# Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data

arXiv: https://arxiv.org/abs/2508.12942 · HTML: https://mlsanity.com/paper/2508.12942/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions](https://mlsanity.com/paper/2107.05451/) (2021)
- [Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data](https://mlsanity.com/paper/2208.03569/) (2022)

## Built on by (most cited first, 2 of 2)

- [Tractography-Driven Synthetic Data Generation for Fiber Bundle Segmentation in Tracer Histology](https://mlsanity.com/paper/2606.26898/) (2026)
- [Topology-Aware Training and Spatial Diagnostics for Fiber Bundle Segmentation in Tracer Histology](https://mlsanity.com/paper/2609.04454/) (2026)

## Abstract (arXiv)

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.