MSRL-Net: An Automatic Segmentation of Intracranial Hemorrhage for CT Images Based on the U-Net Framework

Abstract

Intracranial hemorrhage (ICH) is a hemorrhagic disease occurring in the ventricle or brain, but we found that the U-Net network has poor segmentation performance for small lesion areas. In order to improve the segmentation accuracy, a new convolutional neural network called MSRL-Net is proposed in this paper to accurately segment the lesion regions in the CT images of intracranial hemorrhage. Specifically, to avoid the problem of missing information in the downsampling process, we propose a strategy combining MaxPool and SoftPool. In addition, the mixed loss function is used to optimize the unbalance of medical images. Finally, at the bottleneck layer, an MRHDC module is designed to represent the rich spatial information in the underlying features, in order to obtain multi-scale features with different receptive fields. Our model achieves 0.712 average Dice on a dataset. The experimental results show that this model has a good segmentation effect and potential clinical prospects.