Tractography-Driven Synthetic Data Generation for Fiber Bundle Segmentation in Tracer Histology

2026-06-25

AI Summary

Manual annotation of fiber tracts in histological images of non-human primates is prohibitively costly, severely limiting the validation efficiency of diffusion MRI (dMRI) tractography. To address this challenge, this work proposes a novel approach that leverages ex vivo dMRI tractography results as a generative prior to synthesize 2D image patches with realistic foreground textures, which are then composited onto postmortem blockface photographs. Domain randomization is further incorporated to enhance data diversity. A 2D U-Net model trained on a combination of real and synthetic data achieves state-of-the-art segmentation performance using only one-third of the manually annotated data required by conventional methods. This hybrid training strategy significantly outperforms models trained solely on real data and demonstrates superior generalization across brain regions and varying fiber densities.

Abstract

Diffusion MRI (dMRI) tractography enables non-invasive reconstruction of white-matter pathways, but its accuracy is fundamentally limited by indirect, low-resolution measurements of axonal organization. Tracer injection studies in non-human primates provide a gold standard for validating dMRI tractography. This, however, requires time-consuming manual annotation of fiber bundles in histology sections. We propose a synthetic-data augmented framework for automated fiber bundle segmentation in macaque tracer histology. Our approach uses ex vivo dMRI tractography as a generative prior to synthesize 2D image patches for training. This provides us with sufficiently realistic foreground texture, which we compose with backgrounds from blockface photos and diversify via domain randomization. A 2D U-Net is trained on mixed real and synthetic patches. Experiments on held-out brains demonstrate improved generalization across brains and fiber bundle densities compared to training with real data only. Training with synthetic data only leads to poor performance, underscoring the need for real supervision. Overall, our approach achieves performance comparable to the state-of-the-art while requiring 3x less manually annotated data.

Problem

• fiber bundle segmentation
• tracer histology
• diffusion MRI tractography
• synthetic data
• manual annotation

Innovation

• tractography-driven synthesis
• fiber bundle segmentation
• synthetic data augmentation
• domain randomization
• tracer histology