Diffusion-Based CT Image Segmentation for Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) is a life-threatening stroke that requires precise segmentation for effective treatment. To aid in diagnosing ICH, various deep learning-based methods have been proposed. However, these methods face challenges posed by irregular patterns and low-contrast boundaries in ICH images. In this paper, we introduce a novel conditional diffusion-based segmentation approach for ICH segmentation. Our framework leverages ResNet18 and a Transformer Block to enhance conditional feature extraction, as well as cross-attention mechanisms to align global and local features between the conditional and diffusion branches. Experiment results on the Instance2022 dataset show an improvement exceeding 10% in the Dice coefficient and IoU score, compared to the baseline diffusion-based model, MedSegDiffv2. More impressively, more than 20% improvement is achieved in terms of both the Dice coefficient and IoU score in cross-dataset evaluations on two benchmarks, i.e., the BHSD and PhysioNet, demonstrating excellent generalizability to unseen data.