Intracerebral Haemorrhage Growth Prediction Based on Displacement Vector Field and Clinical Metadata

Intracerebral hemorrhage (ICH) is the deadliest type of stroke. Early prediction of stroke lesion growth is crucial in assisting physicians towards better stroke assessments. Existing stroke lesion prediction methods are mainly for ischemic stroke. In ICH, most methods only focus on whether the hematoma will expand but not how it will develop. This paper explored a new, unknown topic of predicting ICH growth at the image-level based on the baseline non-contrast computerized tomography (NCCT) image and its hematoma mask. We propose a novel end-to-end prediction framework based on the displacement vector fields (DVF) with the following advantages. 1) It can simultaneously predict CT image and hematoma mask at follow-up, providing more clinical assessment references and surgery indication. 2) The DVF regularization enforces a smooth spatial deformation, limiting the degree of the stroke lesion changes and lowering the requirement of large data. 3) A multi-modal fusion module learns high-level associations between global clinical features and spatial image features. Experiments on a multi-center dataset demonstrate improved performance compared to several strong baselines. Detailed ablation experiments are conducted to highlight the contributions of various components.