ArticleMedical & biological engineering & computing2024
ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels.
Article in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Optimized Spatial Transformer for Segmenting Pancreas Abnormalities.Journal of imaging informatics in medicine · 2025Article
- Deep learning and radiomics-driven algorithm for automated identification of May-Thurner syndrome in Iliac CTV imaging.Frontiers in medicine · 2025Article
- An optimized two stage U-Net approach for segmentation of pancreas and pancreatic tumor.MethodsX · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
The pancreas not only is situated in a complex abdominal background but is also surrounded by other abdominal organs and adipose tissue, resulting in blurred organ boundaries. 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).
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Registered trials
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