AUTOMATED CEREBRAL CAVERNOUS MALFORMATION LESION SEGMENTATION FROM SUSCEPTIBILITY WEIGHTED MR IMAGING

Motivation: Familial cerebral cavernous malformation (CCM) cases typically present with multi-focal lesions of varying size, count, and location making automatic segmentation for studying lesion burden challenging. Goal(s): We aimed to develop a deep learning framework for automated detection and volumetric quantification for ongoing large-scale, multi-site evaluation of patients with CCM. Approach: We implemented a framework that ensembles two-staged deep learning networks for large and small CCM lesions. Results: Our model achieved an overall Dice-score of 70%. We demonstrated the feasibility of a deep learning approach for detecting and segmenting various-sized lesions in patients with CCM using SWI acquired from multiple sites with various parameters. Impact: Our study found that total lesion volume quantified with our deep learning approach was statistically associated with increased odds of intracranial hemorrhage history. This demonstrates the potential benefit of our approach in providing a more accurate assessment of disease burden