ICHSC-Diff: A Dual-Stream Guided Conditional Diffusion Model for Early Hematoma Expansion Prediction

Abstract

Intracerebral hemorrhage (ICH) is a type of stroke that, although less common than ischemic stroke, has higher mortality and disability rates. Among its complications, hematoma expansion (HE) is a major determinant of poor outcomes. Accurate segmentation of ICH lesions and timely prediction of HE are crucial for effective intervention. Existing methods mainly rely on the clinical experience of neurosurgeons and typically treat these two tasks independently, fundamentally overlooking the powerful, synergistic information shared between a hematoma’s morphology and its propensity for expansion, leading to inaccurate HE prediction. Furthermore, conventional deep learning models often struggle to capture irregular hematoma boundaries and fail to reconcile the feature conflict between pixel-level segmentation and image-level prediction. To comprehensively address these limitations, we propose a dual-stream guided conditional diffusion framework called ICHSC-Diff. It facilitates a symbiotic interplay by leveraging the intrinsic generative capabilities of diffusion, coupling generative features with explicit radiological data to form a multi-modal representation for robust early HE prediction. In particular, we design a Task-aware Conditioning Module that utilizes a Style Preservation Refinement Module (SPRM) for adaptive preliminary denoising, dynamically adjusting the filters to match different hematomas. Thereafter, a Synergistic Representation Generator captures multi-scale local features by separating noise from the signal, preserving edge and texture details to guide the generation of accurate hematoma masks. Moreover, we develop a Multi-Scale Hybrid Fusion (MSHF) module that uses an attention-driven mechanism to reconstruct and fuse radiological features with generative features. This process reduces redundant information unrelated to hematoma characteristics, leading to robust early HE prediction. We further accelerate the inference process using the DPM-Solver strategy, enhancing the model’s efficiency. Extensive experiments on real-world intracerebral hemorrhage datasets demonstrate that our method excels in overall prediction performance, which in turn enhances ICH segmentation performance. The source codes of our framework are publicly available at https://github.com/tarkmfcv/ICHSC-Diff