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. 2024 Jul;62(7):2087-2100.
doi: 10.1007/s11517-024-03052-9. Epub 2024 Mar 8.

ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels

Zhanlin Ji  1 , Jianuo Liu  1 , Juncheng Mu  1 , Haiyang Zhang  2 , Chenxu Dai  1 , Na Yuan  3 , Ivan Ganchev  4   5   6
Affiliations

ResDAC-Net: a novel pancreas segmentation model utilizing residual double asymmetric spatial kernels

Zhanlin Ji et al. Med Biol Eng Comput. 2024 Jul.

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).

Keywords: Image segmentation; Medical image processing; Pancreatic segmentation; ResDAC-Net.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The elaborated ResDAC block
Fig. 2
Fig. 2
The elaborated ALF block
Fig. 3
Fig. 3
The elaborated PDC block
Fig. 4
Fig. 4
The proposed ResDAC-Net model
Fig. 5
Fig. 5
The DSC validation curves of the compared models
Fig. 6
Fig. 6
The DSC training curves of the compared models
Fig. 7
Fig. 7
Sample visualizations of pancreas segmentation results of different models on NIH dataset
See this image and copyright information in PMC

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