TractSeg - Fast and accurate white matter tract segmentation

Abstract

The individual course of white matter fiber tracts is an important key for analysis of white matter characteristics in healthy and diseased brains. Uniquely, diffusion-weighted MRI tractography in combination with region-based or clustering-based selection of streamlines allows for the in-vivo delineation and analysis of anatomically well known tracts. This, however, currently requires complex, computationally intensive and tedious-to-set-up processing pipelines. TractSeg is a novel convolutional neural network-based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without requiring tractography, image registration or parcellation. We demonstrate in 105 subjects from the Human Connectome Project that the proposed approach is much faster than existing methods while providing unprecedented accuracy. The code and data are openly available at https://github.com/MIC-DKFZ/TractSeg/ and https://doi.org/10.5281/zenodo.1088277, respectively.

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Cited by in corpus (24)

• An overview of deep learning in medical imaging focusing on MRI
• Combined tract segmentation and orientation mapping for bundle-specific tractography
• Tractography and machine learning: Current state and open challenges
• Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions
• Filtering in tractography using autoencoders (FINTA)
• Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation
• DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks
• FIESTA: Autoencoders for accurate fiber segmentation in tractography
• Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
• Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review
• HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI
• Tract orientation mapping for bundle-specific tractography
• Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering
• Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architecture
• Exploring the robustness of TractOracle methods in RL-based tractography
• TractoEmbed: Modular Multi-level Embedding framework for white matter tract segmentation
• Two-step registration method boosts sensitivity in longitudinal fixel-based analyses
• Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation
• DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modeling
• Knowledge Transfer for Few-shot Segmentation of Novel White Matter Tracts
• Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
• Enhancing Fiber Orientation Distributions using convolutional Neural Networks
• Fast and Scalable Optimal Transport for Brain Tractograms
• Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation