Benchmarking Generalizability in Deep Learning-Based White Matter Tract Segmentation
Junbeom Kwon, Gabriele Amorosino, Franco Pestilli
This study benchmarks deep learning models for white matter tract segmentation across diverse datasets, finding that CNN-based models perform best within their training domain but degrade under domain shifts, and introduces an ensemble method to mitigate this issue.