Papers › CE-Net: Context Encoder Network for 2D Medical Image Segmentation
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, Jiang Liu
Medical image segmentation is an important step in medical image analysis. With the rapid development of convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, blood vessel detection, lung segmentation, cell segmentation, etc. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations lead to the loss of some spatial information. In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation. CE-Net mainly contains three major components: a feature encoder module, a context extractor and a feature decoder module. We use pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution (DAC) block and residual multi-kernel pooling (RMP) block. We applied the proposed CE-Net to different 2D medical image segmentation tasks. Comprehensive results show that the proposed method outperforms the original U-Net method and other state-of-the-art methods for optic disc segmentation, vessel detection, lung segmentation, cell contour segmentation and retinal optical coherence tomography layer segmentation.
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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 | CE-Net | Accuracy | 0.99 | #4 of 5 | Archive leaderboard | report |
| Medical Image Segmentation | ISBI 2012 EM Segmentation | CE-Net | VInfo | 0.9878 | #2 of 3 | Archive leaderboard | report |
| Medical Image Segmentation | ISBI 2012 EM Segmentation | CE-Net | VRand | 0.9743 | #2 of 3 | Archive leaderboard | report |
| Retinal Vessel Segmentation | DRIVE | CE-Net | AUC | 0.9779 | #15 of 22 | Archive leaderboard | report |
| Retinal Vessel Segmentation | DRIVE | CE-Net | Accuracy | 0.9545 | #15 of 22 | Archive leaderboard | report |
| Retinal Vessel Segmentation | ROSE-1 DVC | CE-Net | Dice Score | 57.83 | #5 of 5 | Archive leaderboard | report |
| Retinal Vessel Segmentation | ROSE-1 SVC | CE-Net | Dice Score | 75.11 | #3 of 5 | Archive leaderboard | report |
| Retinal Vessel Segmentation | ROSE-1 SVC-DVC | CE-Net | Dice Score | 73.00 | #4 of 5 | Archive leaderboard | report |
| Retinal Vessel Segmentation | ROSE-2 | CE-Net | Dice Score | 70.66 | #3 of 5 | 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.
Methods
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