Dataset Information

Attention-guided duplex adversarial U-net for pancreatic segmentation from computed tomography images.

ABSTRACT:

Purpose

Segmenting the organs from computed tomography (CT) images is crucial to early diagnosis and treatment. Pancreas segmentation is especially challenging because the pancreas has a small volume and a large variation in shape.

Methods

To mitigate this issue, an attention-guided duplex adversarial U-Net (ADAU-Net) for pancreas segmentation is proposed in this work. First, two adversarial networks are integrated into the baseline U-Net to ensure the obtained prediction maps resemble the ground truths. Then, attention blocks are applied to preserve much contextual information for segmentation. The implementation of the proposed ADAU-Net consists of two steps: 1) backbone segmentor selection scheme is introduced to select an optimal backbone segmentor from three two-dimensional segmentation model variants based on a conventional U-Net and 2) attention blocks are integrated into the backbone segmentor at several locations to enhance the interdependency among pixels for a better segmentation performance, and the optimal structure is selected as a final version.

Results

The experimental results on the National Institutes of Health Pancreas-CT dataset show that our proposed ADAU-Net outperforms the baseline segmentation network by 6.39% in dice similarity coefficient and obtains a competitive performance compared with the-state-of-art methods for pancreas segmentation.

Conclusion

The ADAU-Net achieves satisfactory segmentation results on the public pancreas dataset, indicating that the proposed model can segment pancreas outlines from CT images accurately.

SUBMITTER: Li M

PROVIDER: S-EPMC8992955 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Attention-guided duplex adversarial U-net for pancreatic segmentation from computed tomography images.

Li Meiyu M Lian Fenghui F Li Yang Y Guo Shuxu S

Journal of applied clinical medical physics 20220224 4

<h4>Purpose</h4>Segmenting the organs from computed tomography (CT) images is crucial to early diagnosis and treatment. Pancreas segmentation is especially challenging because the pancreas has a small volume and a large variation in shape.<h4>Methods</h4>To mitigate this issue, an attention-guided duplex adversarial U-Net (ADAU-Net) for pancreas segmentation is proposed in this work. First, two adversarial networks are integrated into the baseline U-Net to ensure the obtained prediction maps res ...[more]

PMID: 35199477

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Dataset Information

Attention-guided duplex adversarial U-net for pancreatic segmentation from computed tomography images.

Purpose

Methods

Results

Conclusion

Publications

Attention-guided duplex adversarial U-net for pancreatic segmentation from computed tomography images.

Similar Datasets

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