IEEE Transactions on Medical Imaging· 2019Q1
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
- 2,224citations
- Q1SCImago
- 2019year
Short summary
CE-Net, a novel deep learning architecture, achieves superior 2D medical image segmentation by integrating a context extractor with dense atrous and residual multi-kernel pooling blocks, outperforming U-Net and other methods on tasks like optic disc and lung segmentation.
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Key points
- CE-Net architecture incorporates a context extractor with dense atrous convolution and residual multi-kernel pooling.
- The model uses a pretrained ResNet block as a fixed feature extractor.
- CE-Net preserves spatial information better than U-Net by mitigating information loss from pooling and strided convolutions.
- The method was validated on multiple 2D medical image segmentation tasks, including optic disc, vessel, lung, cell, and retinal OCT segmentation.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. IEEE Transactions on Medical Imaging, 2019 · DOI ↗
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Field: Radiology, Nuclear Medicine and Imaging
Radiology, Nuclear Medicine and ImagingMedicine