{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:49:11Z","timestamp":1771037351861,"version":"3.50.1"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin City","doi-asserted-by":"publisher","award":["20JCQNJC0125"],"award-info":[{"award-number":["20JCQNJC0125"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin City","doi-asserted-by":"publisher","award":["62076077"],"award-info":[{"award-number":["62076077"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin City","doi-asserted-by":"publisher","award":["61871239"],"award-info":[{"award-number":["61871239"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20JCQNJC0125"],"award-info":[{"award-number":["20JCQNJC0125"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076077"],"award-info":[{"award-number":["62076077"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871239"],"award-info":[{"award-number":["61871239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2021,1,29]]},"abstract":"<jats:p>Acute ischemic stroke (AIS) has been a common threat to human health and may lead to severe outcomes without proper and prompt treatment. To precisely diagnose AIS, it is of paramount importance to quantitatively evaluate the AIS lesions. By adopting a convolutional neural network (CNN), many automatic methods for ischemic stroke lesion segmentation on magnetic resonance imaging (MRI) have been proposed. However, most CNN-based methods should be trained on a large amount of fully labeled subjects, and the label annotation is a labor-intensive and time-consuming task. Therefore, in this paper, we propose to use a mixture of many weakly labeled and a few fully labeled subjects to relieve the thirst of fully labeled subjects. In particular, a multifeature map fusion network (MFMF-Network) with two branches is proposed, where hundreds of weakly labeled subjects are used to train the classification branch, and several fully labeled subjects are adopted to tune the segmentation branch. By training on 398 weakly labeled and 5 fully labeled subjects, the proposed method is able to achieve a mean dice coefficient of <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mn>0.699<\/a:mn>\n                        <a:mo>\u00b1<\/a:mo>\n                        <a:mn>0.128<\/a:mn>\n                     <\/a:math>\n                  <\/jats:inline-formula> on a test set with 179 subjects. The lesion-wise and subject-wise metrics are also evaluated, where a lesion-wise F1 score of 0.886 and a subject-wise detection rate of 1 are achieved.<\/jats:p>","DOI":"10.1155\/2021\/3628179","type":"journal-article","created":{"date-parts":[[2021,1,31]],"date-time":"2021-01-31T00:20:08Z","timestamp":1612052408000},"page":"1-13","source":"Crossref","is-referenced-by-count":27,"title":["Deep Learning-Based Acute Ischemic Stroke Lesion Segmentation Method on Multimodal MR Images Using a Few Fully Labeled Subjects"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9018-906X","authenticated-orcid":true,"given":"Bin","family":"Zhao","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1057-8706","authenticated-orcid":true,"given":"Zhiyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohua","family":"Liu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Cao","sequence":"additional","affiliation":[{"name":"Key Laboratory for Cerebral Artery and Neural Degeneration of Tianjin, Department of Medical Imaging, Tianjin Huanhu Hospital, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Jin","sequence":"additional","affiliation":[{"name":"Key Laboratory for Cerebral Artery and Neural Degeneration of Tianjin, Department of Medical Imaging, Tianjin Huanhu Hospital, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0100-3142","authenticated-orcid":true,"given":"Hong","family":"Wu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4963-3883","authenticated-orcid":true,"given":"Shuxue","family":"Ding","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China"},{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin Guangxi 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(06)68770-9"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000000659"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2014.01.003"},{"key":"4","first-page":"1","article-title":"Automatic ischemic stroke lesion segmentation using single MR modality and gravitational histogram optimization based brain segmentation","author":"N. Nabizadeh"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.04.056"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1117\/12.2043494"},{"key":"7","first-page":"271","article-title":"Dense multi-path U-Net for ischemic stroke lesion segmentation in multiple image modalities","author":"J. Dolz","year":"2018"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2872939"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105831"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101791"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.565"},{"key":"13","doi-asserted-by":"crossref","DOI":"10.5244\/C.31.167","article-title":"One-shot learning for semantic segmentation","author":"A. Shaban","year":"2017"},{"issue":"4","key":"14","article-title":"Few-shot semantic segmentation with prototype learning","volume":"3","author":"N. Dong","year":"2018","journal-title":"BMVC"},{"key":"15","article-title":"Few-shot segmentation propagation with guided networks","author":"K. Rakelly","year":"2018"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1109\/cvprw.2009.5206848"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2017.06.016"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2821244"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1186\/s41747-019-0085-6"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1109\/isbi.2018.8363767"},{"key":"21","first-page":"1","article-title":"Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI","volume":"32","author":"L. Liu","year":"2019","journal-title":"Neural Computing and Applications"},{"key":"22","first-page":"2921","article-title":"Learning deep features for discriminative localization","author":"B. Zhou"},{"key":"23","article-title":"Very deep convolutional networks for large-scale image recognition","author":"K. Simonyan","year":"2014"},{"key":"24","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"J. Hu"},{"key":"25","first-page":"1026","article-title":"Delving deep into rectifiers: surpassing human-level performance on ImageNet classification","author":"K. He"},{"key":"26","article-title":"On the variance of the adaptive learning rate and beyond","author":"L. Liu","year":"2019"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2013.00045"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.015"},{"key":"29","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"O. Ronneberger","year":"2015"},{"key":"30","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"J. Long"},{"key":"31","first-page":"2869","article-title":"Fss-1000: a 1000-class dataset for few-shot segmentation","author":"X. Li"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1159\/000103624"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.07.009"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/3628179.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/3628179.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/3628179.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,31]],"date-time":"2021-01-31T00:20:13Z","timestamp":1612052413000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2021\/3628179\/"}},"subtitle":[],"editor":[{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1,29]]},"references-count":33,"alternative-id":["3628179","3628179"],"URL":"https:\/\/doi.org\/10.1155\/2021\/3628179","relation":{},"ISSN":["1748-6718","1748-670X"],"issn-type":[{"value":"1748-6718","type":"electronic"},{"value":"1748-670X","type":"print"}],"subject":[],"published":{"date-parts":[[2021,1,29]]}}}