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ResDAC‑Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels
Date
2024
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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Description
Publisher
Springer
Citation
Medical & Biological Engineering & Computing
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Ganchev_2024_ResDac.pdf
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Funding Information
IReL Development Program of China under the Grant No. 2017YFE0135700, Tsinghua Precision Medicine Foundation under the Grant No. 2022TS003, the Bulgarian National Science Fund (BNSF) under the Grant No. КП-06- ИП-КИTAЙ/1 (КP-06-IP-CHINA/1), and the Telecommunications Research Centre (TRC) of University of Limerick, Ireland