Multi-scale Perception and Feature Refinement Network for multi-class segmentation of intracerebral hemorrhage in CT images

Intracerebral hemorrhage (ICH) poses a severe threat to human health and well-being. Automatic segmentation of hematomas in CT images can provide essential diagnostic assistance to physicians, and ensure improved treatment and recovery outcomes for patients. Existing methods for intracerebral hemorrhage segmentation mainly focus on identifying hemorrhage areas, without the ability to accurately distinguish and outline different types of hematomas. However, different types of hemorrhage exhibit a high degree of similarity in terms of gray matter level and shape, and the scale of hematomas can vary significantly. To address this issue, we propose a Multi-scale Perception and Feature Refinement Network (MPFR-Net) for automatic segmentation of both intraparenchymal and intraventricular hemorrhages. Specifically, we propose a Multi-scale Perception Module (MPM), which consists of the integration of features at different levels and the local and global multi-scale branches. MPM allows for the efficient extraction of multi-scale features and the establishment of long-range relationships between the target and background. Additionally, we propose a Feature Refinement Module (FRM) to refine fuzzy detail information that has been lost after down-sampling to the deep layer, while simultaneously supplementing the small target information from shallow features. To improve the clinical adaptability of our method, we further collect 608 patient cases from multiple hospitals to construct a multi-center dataset, termed as ICH-Seg in which each case contains both intraparenchymal and intraventricular hemorrhages. From the quantitative and visual results, MPFR-Net outperforms previous methods on both private and public datasets, showing promising segmentation for intracerebral hemorrhage and potential clinical applications.