W-Net: A boundary-enhanced segmentation network for stroke lesions

Abstract

Accurate lesion segmentation is a critical technology basis for the treatment and prognosis of stroke. Stroke lesion segmentation suffers from complex background and noise interferes due to the similar grayscale characteristics of tissue structures and imaging mode in medical image. To resolve the difficulty of fuzzy boundary for lesion segmentation, we propose a novel two-stage brain MRI lesion segmentation method, called W-Net. W-Net leverages the CNN and transformer-based method as the backbone network, and introduces boundary deformation module (BDM), boundary constraint module (BCM) to handle the tough condition of fuzzy boundary. The BDM utilizes circular convolution to correct the initial boundary and BCM exploits dilated convolution to dynamically constrain the object boundary. Moreover, we design a multi-task learning loss function to optimize the W-Net from both region and boundary perspectives. Extensive experiments are conducted using the two ischemic stroke lesion segmentation datasets, and the results are compared with other advanced models. The experimental results demonstrate that the proposed method is effective in preserving the edge information of stroke lesions, thus achieving competitive performance on the segmentation task.