Multiple sclerosis (MS) is characterized by heterogeneous white matter lesions that vary in immune activity, myelin damage, and spatial organization. This heterogeneity complicates the systematic characterization of lesion subtypes and their role in disease progression. To address this, our group is generating a large-scale neuropathological imaging dataset in close collaboration with the Netherlands Brain Bank (NBB), a unique resource providing high-quality post-mortem brain tissue with minimal post-mortem delay. These high-resolution histological images capture the full spectrum of lesion variability; however, their analysis remains limited by the need for expert annotation.

Our core technical contributions are threefold: (1) a patch-based processing strategy that enables efficient analysis of gigapixel whole-slide images; (2) the use of a vision foundation model, whose rich and generalizable representations - learned from large-scale image data - transfer effectively to this specialized histological domain; and (3) a label-efficient pipeline that leverages these pre-trained features to achieve strong performance using only 100 annotated images from a cohort of ~2,000. Notably, the foundation model embeddings were sufficiently expressive to capture pathologically relevant structures. The model achieved 85% accuracy in lesion segmentation. Overall, this embedding-based framework provides a scalable and data-efficient approach for large-scale analysis and can be extended to other neuropathological applications.

Co-authors: Abel Koffeman, Inge Holtman