-number":["CRC_2016-0707"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Magnetic resonance imaging (MRI) has a growing role in the clinical workup of prostate cancer. However, manual three-dimensional (3D) segmentation of the prostate is a laborious and time-consuming task. In this scenario, the use of automated algorithms for prostate segmentation allows us to bypass the huge workload of physicians. In this work, we propose a fully automated hybrid approach for prostate gland segmentation in MR images using an initial segmentation of prostate volumes using a custom-made 3D deep network (VNet-T2), followed by refinement using an Active Shape Model (ASM). While the deep network focuses on three-dimensional spatial coherence of the shape, the ASM relies on local image information and this joint effort allows for improved segmentation of the organ contours. Our method is developed and tested on a dataset composed of T2-weighted (T2w) MRI prostatic volumes of 60 male patients. In the test set, the proposed method shows excellent segmentation performance, achieving a mean dice score and Hausdorff distance of 0.851 and 7.55 mm, respectively. In the future, this algorithm could serve as an enabling technology for the development of computer-aided systems for prostate cancer characterization in MR imaging.<\/jats:p>","DOI":"10.3390\/jimaging8050133","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T10:19:46Z","timestamp":1652264386000},"page":"133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7225-7401","authenticated-orcid":false,"given":"Massimo","family":"Salvi","sequence":"first","affiliation":[{"name":"Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3790-7645","authenticated-orcid":false,"given":"Bruno","family":"De Santi","sequence":"additional","affiliation":[{"name":"Multi-Modality Medical Imaging (M3I), Technical Medical Centre, University of Twente, PB217, 7500 AE Enschede, The Netherlands"}]},{"given":"Bianca","family":"Pop","sequence":"additional","affiliation":[{"name":"Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy"}]},{"given":"Martino","family":"Bosco","sequence":"additional","affiliation":[{"name":"Department of Pathology, Ospedale Michele e Pietro Ferrero, 12060 Verduno, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5052-8231","authenticated-orcid":false,"given":"Valentina","family":"Giannini","sequence":"additional","affiliation":[{"name":"Department of Surgical Sciences, University of Turin, 10126 Turin, Italy"},{"name":"Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy"}]},{"given":"Daniele","family":"Regge","sequence":"additional","affiliation":[{"name":"Department of Surgical Sciences, University of Turin, 10126 Turin, Italy"},{"name":"Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1150-2244","authenticated-orcid":false,"given":"Filippo","family":"Molinari","sequence":"additional","affiliation":[{"name":"Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7302-6135","authenticated-orcid":false,"given":"Kristen M.","family":"Meiburger","sequence":"additional","affiliation":[{"name":"Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.14740\/wjon1191","article-title":"Epidemiology of Prostate Cancer","volume":"10","author":"Rawla","year":"2019","journal-title":"World J. 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