{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T13:33:33Z","timestamp":1781530413602,"version":"3.54.1"},"reference-count":31,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GRRC program of the Gyeonggi Province","award":["GRRC-Gachon2020 (B01)"],"award-info":[{"award-number":["GRRC-Gachon2020 (B01)"]}]},{"name":"AI-based Medical Image Analysis, and Gachon Gil Medical Center","award":["FRD2019-11-02(3)"],"award-info":[{"award-number":["FRD2019-11-02(3)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22010245","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T23:29:07Z","timestamp":1640906947000},"page":"245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach"],"prefix":"10.3390","volume":"22","author":[{"given":"Seok","family":"Oh","sequence":"first","affiliation":[{"name":"Gil Medical Center, Department of Biomedical Engineering, Gachon University College of Medicine, Incheon 21565, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young-Jae","family":"Kim","sequence":"additional","affiliation":[{"name":"Gil Medical Center, Department of Biomedical Engineering, Gachon University College of Medicine, Incheon 21565, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7574-4165","authenticated-orcid":false,"given":"Young-Taek","family":"Park","sequence":"additional","affiliation":[{"name":"HIRA Research Institute, Health Insurance Review & Assessment Service (HIRA), Wonju-si 26465, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-6038","authenticated-orcid":false,"given":"Kwang-Gi","family":"Kim","sequence":"additional","affiliation":[{"name":"Gil Medical Center, Department of Biomedical Engineering, Gachon University College of Medicine, Incheon 21565, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1159\/000134279","article-title":"Cystic lesions of the pancreas: A diagnostic and management dilemma","volume":"8","author":"Garcea","year":"2008","journal-title":"Pancreatology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.bpg.2013.04.001","article-title":"Precancerous lesions of the pancreas","volume":"27","author":"Zamboni","year":"2013","journal-title":"Best Pract. 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