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The complex anatomical structures and locations of PAT exacerbate the blurring phenomenon at the boundaries of MR images. Although Unet has become the cornerstone of PAT segmentation, it still faces challenges such as weak long\u2010range dependency capture and insufficient nonlinear modeling. These limitations are even more prominent in the scenario of few\u2010shot learning where labeled MR dataset is scarce. Therefore, we propose a novel few\u2010shot learning\u2010based Cross\u2010attention KAN\u2010Transformer Network, named CKTNet. Specifically, we propose an adaptive multi\u2010scale feature fusion module for dynamically obtaining valuable low\u2010level and high\u2010level features, fusing multi\u2010scale contextual information to alleviate the challenges posed by complex anatomical structures in PAT. Secondly, we combine KAN with Depthwise Convolution to construct a KAN encoder and embed it into a Transformer architecture. The KAN encoder can effectively capture the nonlinear relationship of PAT structure complexity. Finally, we propose a Cross\u2010attention Transformer block, which addresses two major shortcomings: the difficulty of prototype based few\u2010shot learning models in capturing subtle differences between instances, and the Transformer's lack of attention to local context. We compared our model with the state\u2010of\u2010the\u2010art models on the MRPEAT dataset in the MR modality. Our model achieved an  score of 0.799, precision of 0.762, recall of 0.842, and Hausdorff distance of 14.016. The experimental results show that our model significantly improves PAT MR images segmentation accuracy and interpretability, which is crucial for clinical applications.<\/jats:p>","DOI":"10.1002\/ima.70353","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T04:02:22Z","timestamp":1776657742000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>CKTNet<\/scp>\n                    : A Cross\u2010Attention\n                    <scp>KAN<\/scp>\n                    \u2010Transformer Based Few\u2010Shot Learning for Pericardial Adipose Tissue Segmentation"],"prefix":"10.1002","volume":"36","author":[{"given":"Junchi","family":"Lu","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering Shenyang University of Technology  Shenyang China"},{"name":"Key Laboratory of Intelligent Computing in Medical Image (MIIC), Ministry of Education  Shenyang 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