TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View

TL;DR

TractCloud-FOV is a deep learning framework that improves tractography parcellation accuracy in diffusion MRI scans with incomplete fields of view by using a novel data augmentation strategy to simulate real-world truncation scenarios.

Contribution

We introduce FOV-Cut Augmentation, a novel training strategy that enhances model robustness to incomplete FOV in tractography parcellation tasks.

Findings

Outperforms state-of-the-art methods on synthetic and real datasets

Achieves higher classification accuracy and better generalization

Provides efficient and consistent parcellation in incomplete FOV conditions

Abstract

Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions are partially imaged, leading to partial or truncated fiber tracts. To address this challenge, we introduce TractCloud-FOV, a deep learning framework that robustly parcellates tractography under conditions of incomplete FOV. We propose a novel training strategy, FOV-Cut Augmentation (FOV-CA), in which we synthetically cut tractograms to simulate a spectrum of real-world inferior FOV cutoff scenarios. This data augmentation approach enriches the training set with realistic truncated streamlines, enabling the model to achieve superior generalization. We evaluate the proposed TractCloud-FOV on both synthetically cut tractography and two real-life datasets with incomplete FOV. TractCloud-FOV significantly outperforms several state-of-the-art methods on all testing datasets in terms of streamline classification accuracy, generalization ability, tract anatomical depiction, and computational efficiency. Overall, TractCloud-FOV achieves efficient and consistent tractography parcellation in diffusion MRI with incomplete FOV.