Sequential glioblastoma segmentation via topological data analysis and spatial adjacency.

Segmenting glioblastoma in medical imaging remains challenging due to the tumor's irregular shape, heterogeneous texture, and poorly defined boundaries, which often lead to inaccurate delineation by conventional methods. To address these challenges, we propose a novel segmentation framework that leverages Topological Data Analysis (TDA) to capture intrinsic topological features of gliomas, reducing reliance on large annotated datasets while improving structural fidelity. Our approach exploits TDA's interpretable filtrations and persistent homology alongside explicit spatial adjacency information to enhance segmentation accuracy. The method proceeds sequentially: (1) Whole Tumor (WT) segmentation via an Automated Thresholding algorithm on reverse filtration (R-filtration), (2) Enhancing Tumor (ET) segmentation using carefully selected persistent homology features, and (3) non-enhancing Tumor Core (TC) and Edema (ED) segmentation based on their spatial adjacency with ET through a criterion-driven iterative process. To quantify segmentation performance, we introduce a novel fuzzy Edge-Dice score applicable across all steps. Evaluated on the public BRATS2021 and BRATS2022-Reg datasets, our TDA-based framework achieves robust and accurate segmentation, highlighting the potential of TDA methods to complement or surpass conventional deep learning approaches in real-world medical imaging applications.