FastSurfer-CC: A robust, accurate, and comprehensive framework for corpus callosum morphometry
This provides a robust, automated solution for researchers and clinicians studying brain aging and diseases, though it is incremental as it builds on existing segmentation methods.
The authors tackled the lack of comprehensive automated tools for corpus callosum morphometry by developing FastSurfer-CC, a framework that outperforms existing tools and detects statistically significant differences in Huntington's disease patients versus healthy controls not found by current state-of-the-art methods.
The corpus callosum, the largest commissural structure in the human brain, is a central focus in research on aging and neurological diseases. It is also a critical target for interventions such as deep brain stimulation and serves as an important biomarker in clinical trials, including those investigating remyelination therapies. Despite extensive research on corpus callosum segmentation, few publicly available tools provide a comprehensive and automated analysis pipeline. To address this gap, we present FastSurfer-CC, an efficient and fully automated framework for corpus callosum morphometry. FastSurfer-CC automatically identifies mid-sagittal slices, segments the corpus callosum and fornix, localizes the anterior and posterior commissures to standardize head positioning, generates thickness profiles and subdivisions, and extracts eight shape metrics for statistical analysis. We demonstrate that FastSurfer-CC outperforms existing specialized tools across the individual tasks. Moreover, our method reveals statistically significant differences between Huntington's disease patients and healthy controls that are not detected by the current state-of-the-art.