{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T20:02:54Z","timestamp":1776542574519,"version":"3.51.2"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Because of the large variabilities in brain tumors, automating segmentation remains a difficult task. We propose an automated method to segment brain tumors by integrating the deep capsule network (CapsNet) and the latent-dynamic condition random field (LDCRF). The method consists of three main processes to segment the brain tumor\u2014pre-processing, segmentation, and post-processing. In pre-processing, the N4ITK process involves correcting each MR image\u2019s bias field before normalizing the intensity. After that, image patches are used to train CapsNet during the segmentation process. Then, with the CapsNet parameters determined, we employ image slices from an axial view to learn the LDCRF-CapsNet. Finally, we use a simple thresholding method to correct the labels of some pixels and remove small 3D-connected regions from the segmentation outcomes. On the BRATS 2015 and BRATS 2021 datasets, we trained and evaluated our method and discovered that it outperforms and can compete with state-of-the-art methods in comparable conditions.<\/jats:p>","DOI":"10.3390\/jimaging8070190","type":"journal-article","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T11:37:08Z","timestamp":1657280228000},"page":"190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8750-1366","authenticated-orcid":false,"given":"Mahmoud","family":"Elmezain","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Science, Tanta University, Tanta 31527, Egypt"},{"name":"College of Computer Science and Engineering, Taibah University, Yanbu 966144, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5415-2972","authenticated-orcid":false,"given":"Amena","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, KafrElSheikh University, Kafr El-Sheikh 32626, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diana T.","family":"Mosa","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information, KafrElSheikh University, Kafr El-Sheikh 32626, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8623-6847","authenticated-orcid":false,"given":"Wael","family":"Said","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University, Yanbu 966144, Saudi Arabia"},{"name":"Computer Science Department, Faculty of Computers and Informatics, Zagazig University, Zagazig 44511, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","article-title":"The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary","volume":"131","author":"Perry","year":"2016","journal-title":"Acta Neuropathol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TST.2014.6961028","article-title":"A Survey of MRI-Based Brain Tumor Segmentation Methods","volume":"19","author":"Liu","year":"2014","journal-title":"Tsinghua Sci. 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