Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data
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arXiv
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| Format: | Preprint |
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2025
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| author | Bintsi, Kyriaki-Margarita Balbastre, Yaël Wu, Jingjing Lehman, Julia F. Haber, Suzanne N. Yendiki, Anastasia |
| author_facet | Bintsi, Kyriaki-Margarita Balbastre, Yaël Wu, Jingjing Lehman, Julia F. Haber, Suzanne N. Yendiki, Anastasia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12942 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data Bintsi, Kyriaki-Margarita Balbastre, Yaël Wu, Jingjing Lehman, Julia F. Haber, Suzanne N. Yendiki, Anastasia Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.12942 |