Deep learning-based 3D reconstruction of dentate nuclei in Friedreich’s ataxia from T2*weighted MR images

Trushal Sardhara, Ravi Dadsena, Roland C. Aydin, Ralf-Dieter Hilgers, Leon Horn, Jörg B. Schulz, Kathrin Reetz, Sandro Romanzetti, Imis Dogan, Stella A. Lischewski, Kerstin Konrad, Miguel Pishnamaz, Maximillian Praster, Thomas Clavel, Vera Jankowski, Joachim Jankowski, Oliver Pabst, Katharina Marx-Schütt, Nikolaus Marx, Julia Möllmann, Malte Jacobsen, Juergen Dukart, Simon Eickhoff, Deep learning-based 3D reconstruction of dentate nuclei in Friedreich’s ataxia from T2*weighted MR images, Machine Learning with Applications, 2025, 100790, ISSN 2666-8270, doi:10.1016/j.mlwa.2025.100790. 

 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.