Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Seok | - |
dc.contributor.author | Kim, Young-Jae | - |
dc.contributor.author | Park, Young-Taek | - |
dc.contributor.author | Kim, Kwang-Gi | - |
dc.date.accessioned | 2022-01-25T23:40:10Z | - |
dc.date.available | 2022-01-25T23:40:10Z | - |
dc.date.created | 2022-01-19 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83353 | - |
dc.description.abstract | The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS images. We employed the Attention U-Net model for automatic PCL segmentation. The Attention U-Net was compared with the Basic U-Net, Residual U-Net, and U-Net++ models. The Attention U-Net showed a better dice similarity coefficient (DSC) and intersection over union (IoU) scores than the other models on the internal test. Although the Basic U-Net showed a higher DSC and IoU scores on the external test than the Attention U-Net, there was no statistically significant difference. On the internal test of the cross-over study, the Attention U-Net showed the highest DSC and IoU scores. However, there was no significant difference between the Attention U-Net and Residual U-Net or between the Attention U-Net and U-Net++. On the external test of the cross-over study, all models showed no significant difference from each other. To the best of our knowledge, this is the first study implementing segmentation of PCL on EUS images using a deep-learning approach. Our experimental results show that a deep-learning approach can be applied successfully for PCL segmentation on EUS images. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | Sensors | - |
dc.title | Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000742430100001 | - |
dc.identifier.doi | 10.3390/s22010245 | - |
dc.identifier.bibliographicCitation | Sensors, v.22, no.1 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85121853959 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 22 | - |
dc.citation.number | 1 | - |
dc.contributor.affiliatedAuthor | Oh, Seok | - |
dc.contributor.affiliatedAuthor | Kim, Young-Jae | - |
dc.contributor.affiliatedAuthor | Kim, Kwang-Gi | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Computer-aided diagnosis | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Endoscopic ultrasonography | - |
dc.subject.keywordAuthor | Pancreatic cyst lesion | - |
dc.subject.keywordAuthor | Segmentation | - |
dc.subject.keywordPlus | FLUID ANALYSIS | - |
dc.subject.keywordPlus | CANCER | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon 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.