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

TLDR

Medical image segmentation is crucial for analysis, and deep learning, especially U‑Net variants, has been widely applied, but conventional pooling and strided convolutions lose spatial detail. This paper introduces the CE‑Net to capture high‑level context while preserving spatial information for 2‑D medical image segmentation. CE‑Net comprises a ResNet‑based encoder, a context extractor using dense atrous convolutions and residual multi‑kernel pooling, and a decoder, applied to various segmentation tasks. Experiments show CE‑Net surpasses U‑Net and other state‑of‑the‑art methods on optic disc, vessel, lung, cell contour, and OCT layer segmentation.

Abstract

Medical image segmentation is an important step in medical image analysis. With the rapid development of a 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, and so on. Previously, U-net based approaches have been proposed. However, the consecutive pooling and strided convolutional operations led to the loss of some spatial information. In this paper, we propose a context encoder network (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 the pretrained ResNet block as the fixed feature extractor. The context extractor module is formed by a newly proposed dense atrous convolution block and a residual multi-kernel pooling 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.