Anatomic tracer studies are critical for validating and improving diffusion MRI (dMRI) tractography. However, large-scale analysis of data from such studies is hampered by the labor-intensive process of annotating fiber bundles manually on histological slides. Existing automated methods often miss sparse bundles or require complex post-processing across consecutive sections, limiting their flexibility and generalizability. We present a streamlined, fully automated framework for fiber bundle segmentation in macaque tracer data, based on a U-Net architecture with large patch sizes, foreground aware sampling, and semisupervised pre-training. Our approach eliminates common errors such as mislabeling terminals as bundles, improves detection of sparse bundles by over 20% and reduces the False Discovery Rate (FDR) by 40% compared to the state-of-the-art, all while enabling analysis of standalone slices. This new framework will facilitate the automated analysis of anatomic tracing data at a large scale, generating more ground-truth data that can be used to validate and optimize dMRI tractography methods.
@article{arxiv.2508.12942,
title = {Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data},
author = {Kyriaki-Margarita Bintsi and Yaël Balbastre and Jingjing Wu and Julia F. Lehman and Suzanne N. Haber and Anastasia Yendiki},
journal= {arXiv preprint arXiv:2508.12942},
year = {2025}
}