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Unsupervised model for structure segmentation applied to brain computed tomography
This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for...
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| Published in: | PloS one 2024-06, Vol.19 (6), p.e0304017 |
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| container_issue | 6 |
| container_start_page | e0304017 |
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| creator | Dos Santos, Paulo Victor Scoczynski Ribeiro Martins, Marcella Amorim Nogueira, Solange Gonçalves, Cristhiane Maffei Loureiro, Rafael Pacheco Calixto, Wesley |
| description | This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for real-world datasets, this approach applies a spatial continuity scoring function tailored to the desired number of structures. The primary objective is to assist medical experts in diagnosis by identifying regions with specific abnormalities. Results indicate a simplified and accessible solution, reducing computational effort, training time, and financial costs. Moreover, the method presents potential for expediting the interpretation of abnormal scans, thereby impacting clinical practice. This proposed approach might serve as a practical tool for segmenting brain computed tomography scans, and make a significant contribution to the analysis of medical images in both research and clinical settings. |
| doi_str_mv | 10.1371/journal.pone.0304017 |
| format | article |
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The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for real-world datasets, this approach applies a spatial continuity scoring function tailored to the desired number of structures. The primary objective is to assist medical experts in diagnosis by identifying regions with specific abnormalities. Results indicate a simplified and accessible solution, reducing computational effort, training time, and financial costs. Moreover, the method presents potential for expediting the interpretation of abnormal scans, thereby impacting clinical practice. This proposed approach might serve as a practical tool for segmenting brain computed tomography scans, and make a significant contribution to the analysis of medical images in both research and clinical settings.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0304017</identifier><identifier>PMID: 38870119</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Abnormalities ; Algorithms ; Artificial intelligence ; Biology and Life Sciences ; Brain ; Brain - diagnostic imaging ; Computational neuroscience ; Computed tomography ; Computer and Information Sciences ; Continuity (mathematics) ; CT imaging ; Datasets ; Feature extraction ; Humans ; Image Processing, Computer-Assisted - methods ; Medical imaging ; Medicine and Health Sciences ; Neural networks ; Physical Sciences ; Research and Analysis Methods ; Segmentation ; Tomography, X-Ray Computed - methods ; Unsupervised Machine Learning</subject><ispartof>PloS one, 2024-06, Vol.19 (6), p.e0304017</ispartof><rights>Copyright: © 2024 dos Santos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 dos Santos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 dos Santos et al 2024 dos Santos et al</rights><rights>2024 dos Santos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c506t-3dc5c84cd7d87a0b3887338af7afc478d36ade414d1feb734586702af4cb73723</cites><orcidid>0000-0002-5716-4968 ; 0000-0002-1635-2225 ; 0000-0002-3894-2971 ; 0000-0002-8267-7562</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3069265670/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3069265670?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,38516,43895,44590,53791,53793,74412,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38870119$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Masad, Ihssan S.</contributor><creatorcontrib>Dos Santos, Paulo Victor</creatorcontrib><creatorcontrib>Scoczynski Ribeiro Martins, Marcella</creatorcontrib><creatorcontrib>Amorim Nogueira, Solange</creatorcontrib><creatorcontrib>Gonçalves, Cristhiane</creatorcontrib><creatorcontrib>Maffei Loureiro, Rafael</creatorcontrib><creatorcontrib>Pacheco Calixto, Wesley</creatorcontrib><title>Unsupervised model for structure segmentation applied to brain computed tomography</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>This article presents an unsupervised method for segmenting brain computed tomography scans. The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for real-world datasets, this approach applies a spatial continuity scoring function tailored to the desired number of structures. The primary objective is to assist medical experts in diagnosis by identifying regions with specific abnormalities. Results indicate a simplified and accessible solution, reducing computational effort, training time, and financial costs. Moreover, the method presents potential for expediting the interpretation of abnormal scans, thereby impacting clinical practice. 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The proposed methodology involves image feature extraction and application of similarity and continuity constraints to generate segmentation maps of the anatomical head structures. Specifically designed for real-world datasets, this approach applies a spatial continuity scoring function tailored to the desired number of structures. The primary objective is to assist medical experts in diagnosis by identifying regions with specific abnormalities. Results indicate a simplified and accessible solution, reducing computational effort, training time, and financial costs. Moreover, the method presents potential for expediting the interpretation of abnormal scans, thereby impacting clinical practice. 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| source | Publicly Available Content Database; PubMed Central; Coronavirus Research Database |
| subjects | Abnormalities Algorithms Artificial intelligence Biology and Life Sciences Brain Brain - diagnostic imaging Computational neuroscience Computed tomography Computer and Information Sciences Continuity (mathematics) CT imaging Datasets Feature extraction Humans Image Processing, Computer-Assisted - methods Medical imaging Medicine and Health Sciences Neural networks Physical Sciences Research and Analysis Methods Segmentation Tomography, X-Ray Computed - methods Unsupervised Machine Learning |
| title | Unsupervised model for structure segmentation applied to brain computed tomography |
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