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arXiv · 2508.12942

Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data

Kyriaki-Margarita Bintsi·Yaël Balbastre·Jingjing Wu·Julia F. Lehman·Suzanne N. Haber·Anastasia Yendiki
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Abstract

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.

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Kyriaki-Margarita Bintsi, Yaël Balbastre, Jingjing Wu, Julia F. Lehman, Suzanne N. Haber, Anastasia Yendiki. 2025-08-18. Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data. https://arxiv.org/abs/2508.12942

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