Abstract
Abstract
Identifying structural abnormalities such as hippocampal sclerosis (HS) and focal cortical dysplasia (FCD) on conventional magnetic resonance imaging remains challenging. We introduce the
Single-Subject Morphometry
(SSM) toolbox, a voxel-based framework designed with rule-based statistical approaches for individualized morphometric analysis with customizable embedded normative metrics. SSM utilizes conventional preprocessing pipelines to generate subject-specific maps and reports of gray (GM) and white matter abnormalities and FCD lesions. We evaluated SSM against reference tools using 377 subjects from public and internal datasets for the GM and FCD modalities. For HS (n = 211), SSM achieved a lateralization accuracy of 94% while AID-HS achieved 96%. For FCD detection (n = 103), SSM correctly localized lesions in 70% of cases, similar to the 73% achieved by MELD-Graph, while completing processing approximately nine times faster. Although SSM yielded more false-positive clusters, it provided robust whole-brain statistical inference rather than restricted focal predictions. The SSM toolbox provides a flexible, fast, and statistically interpretable framework for individualized morphometric analysis. By enabling comprehensive whole-brain characterization of structural abnormalities, SSM offers a valuable and transparent alternative for clinical research and diagnostic support in neurological disorders.