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

## TL;DR

This paper introduces CE-Net, a novel deep learning architecture for 2D medical image segmentation that improves spatial information preservation and high-level feature extraction, outperforming previous models like U-Net.

## Contribution

The paper proposes CE-Net, featuring a dense atrous convolution and residual multi-kernel pooling, enhancing segmentation accuracy over existing methods.

## Key findings

- Outperforms U-Net and other state-of-the-art methods
- Effective across various medical imaging tasks
- Preserves spatial information better than previous models

## Abstract

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.