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Accurate segmentation of pancreatic tissue is crucial for computer-aided diagnosis systems, as it can be used for surgical planning, navigation, and assessment of organs. In the light of this, the current paper proposes a novel Residual Double Asymmetric Convolution Network (ResDAC-Net) model. Firstly, newly designed ResDAC blocks are used to highlight pancreatic features. Secondly, the feature fusion between adjacent encoding layers fully utilizes the low-level and deep-level features extracted by the ResDAC blocks. Finally, parallel dilated convolutions are employed to increase the receptive field to capture multiscale spatial information. ResDAC-Net is highly compatible to the existing state-of-the-art models, according to three (out of four) evaluation metrics, including the two main ones used for segmentation performance evaluation (i.e., DSC and Jaccard index).<\/jats:p>\n Graphical abstract<\/jats:bold><\/jats:p>","DOI":"10.1007\/s11517-024-03052-9","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T06:29:26Z","timestamp":1709879366000},"page":"2087-2100","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels"],"prefix":"10.1007","volume":"62","author":[{"given":"Zhanlin","family":"Ji","sequence":"first","affiliation":[]},{"given":"Jianuo","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Juncheng","family":"Mu","sequence":"additional","affiliation":[]},{"given":"Haiyang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chenxu","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Na","family":"Yuan","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0535-7087","authenticated-orcid":false,"given":"Ivan","family":"Ganchev","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"3052_CR1","doi-asserted-by":"publisher","first-page":"5013","DOI":"10.1109\/JBHI.2022.3192277","volume":"26","author":"L Xie","year":"2022","unstructured":"Xie L, Cai W, Gao Y (2022) DMCGNet: a novel network for medical image segmentation with dense self-mimic and channel grouping mechanism. 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