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Contour-aware semantic segmentation network with spatial attention mechanism for medical image
The Visual Computer ( IF 3.5 ) Pub Date : 2021-02-22 , DOI: 10.1007/s00371-021-02075-9
Zhiming Cheng 1 , Aiping Qu 1, 2 , Xiaofeng He 1
Affiliation  

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.



中文翻译:

具有空间注意机制的医学图像轮廓感知语义分割网络

医学图像分割是在临床情况下开发计算机辅助系统的关键和重要步骤。由于各种成像方式和不同的病例,它仍然是一项复杂而具有挑战性的任务。最近,Unet 因其在生物医学图像分割中的准确性能而成为最流行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。所提出的方法包括语义分支和细节分支。语义分支侧重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,MulBlock 模块旨在提取具有不同感受野的语义信息。空间注意模块(CAM)用于自适应抑制冗余特征。与最先进的方法相比,我们的方法在几个公共医学图像分割挑战中取得了显着的性能。

更新日期:2021-02-22
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