Cheng Zhiming, Qu Aiping, He Xiaofeng
School of Computer, University of South China, Hengyang, 421001 China.
Hunan Provincial Base for Scientific and Technological Innovation Cooperation, Hengyang, 421001 China.
Vis Comput. 2022;38(3):749-762. doi: 10.1007/s00371-021-02075-9. Epub 2021 Feb 22.
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.
医学图像分割是临床环境中开发计算机辅助系统的关键且重要的一步。由于成像模态种类繁多且病例各异，它仍然是一项复杂且具有挑战性的任务。近年来，Unet因其在生物医学图像分割中的准确性能，已成为最受欢迎的深度学习框架之一。在本文中，我们提出了一种轮廓感知语义分割网络，它是Unet的扩展，用于医学图像分割。所提出的方法包括一个语义分支和一个细节分支。语义分支专注于从浅层和深层提取语义特征；细节分支用于增强浅层中隐含的轮廓信息。为了提高网络的表示能力，设计了一个MulBlock模块来提取具有不同感受野的语义信息。空间注意力模块（CAM）用于自适应地抑制冗余特征。与当前的先进方法相比，我们的方法在几个公共医学图像分割挑战中取得了显著的性能。
