Supervised segmentation with domain adaptation for small sampled orbital CT images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Suh Sungho | - |
dc.contributor.author | Cheon Sojeong | - |
dc.contributor.author | Choi Wonseo | - |
dc.contributor.author | Chung Yeon Woong | - |
dc.contributor.author | Cho Won-Kyung | - |
dc.contributor.author | Paik Ji-Sun | - |
dc.contributor.author | Kim Sung Eun | - |
dc.contributor.author | Chang Dong-Jin | - |
dc.contributor.author | Lee Yong Oh | - |
dc.date.accessioned | 2024-04-16T02:32:26Z | - |
dc.date.available | 2024-04-16T02:32:26Z | - |
dc.date.issued | 2022-04-01 | - |
dc.identifier.issn | 2288-4300 | - |
dc.identifier.issn | 2288-5048 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32967 | - |
dc.description.abstract | Deep neural networks have been widely used for medical image analysis. However, the lack of access to a large-scale annotated dataset poses a great challenge, especially in the case of rare diseases or new domains for the research society. Transfer of pre-trained features from the relatively large dataset is a considerable solution. In this paper, we have explored supervised segmentation using domain adaptation for optic nerve and orbital tumour, when only small sampled CT images are given. Even the lung image database consortium image collection (LIDC-IDRI) is a cross-domain to orbital CT, but the proposed domain adaptation method improved the performance of attention U-Net for the segmentation in public optic nerve dataset and our clinical orbital tumour dataset by 3.7% and 13.7% in the Dice score, respectively. The code and dataset are available at https://github.com/cmcbigdata. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 한국CDE학회 | - |
dc.title | Supervised segmentation with domain adaptation for small sampled orbital CT images | - |
dc.title.alternative | Supervised segmentation with domain adaptation for small sampled orbital CT images | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.1093/jcde/qwac029 | - |
dc.identifier.scopusid | 2-s2.0-85130643485 | - |
dc.identifier.wosid | 000783650700002 | - |
dc.identifier.bibliographicCitation | Journal of Computational Design and Engineering, v.9, no.2, pp 783 - 792 | - |
dc.citation.title | Journal of Computational Design and Engineering | - |
dc.citation.volume | 9 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 783 | - |
dc.citation.endPage | 792 | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002832704 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.subject.keywordPlus | DIABETIC-RETINOPATHY | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | NET | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | domain adaptation | - |
dc.subject.keywordAuthor | object segmentation | - |
dc.subject.keywordAuthor | optical nerve | - |
dc.subject.keywordAuthor | orbital tumour | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.