Deformable Attention U-Shaped Network with Progressively Supervised Learning for Subarachnoid Hemorrhage Image Segmentation

Hao Sun, Lianghao Jin, Wei Xie

Abstract: Subarachnoid hemorrhage (SAH) is a common acute disease, which belongs to a subtype of intracranial hemorrhage. In this paper, a deformable attention u-shaped network (DAUN) is specially designed for SAH image segmentation. Firstly, a deformable attention module is embedded at the end of each encoding layer in Res-UNet to adaptively adjust the attention domain for alleviating the introduction of irrelevant information. Then, to improve the segmentation accuracy on irregular edges and small lesions, a region-boundary-aware loss is utilized to optimize the model. Finally, a progressively supervised learning strategy is proposed to train the proposed DAUN, which enables DAUN to find a balance between the focus on semantic information and position information of each pixel. A novel SAHCT dataset is constructed to demonstrate the performance of DAUN. In addition, the Monuseg dataset is utilized to evaluate the generalization ability of DAUN.