# CE-Net: Context Encoder Network for 2D Medical Image Segmentation

arXiv: https://arxiv.org/abs/1903.02740 · HTML: https://mlsanity.com/paper/1903.02740/

## Key facts

- Cited by 220 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 30 of 220)

- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://mlsanity.com/paper/2211.00611/) (2022)
- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [MISSFormer: An Effective Medical Image Segmentation Transformer](https://mlsanity.com/paper/2109.07162/) (2021)
- [Ambiguous Medical Image Segmentation using Diffusion Models](https://mlsanity.com/paper/2304.04745/) (2023)
- [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.13385/) (2023)
- [Semantic Segmentation using Vision Transformers: A survey](https://mlsanity.com/paper/2305.03273/) (2023)
- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.05246/) (2024)
- [Multi-Compound Transformer for Accurate Biomedical Image Segmentation](https://mlsanity.com/paper/2106.14385/) (2021)
- [TransClaw U-Net: Claw U-Net with Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2107.05188/) (2021)
- [Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting](https://mlsanity.com/paper/2309.06824/) (2023)
- [Boundary-aware Transformers for Skin Lesion Segmentation](https://mlsanity.com/paper/2110.03864/) (2021)
- [REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening](https://mlsanity.com/paper/2202.08994/) (2022)
- [D-Former: A U-shaped Dilated Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2201.00462/) (2022)
- [Directional Connectivity-based Segmentation of Medical Images](https://mlsanity.com/paper/2304.00145/) (2023)
- [Coarse to Fine Multi-Resolution Temporal Convolutional Network](https://mlsanity.com/paper/2105.10859/) (2021)
- [MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset](https://mlsanity.com/paper/2306.16925/) (2023)
- [Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention](https://mlsanity.com/paper/2405.06284/) (2024)
- [C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation](https://mlsanity.com/paper/2212.11078/) (2022)
- [Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2307.09005/) (2023)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning](https://mlsanity.com/paper/2304.12856/) (2023)
- [AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation](https://mlsanity.com/paper/2207.13249/) (2022)
- [Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation](https://mlsanity.com/paper/2209.02048/) (2022)
- [T-Mamba: A unified framework with Long-Range Dependency in dual-domain for 2D & 3D Tooth Segmentation](https://mlsanity.com/paper/2404.01065/) (2024)
- [Improving Camouflaged Object Detection with the Uncertainty of Pseudo-edge Labels](https://mlsanity.com/paper/2110.15606/) (2021)
- [Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation](https://mlsanity.com/paper/2206.13632/) (2022)

## Abstract (arXiv)

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.