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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    Traditional fracture diagnosis relies heavily on the experience of clinicians and the interpretation of medical imaging. In complex cases, the inefficiency of manual interpretation often leads to misdiagnosis or missed detection, underscoring the need for automated segmentation techniques. A major challenge in calcaneal fracture image segmentation lies in the blurred and irregular boundaries of fractures, coupled with the scarcity of high-quality annotated data. To address these issues, this study independently constructs the first dataset specifically dedicated to Calcaneal Fracture segmentation, termed CalFrac. This dataset, collected from Ruijin Hospital in Shanghai, comprises CT scans of calcaneal fractures from 139 patients, along with corresponding pixel-level annotated ground truth segmentation masks. In addition, we propose the Calcaneal Fracture segmentation-Edge detection Network (CFE-Net), a multi-task CNN-Transformer hybrid architecture that employs a dual-branch structure to jointly perform fracture segmentation and edge detection. The main segmentation network adopts an encoder\u2013decoder design to localize the fracture region, while the edge detection branch extracts boundary information and refines the segmentation via cross-branch feature interaction. Experiments on the CalFrac dataset compare CFE-Net with eight state-of-the-art methods. CFE-Net achieves superior performance across all evaluation metrics, demonstrating its advantages in both region integrity and boundary delineation. We have released the dataset and code at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/esdszdx0\/CalFrac-Dataset\">https:\/\/github.com\/esdszdx0\/CalFrac-Dataset<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3817116","type":"journal-article","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T14:30:41Z","timestamp":1779546641000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Edge-Enhanced Calcaneal Fracture Segmentation Using a Multi-Task CNN-Transformer Hybrid Network"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6183-5394","authenticated-orcid":false,"given":"Xinfan","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Transportation and Civil Engineering, Nantong University, Nantong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4614-1023","authenticated-orcid":false,"given":"Quan","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Transportation and Civil Engineering, Nantong University, Nantong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8277-797X","authenticated-orcid":false,"given":"Guangcheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Transportation and Civil Engineering, Nantong University, Nantong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6680-1148","authenticated-orcid":false,"given":"Lijuan","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Transportation and Civil Engineering, Nantong University, Nantong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3255-4211","authenticated-orcid":false,"given":"Ming","family":"Ni","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4055-7503","authenticated-orcid":false,"given":"Kui","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Industry University, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"issue":"1","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1038\/s41597-023-02432-4","article-title":"FracAtlas: A dataset for fracture classification, localization and segmentation of musculoskeletal radiographs","volume":"10","author":"Abedeen Iftekharul","year":"2023","unstructured":"Iftekharul Abedeen, Md Ashiqur Rahman, Fatema Zohra Prottyasha, Tasnim Ahmed, Tareque Mohmud Chowdhury, and Swakkhar Shatabda. 2023. FracAtlas: A dataset for fracture classification, localization and segmentation of musculoskeletal radiographs. Scientific Data 10, 1 (2023), 521.","journal-title":"Scientific Data"},{"issue":"8","key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.compbiomed.2011.05.017","article-title":"Detection of masses in mammogram images using CNN, geostatistic functions and SVM","volume":"41","author":"Sampaio Wener Borges","year":"2011","unstructured":"Wener Borges Sampaio, Edgar Moraes Diniz, Arist\u00f3fanes Corr\u00eaa Silva, Anselmo Cardoso de Paiva, and Marcelo Gattass. 2011. Detection of masses in mammogram images using CNN, geostatistic functions and SVM. 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Slater, Christopher L. Sistrom, Anthony A. Mancuso, et al. 2024. Challenges in diagnosis of calcaneal fractures: An examination using the WIDI SIM platform. Emerging Radiology 31 (2024), 653\u2013660.","journal-title":"Emerging Radiology"},{"issue":"1","key":"e_1_3_1_20_2","doi-asserted-by":"crossref","first-page":"20431","DOI":"10.1038\/s41598-023-47706-4","article-title":"Automatic segmentation of inconstant fractured fragments for tibia\/fibula from CT images using deep learning","volume":"13","author":"Kim Hyeonjoo","year":"2023","unstructured":"Hyeonjoo Kim, Young Dae Jeon, Ki Bong Park, Hayeong Cha, Moo-Sub Kim, Juyeon You, Se-Won Lee, Seung-Han Shin, Yang-Guk Chung, Sung Bin Kang, et al. 2023. Automatic segmentation of inconstant fractured fragments for tibia\/fibula from CT images using deep learning. 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