MedSAM-Guided Curriculum Learning for White Matter Tract Segmentation in Block Face Imaging of Fetal Brain

Athira Kalladayil Shibu, Sriprabha Ramanarayanan, Vinoth Kanna, Jaikishan Jayakumar, Keerthi Ram, Mohanasankar Sivaprakasam; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 1034-1041

Block Face Imaging (BFI) is a high-resolution snapshot acquired during histological sectioning that captures the exposed tissue surface using tissue autofluorescence of lipids and NADPH. BFI offers up to five times greater white-gray matter contrast than traditional imaging, allowing clear visualization of white matter(WM) tracts. To enable accurate segmentation of WM tracts, we introduce a MedSAM-guided curriculum learning that progressively improves performance by training on increasingly complex samples. This approach takes advantage of both the anatomical precision of BFI and the generalizability of the medical foundation models. The resulting segmentation are manually validated by expert neuroscientists to ensure anatomical accuracy. Our method achieves an average Dice score of 0.9297, evaluated across 230 BFI slices with a spatial resolution of approximately 60um, offering significantly enhanced structural clarity over conventional MRI-based approaches (500um resolution) and outperforming state-of-the-art segmentation techniques. These findings demonstrate the effectiveness of our approach in leveraging high-resolution BFI data for accurate WM tract segmentation, highlighting its potential as a complementary tool alongside conventional neuroimaging techniques.