Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data

• URL: http://arxiv.org/abs/2508.12942v2
• Date: Tue, 19 Aug 2025 15:58:58 GMT
• Title: Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data
• Authors: Kyriaki-Margarita Bintsi, Yaël Balbastre, Jingjing Wu, Julia F. Lehman, Suzanne N. Haber, Anastasia Yendiki,
• Abstract summary: We present a streamlined, fully automated framework for fiber bundle segmentation in macaque tracer data.<n>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.<n>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.
• Score: 1.714556946340362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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