Multi-level Topological Analysis Framework for Multifocal Diseases

Feature engineering and deep learning have been widely used to characterize imaging features in medical applications. However, the importance of geometric structure and spatial relationship of multiple lesions for multifocal diseases are often neglected by these methods. In this paper, we propose a Multi-level Topological Analysis (MTA) framework based on persistent homology, by capturing global-level topological invariants underlying geometric structure and local-level spatial adjacency relationship among lesions and local structure. In particular, a novel Filtration-based Community Discovery algorithm is designed to efficiently partition lesions to local clusters. Experiments demonstrate that our MTA framework outperforms five state-of-the-art persistent homology methods and achieved AUC 824±0.132 on a task of differentiating two multifocal diseases, Multiple Sclerosis and Neuromyelitis Optica.