{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T02:16:22Z","timestamp":1773281782777,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:00:00Z","timestamp":1762560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003052","name":"the Technology Innovation Program","doi-asserted-by":"publisher","award":["RS-2025-02305698"],"award-info":[{"award-number":["RS-2025-02305698"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Ministry of Trade, Industry & Energy (MOTIE, Korea) and by the Clinical Research Supporting Program of the Korea Pancreas Surgery Club","award":["KPSC-2023-001"],"award-info":[{"award-number":["KPSC-2023-001"]}]},{"name":"Gachon University research fund","award":["GCU-202410470001"],"award-info":[{"award-number":["GCU-202410470001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Accurate volumetry of the remnant pancreas after pancreatectomy is crucial for assessing postoperative endocrine and exocrine function but remains challenging due to anatomical variability and complex postoperative morphology. This study aimed to develop and validate a deep learning (DL)-based model for automatic segmentation and volumetry of the remnant pancreas using abdominal CT images. Methods: A total of 1067 CT scans from 341 patients who underwent pancreaticoduodenectomy and 512 scans from 184 patients who underwent distal pancreatectomy were analyzed. Ground truth masks were manually delineated and verified through multi-expert consensus. Six 3D segmentation models were trained and compared, including four convolution-based U-Net variants (basic, dense, residual, and residual dense) and two transformer-based models (Trans U-Net and Swin U-Net). Model performance was evaluated using five-fold cross-validation with sensitivity, specificity, precision, accuracy, and Dice similarity coefficient. Results: The Residual Dense U-Net achieved the best performance among convolutional models, with dice similarity coefficient (DSC) values of 0.7655 \u00b1 0.0052 for pancreaticoduodenectomy and 0.8086 \u00b1 0.0091 for distal pancreatectomy. Transformer-based models showed slightly higher DSCs (Swin U-Net: 0.7787 \u00b1 0.0062 and 0.8132 \u00b1 0.0101), with statistically significant but numerically small improvements (p &lt; 0.01). Conclusions: The proposed DL-based approach enables accurate and reproducible postoperative pancreas segmentation and volumetry. Automated volumetric assessment may support objective evaluation of remnant pancreatic function and provide a foundation for predictive modeling in long-term clinical management after pancreatectomy.<\/jats:p>","DOI":"10.3390\/diagnostics15222834","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T10:35:31Z","timestamp":1762770931000},"page":"2834","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning Model for Volume Measurement of the Remnant Pancreas After Pancreaticoduodenectomy and Distal Pancreatectomy"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0443-0051","authenticated-orcid":false,"given":"Young Jae","family":"Kim","sequence":"first","affiliation":[{"name":"Gachon Biomedical & Convergence Institute, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9656-0605","authenticated-orcid":false,"given":"Juhui","family":"Lee","sequence":"additional","affiliation":[{"name":"Medical Devices R & D Center, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeon-Ho","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Surgery, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6256-3103","authenticated-orcid":false,"given":"Jaehun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Surgery, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Doojin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Surgery, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-6038","authenticated-orcid":false,"given":"Kwang Gi","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9939-207X","authenticated-orcid":false,"given":"Doo-Ho","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Surgery, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"386","DOI":"10.14701\/ahbps.2018.22.4.386","article-title":"Recent treatment patterns and survival outcomes in pancreatic cancer according to clinical stage based on single-center large-cohort data","volume":"22","author":"Lee","year":"2018","journal-title":"Ann. 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