Multi-directional attention: a lightweight attention module for slender structures
Dong Liu, Yuyi Zhang, Yu Ren, Dehua Pan, Xiang He, Ming Cong, Guanghai Yu
Abstract
The segmentation of slender structures in images faces challenges of discontinuous segmentation and insufficient recognition. These slender structures, such as arteries and veins, are particularly important in 3D medical images that are sensitive to the computational complexity of segmentation networks. Therefore, in order to balance the computational complexity of the network and adaptive perception of slender structures, this paper proposes a multi-directional attention module for slender structures, which is a lightweight attention module that can be inserted into the encoder or decoder unit. At the same time, we propose a contour loss function to address the class imbalance phenomenon that may arise in the joint segmentation task of slender and ordinary structures. This function improves the balance by converting traditional class mask labels into contour mask labels. The effectiveness of our proposed module has been validated through training on segmentation tasks on 2D and 3D images.