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Type 2 diabetes and metabolic syndrome are associated with pancreatic fat accumulation. Calculating the fat fraction aids in the investigation of \u03b2-cell malfunction and insulin resistance. The most widely used pancreas segmentation technique is a U-shaped network based on deep convolutional neural networks (DCNNs). They struggle to capture long-range biases in an image because they rely on local receptive fields. This research proposes a novel dual Self-attentive Transformer Unet (DSTUnet) model for accurate pancreatic segmentation, addressing this problem. This model incorporates dual self-attention Swin transformers on both the encoder and decoder sides to facilitate global context extraction and refine candidate regions. After segmenting the pancreas using a DSTUnet, a histogram analysis is used to estimate the fat fraction. The suggested method demonstrated excellent performance on the standard dataset, achieving a DSC of 93.7% and an HD of 2.7\u00a0mm. The average volume of the pancreas was 92.42, and its fat volume fraction (FVF) was 13.37%.<\/jats:p>","DOI":"10.1186\/s12880-025-01852-5","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T17:00:25Z","timestamp":1754326825000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A dual self-attentive transformer U-Net model for precise pancreatic segmentation and fat fraction estimation"],"prefix":"10.1186","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8852-6925","authenticated-orcid":false,"given":"Ashok","family":"Shanmugam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3907-351X","authenticated-orcid":false,"given":"Prianka Ramachandran","family":"Radhabai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7312-8385","authenticated-orcid":false,"given":"Kavitha","family":"KVN","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8921-8353","authenticated-orcid":false,"given":"Agbotiname Lucky","family":"Imoize","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"1852_CR1","doi-asserted-by":"publisher","first-page":"438","DOI":"10.21147\/j.issn.1000-9604.2023.05.03","volume":"35","author":"W Luo","year":"2023","unstructured":"Luo W, Wang J, Chen H, Liyuan Y, Qiu J, Liu Y, Wang R, et al. 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