{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:06:47Z","timestamp":1760123207571,"version":"3.28.0"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,11]]},"DOI":"10.1109\/embc48229.2022.9871715","type":"proceedings-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T16:02:05Z","timestamp":1662652925000},"page":"2140-2143","source":"Crossref","is-referenced-by-count":2,"title":["A Fast and Memory-Efficient Brain MRI Segmentation Framework for Clinical Applications"],"prefix":"10.1109","author":[{"given":"Ashkan","family":"Nejad","sequence":"first","affiliation":[{"name":"Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen,Machine Learning Lab,The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeed","family":"Masoudnia","sequence":"additional","affiliation":[{"name":"Tehran University of Medical Sciences (TUMS),Medical Physics and Biomedical Engineering Department and the research center for molecular and cellular imaging (RCMCI),Tehran,Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad-Reza","family":"Nazem-Zadeh","sequence":"additional","affiliation":[{"name":"Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen,Machine Learning Lab,The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.102000"},{"key":"ref11","first-page":"424","article-title":"3d u-net: learning dense volumetric segmentation from sparse annotation","author":"\u00e7i\u00e7ek","year":"2016","journal-title":"MICCAI"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.2.024003"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.06.005"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2013.00027"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci10120974"},{"key":"ref16","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"zhou","year":"2018","journal-title":"Deep learning for medical image analysis"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117012"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.11.042"},{"key":"ref19","article-title":"nnu-net: Self-adapting framework for u-net-based medical image segmentation","author":"isensee","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"116819","DOI":"10.1016\/j.neuroimage.2020.116819","article-title":"Automatic cerebellum anatomical parcellation using u-net with locally constrained optimization","volume":"218","author":"shuo","year":"2020","journal-title":"NeuroImage"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117026"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnu-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"isensee","year":"2021","journal-title":"Nature Methods"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.116947"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/NSSMIC.1993.373602"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/S0262-8856(00)00052-4"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.03.041"},{"key":"ref1","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"MICCAI"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2046908"},{"key":"ref20","article-title":"Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and alzheimer disease","author":"lamontagne","year":"2019","journal-title":"medRxiv"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2014.49","article-title":"An open science resource for establishing reliability and reproducibility in functional connectomics","volume":"1","author":"zuo","year":"2014","journal-title":"Scientific Data"}],"event":{"name":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","start":{"date-parts":[[2022,7,11]]},"location":"Glasgow, Scotland, United Kingdom","end":{"date-parts":[[2022,7,15]]}},"container-title":["2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9870821\/9870822\/09871715.pdf?arnumber=9871715","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T17:01:13Z","timestamp":1664211673000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9871715\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,11]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/embc48229.2022.9871715","relation":{},"subject":[],"published":{"date-parts":[[2022,7,11]]}}}