Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data

TL;DR

This paper introduces a semi-supervised, anatomy-constrained deep learning method with temporal ensembling for accurate fiber bundle detection in anatomic tracing data, addressing challenges like noise and distortions.

Contribution

It presents a novel self-supervised, anatomy-aware segmentation approach with semi-supervised training for fiber bundle detection in brain tracing data.

Findings

Achieved ~0.90 true positive rate on unseen macaque brain sections.

Effectively utilized unlabeled data to enhance segmentation performance.

Reduced false positives through location constraints.

Abstract

Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detection of fiber bundles on tracing data is challenging due to sectioning distortions, presence of noise and artifacts and intensity/contrast variations. In this work, we propose a deep learning method with a self-supervised loss function that takes anatomy-based constraints into account for accurate segmentation of fiber bundles on the tracer sections from macaque brains. Also, given the limited availability of manual labels, we use a semi-supervised training technique for efficiently using unlabeled data to improve the performance, and location constraints for further reduction of false positives. Evaluation of our method on unseen sections from a different macaque yields promising results with a true positive rate of ~0.90. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.