Papers › ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Zhijie Zhang, Huazhu Fu, Hang Dai, Jianbing Shen, Yanwei Pang, Ling Shao
Segmentation is a fundamental task in medical image analysis. However, most existing methods focus on primary region extraction and ignore edge information, which is useful for obtaining accurate segmentation. In this paper, we propose a generic medical segmentation method, called Edge-aTtention guidance Network (ET-Net), which embeds edge-attention representations to guide the segmentation network. Specifically, an edge guidance module is utilized to learn the edge-attention representations in the early encoding layers, which are then transferred to the multi-scale decoding layers, fused using a weighted aggregation module. The experimental results on four segmentation tasks (i.e., optic disc/cup and vessel segmentation in retinal images, and lung segmentation in chest X-Ray and CT images) demonstrate that preserving edge-attention representations contributes to the final segmentation accuracy, and our proposed method outperforms current state-of-the-art segmentation methods. The source code of our method is available at https://github.com/ZzzJzzZ/ETNet.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Lung Nodule Segmentation | LUNA | ET-Net | Accuracy | 0.9868 | #5 of 5 | Archive leaderboard | report |
| Lung Nodule Segmentation | LUNA | ET-Net | mIoU | 0.9623 | #5 of 5 | Archive leaderboard | report |
| Lung Nodule Segmentation | Montgomery County | ET-Net | Accuracy | 0.9865 | #1 of 1 | Archive leaderboard | report |
| Lung Nodule Segmentation | Montgomery County | ET-Net | mIoU | 0.942 | #1 of 1 | Archive leaderboard | report |
| Retinal Vessel Segmentation | DRIVE | ET-Net | Accuracy | 0.956 | #22 of 22 | Archive leaderboard | report |
| Retinal Vessel Segmentation | DRIVE | ET-Net | mIoU | 0.7744 | #22 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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