{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T08:46:47Z","timestamp":1746089207354,"version":"3.37.3"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T00:00:00Z","timestamp":1631577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Department of Science and Technology Ho Chi Minh city","award":["43\/2019\/HD-QPTKHCN"],"award-info":[{"award-number":["43\/2019\/HD-QPTKHCN"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>According to the World Alzheimer Report 2015, 46 million people are living with dementia in the world. The diagnosis of diseases helps doctors treating patients better. One of the signs of diseases is related to white matter, grey matter and cerebrospinal fluid. Therefore, the automatic segmentation of three tissues in brain imaging especially from magnetic resonance imaging (MRI) plays an important role in medical analysis. In this research, we proposed an effective approach to segment automatically these tissues in three-dimensional (3D) brain MRI. First, a deep learning model is used to segment the sure and unsure regions. In the unsure region, another deep learning model is used to classify each pixel. In the experiments, an adaptive U-net model is used to segment the sure and unsure regions, and the Local Convolutional Neural Network (CNN) model with multiple inputs is used to classify each pixel only in the unsure region. Our method was evaluated with a real image database, Internet Brain Segmentation Repository database, with 18 persons (IBSR 18) (https:\/\/www.nitrc.org\/projects\/ibsr) and compared with state of art methods being the results very promising.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab127","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T11:12:49Z","timestamp":1628766769000},"page":"3081-3090","source":"Crossref","is-referenced-by-count":5,"title":["White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain Magnetic Resonance Imaging Using Adaptive U-Net and Local Convolutional Neural Network"],"prefix":"10.1093","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4847-4366","authenticated-orcid":false,"given":"Pham The","family":"Bao","sequence":"first","affiliation":[{"name":"Department of Computer Science , Sai Gon University, Vietnam 273 An Duong Vuong street, Ward 3, District 5, Ho Chi Minh City 700000, Vietnam"}]},{"given":"Tran Anh","family":"Tuan","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science , University of Science, Vietnam National University, Ho Chi Minh City, Vietnam 227 Nguyen Van Cu street, Ward 3, District 5, Ho Chi Minh City 700000, Vietnam"}]},{"given":"Tran Anh","family":"Tuan","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science , University of Science, Vietnam National University, Ho Chi Minh City, Vietnam 227 Nguyen Van Cu street, Ward 3, District 5, Ho Chi Minh City 700000, Vietnam"}]},{"given":"Le Nhi Lam","family":"Thuy","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Sai Gon University, Vietnam 273 An Duong Vuong street, Ward 3, District 5, Ho Chi Minh City 700000, Vietnam"}]},{"given":"Jin Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering , Chonnam National University, South Korea. 77 Yongbong-ro, Yongbong-dong, Buk-gu, Gwangju 61186, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7603-6526","authenticated-orcid":false,"given":"Jo\u00e3o Manuel R S","family":"Tavares","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial , Departamento de Engenharia Mec\u00e2nica Faculdade de Engenharia, , Portugal Rua Dr. Roberto Frias, Porto 4200-465, Portugal"},{"name":"Universidade do Porto , Departamento de Engenharia Mec\u00e2nica Faculdade de Engenharia, , Portugal Rua Dr. Roberto Frias, Porto 4200-465, Portugal"}]}],"member":"286","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"2023010312520634800_ref1","first-page":"283","volume-title":"Imaging of the Pelvis, Musculoskeletal System, and Special Applications to CAD","author":"Saba","year":"2019","edition":"1st"},{"key":"2023010312520634800_ref2","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1109\/TASC.2005.849580","article-title":"Superconducting systems for MRI-present solutions and new trends","volume":"15","author":"Lvovsky","year":"2005","journal-title":"IEEE Trans. Appl. Supercond."},{"key":"2023010312520634800_ref3","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","article-title":"Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain","volume":"33","author":"Fischl","year":"2002","journal-title":"Neuron"},{"key":"2023010312520634800_ref4","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1212\/WNL.55.11.1626","article-title":"Hippocampal and cortical atrophy predict dementia in subcortical ischemic vascular disease","volume":"55","author":"Fein","year":"2000","journal-title":"Neurology"},{"key":"2023010312520634800_ref5","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1136\/jnnp.67.1.66","article-title":"White matter lesions on magnetic resonance imaging in dementia with Lewy bodies, Alzheimer's disease, vascular dementia, and normal aging","volume":"67","author":"Barber","year":"1999","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"2023010312520634800_ref6","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1037\/0894-4105.14.2.224","article-title":"The cognitive correlates of white matter abnormalities in normal aging: a quantitative review","volume":"14","author":"Gunning-Dixon","year":"2000","journal-title":"Neuropsychology"},{"key":"2023010312520634800_ref7","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1001\/archneur.61.10.1531","article-title":"Cerebral white matter lesions and the risk of dementia","volume":"61","author":"Prins","year":"2004","journal-title":"Arch. Neurol."},{"key":"2023010312520634800_ref8","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/RBME.2017.2715350","article-title":"State-of-the-art methods for brain tissue segmentation: A review","volume":"10","author":"Dora","year":"2017","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"2023010312520634800_ref9","doi-asserted-by":"crossref","first-page":"e0125477","DOI":"10.1371\/journal.pone.0125477","article-title":"Fully automated whole-head segmentation with improved smoothness and continuity, with theory reviewed","volume":"10","author":"Yu","year":"2015","journal-title":"PLoS ONE"},{"key":"2023010312520634800_ref10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/s41598-017-00239-z","article-title":"White matter and Gray matter segmentation in 4D computed tomography","volume":"7","author":"Manniesing","year":"2017","journal-title":"Sci. Rep."},{"key":"2023010312520634800_ref11","first-page":"1","article-title":"Techniques of medical image processing and analysis accelerated by high-performance computing: A systematic literature review","volume":"16","author":"Gulo","year":"2017","journal-title":"J. Real-Time Image Proc."},{"key":"2023010312520634800_ref12","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/B978-012372560-8\/50006-1","volume-title":"Statistical Parametric Mapping: The Analysis of Functional Brain Images","year":"2007"},{"key":"2023010312520634800_ref13","doi-asserted-by":"crossref","first-page":"S208","DOI":"10.1016\/j.neuroimage.2004.07.051","article-title":"Advances in functional and structural MR image analysis and implementation as FSL","volume":"23","author":"Smith","year":"2004","journal-title":"NeuroImage"},{"key":"2023010312520634800_ref14","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1038\/s41598-018-24304-3","article-title":"Spinal cord gray matter segmentation using deep dilated convolutions","volume":"8","author":"Perone","year":"2018","journal-title":"Sci. Rep."},{"volume-title":"IEEE Winter Conference on Applications of Computer Vision (WACV), USA","year":"2017","author":"Nguyen","key":"2023010312520634800_ref15"},{"key":"2023010312520634800_ref16","first-page":"2278","volume-title":"Proceedings of the IEEE","author":"Lecun","year":"1998"},{"key":"2023010312520634800_ref17","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2023010312520634800_ref18","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2023010312520634800_ref19","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","article-title":"Deep learning for brain MRI segmentation: State of the art and future directions","volume":"30","author":"Akkus","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"2023010312520634800_ref20","first-page":"234","volume-title":"Medical Image Computing and Computer-Assisted Intervention (MICCAI)","author":"Ronneberger","year":"2015"},{"key":"2023010312520634800_ref21","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/0734-189X(85)90016-7","article-title":"Topological structural analysis of digitized binary images by border following","volume":"30","author":"Suzuki","year":"1985","journal-title":"Computer Vision, Graphics, and Image Processing"},{"key":"2023010312520634800_ref22","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","article-title":"A review of semantic segmentation using deep neural networks","volume":"7","author":"Guo","year":"2018","journal-title":"Int. J. Multimedia Information Retrieval"},{"key":"2023010312520634800_ref23","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"2023010312520634800_ref24","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"volume-title":"Child and Adolescent NeuroDevelopment Initiative","year":"2007","author":"Frazier","key":"2023010312520634800_ref25"},{"key":"2023010312520634800_ref26","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/jmri.24517","article-title":"Comparison of 10 brain tissue segmentation methods using revisited ibsr annotations","volume":"41","author":"Valverde","year":"2015","journal-title":"J. Magn. Reson. Imaging"},{"key":"2023010312520634800_ref27","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"volume-title":"Keras","year":"2015","author":"Chollet","key":"2023010312520634800_ref28"},{"volume-title":"Adam: a Method for Stochastic Optimization","year":"2015","author":"Kingma","key":"2023010312520634800_ref29"},{"volume-title":"Pattern Recognition and Machine Learning","year":"2006","author":"Bishop","key":"2023010312520634800_ref30"},{"key":"2023010312520634800_ref31","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/978-1-4614-7138-7","volume-title":"An Introduction to Statistical Learning with Applications in R","author":"James","year":"2013"},{"first-page":"188","volume-title":"VipIMAGE 2019-Proceedings of the VII ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing","year":"2019","key":"2023010312520634800_ref32"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/65\/12\/3081\/48480795\/bxab127.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/65\/12\/3081\/48480795\/bxab127.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T12:53:47Z","timestamp":1672750427000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/65\/12\/3081\/6370299"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,14]]},"references-count":32,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,9,14]]},"published-print":{"date-parts":[[2022,12,30]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxab127","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"type":"print","value":"0010-4620"},{"type":"electronic","value":"1460-2067"}],"subject":[],"published-other":{"date-parts":[[2022,12]]},"published":{"date-parts":[[2021,9,14]]}}}