Sardhara, TrushalTrushalSardharaDadsena, RaviRaviDadsenaAydin, Roland C.Roland C.AydinHilgers, Ralf-DieterRalf-DieterHilgersHorn, LeonLeonHornSchulz, Jörg B.Jörg B.SchulzReetz, KathrinKathrinReetzRomanzetti, SandroSandroRomanzettiDogan, ImisImisDoganthe FACROSS study group2025-11-172025-11-172025-11-07Machine Learning with Applications 22: 100790 (2025)https://hdl.handle.net/11420/58764Dentate nucleus (DN) degeneration is a key neuropathological feature in Friedreich’s ataxia (FRDA), and its accurate quantification is critical for understanding disease progression. However, its visualization and volumetry require iron-sensitive imaging techniques and time-consuming segmentation procedures, posing challenges for conventional ML approaches due to small datasets typical of rare diseases. We present a transfer learning–based machine learning pipeline for automated DN segmentation that directly uses standard T2*-weighted Magnetic Resonance Imaging (MRI), which highlights the DN without additional processing, and is designed to perform robustly with limited annotated data. Using 38 manually labeled subjects (18 FRDA, 20 controls), the model was validated via five-fold cross-validation and an independent hold-out test set, achieving Dice scores of 0.81–0.87 and outperforming classical atlas-based methods. Pretraining improved performance by ~10% in patients and >5% in controls. Applied to 181 longitudinal scans from 33 FRDA patients and 33 controls, the model revealed significantly reduced DN volumes in FRDA, with reductions correlating with disease duration and clinical severity over time. Our approach provides a scalable and reproducible segmentation framework, requiring minimal annotated data and no preprocessing, while demonstrating robust performance across crossvalidationand independent testing. Additionally, it enables the first longitudinal volumetric analysis of DN in FRDA using standard T2*-weighted MRI, demonstrating its practical utility for monitoring neurodegenerative changes. Overall, this work illustrates how transfer learning can overcome data scarcity in rare diseases and provides a robust methodology for automated MRI segmentation in both research and clinical applications.en2666-82702025Elsevier BVhttps://creativecommons.org/licenses/by/4.0/Automated reconstructionNeuroimaging markersNeurodegenerationLongitudinal analysisDisease progressionTechnology::616: DeseasesComputer Science, Information and General Works::006: Special computer methodsJournal Articlehttps://doi.org/10.15480/882.1615510.1016/j.mlwa.2025.10079010.15480/882.16155Journal Article