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Contour-aware semantic segmentation network with spatial attention mechanism for medical image

Authors
  • Cheng, Zhiming1
  • Qu, Aiping1, 2
  • He, Xiaofeng1
  • 1 University of South China,
  • 2 Hunan Provincial Base for Scientific and Technological Innovation Cooperation, Hengyang, 421001 China
Type
Published Article
Journal
The Visual Computer
Publisher
Springer Berlin Heidelberg
Publication Date
Feb 22, 2021
Pages
1–14
Identifiers
DOI: 10.1007/s00371-021-02075-9
PMID: 33642659
PMCID: PMC7898027
Source
PubMed Central
Keywords
Disciplines
  • Original Article
License
Unknown

Abstract

Medical image segmentation is a critical and important step for developing computer-aided system in clinical situations. It remains a complicated and challenging task due to the large variety of imaging modalities and different cases. Recently, Unet has become one of the most popular deep learning frameworks because of its accurate performance in biomedical image segmentation. In this paper, we propose a contour-aware semantic segmentation network, which is an extension of Unet, for medical image segmentation. The proposed method includes a semantic branch and a detail branch. The semantic branch focuses on extracting the semantic features from shallow and deep layers; the detail branch is used to enhance the contour information implied in the shallow layers. In order to improve the representation capability of the network, a MulBlock module is designed to extract semantic information with different receptive fields. Spatial attention module (CAM) is used to adaptively suppress the redundant features. In comparison with the state-of-the-art methods, our method achieves a remarkable performance on several public medical image segmentation challenges.

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