ICH-PFNet: Prompt-Free Intracerebral Hemorrhage Segmentation via Convolutional Sparse Embeddings and Contrastive Semantic Consistency

Intracerebral hemorrhage (ICH) necessitates precise and efficient segmentation of hemorrhagic regions in head computed tomography (CT) scans to facilitate timely clinical decisions. To address challenges such as irregular shapes of hematomas, unclear lesion boundaries, and the scarcity of annotated data, we introduce the ICH-PFNet, a text-guided segmentation framework specifically designed for ICH imaging that operates without prompts. The Mamba Pyramid Downsampling module ensures robust multi-scale feature extraction, while the GCS-CLIP fusion mechanism enhances semantic consistency through batch-level contrastive similarity. The Enhanced SAM module provides automatic spatial guidance and convolution-based sparse embeddings to eliminate manual input. Furthermore, a Feature Pyramid Network combined with a Group Aggregation Bridge enhances multi-scale feature fusion and refines boundaries. Our model showed superior performance in segmenting small and structurally complex hemorrhages by using a private CT dataset. These results highlight its potential for integration into automated ICH assessment workflows. The code is available at https://github.com/Hzchzc123/ICH-CMNet.