Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data
This work addresses the bottleneck of large-scale analysis in neuroimaging by automating fiber bundle segmentation, though it is incremental as it builds on existing U-Net methods with specific enhancements.
The paper tackles the labor-intensive manual annotation of fiber bundles in anatomic tracer data by introducing a fully automated segmentation framework based on a U-Net architecture, which improves sparse bundle detection by over 20% and reduces the False Discovery Rate by 40% compared to state-of-the-art methods.
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.