TractEdit: an open-source interactive tool for virtual dissection and manual refinement of diffusion MRI tractography

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Medical Image Analysis

Background:

• Accurate white matter pathway reconstruction is crucial for connectomics and pre-surgical planning.
• Tractography algorithms often produce false positives, requiring manual refinement for precise analysis.
• Existing tools face challenges with format compatibility and integrating 3D visualization with slice-based editing.

Purpose of the Study:

• To develop a lightweight, open-source tool for interactive cleaning and validation of tractography data.
• To address limitations of existing tools in format compatibility and editing functionality.
• To facilitate rigorous quality control of diffusion MRI tractographic data.

Main Methods:

• Developed TractEdit, a Python-based desktop application using VTK and FURY.
• Implemented a hybrid protocol combining 3D streamline selection with voxel-level ROI definitions.
• Supported multiple standard and next-generation file formats (.trk, .tck, .trx, .vtk, .vtp) with memory mapping for large datasets.

Main Results:

• TractEdit enables real-time filtering and manual bundle segmentation via point-and-click interface.
• Automated calculation of bundle analytics (centroids, medoids, TDI maps) and an ODF Tunnel View for fiber alignment verification.
• Export module for serializing validated bundles into interactive HTML5 files for browser-based visualization.

Conclusions:

• TractEdit bridges the gap between automated tractography and manual validation, enhancing data quality.
• Supports the TRX standard and integrates microstructural visualization for advanced connectivity analyses.
• Provides a versatile resource for neuroimaging researchers generating training data for machine learning models.