Title
TractFigure Studio: a multi-tool for tractography visualization
Leaders
Gabriella Taylor (@gabtaylor1)
Collaborators
No response
Project description
TractFigure Studio is a cross-platform Python application for loading, inspecting, rendering, and exporting publication-ready diffusion MRI tractography figures. At least, it will be by the end of BrainHack.
To date, there is no singular tractography visualization software that does it all - a veritable "multi-tool" capable of loading any tract file or tractography dataset, making high-quality figures exactly to the user's specifications, and storing all settings in a interpretable format to be used on multiple sets of tracts. We owe it to ourselves and to diffusion researchers everywhere to make that "multi-tool" a reality.
Contributors will build on a validated starter application, incorporating anatomical presets, advanced rendering options, registration controls, scalable tract management, and batch rendering features. This project has something for everyone: whether you come from a neuroscience, computer science, engineering, or UX background, you are bound to find a niche here!
Link to project repository/sources
https://github.com/gabtaylor1/tractfigure-studio/tree/main
Concerete goals with specific tasks for Brainhack Vanderbilt 2026
The starter code already provides installation, data fetching, automatic coordinate detection, and platform cross-integration, as well as independent visibility options, reset behavior, and save/export options. The objective for the hackathon is to build upon this basic foundation in several key areas, including:
- Neuro: make anatomically meaningful figure presets for users who do not want to configure every visual parameter themselves.
- Registration: turn the existing registration backend into a full UI registration workflow.
- Rendering: add customizable glass brain rendering, lighting effects, and color gradient settings.
- UI: build scalable layer cards, filtering/search features, and explicit global-versus-active-layer operations.
- API: add batch rendering options to build consistent figures across multiple files or datasets using a single scene "recipe".
Additionally, we propose the following advanced "stretch goals" to be completed only after meeting all of the above milestones:
- Add deterministic turntable/video export options
- Add a smooth nonlinear registration prototype
- Add a "universal adapter" for all major tractography filetypes.
Detailed descriptions and acceptance criteria for the above goals can be found in the attached onboarding document: PREHACKATHON_GUIDE.md
Good first issues
Issue 1: follow the instructions in PREHACKATHON_GUIDE.md from beginning to end. The guide covers all steps in the current project workflow from cloning the repository through submitting a pull request.
Beyond this point, issues are divided by area of focus. Your starting place will depend on your interests. Good first issues per area are:
- Neuro issue 2: define named anatomical presentation presets (e.g.: Orthographic, Four-view Clinical) and create HEX-value tract palettes for each.
- Registration issue 2: add new rigid and full-affine registration actions to the existing workflow. Rigid mode should add no scale or shear, and full affine mode should follow the moving-RASMM-to-fixed-RASMM direction.
- Rendering issue 2: implement a glass-brain surface rendering option using a binary mask of a diffusion or anatomical image.
- UI issue 2: build scalable layer cards for the current UI. Scenes containing many layers (70+) should remain navigable by the user.
- API issue 2: add a supported headless CLI/API that loads a scene "recipe", validates its data root, renders requested views, and returns exit codes.
Skills
- Python: basic to intermediate
This project is an excellent learning opportunity for beginners.
- Git: intermediate
We will use Git extensively throughout the hackathon. Knowledge of CI/CD preferred but not required.
- UNIX shell/bash: basic
You must be able to access and navigate a UNIX-like environment. If you don't have this skill, this is a good chance to learn!
- Prior experience with diffusion MRI processing preferred but not required.
Bonus points if you have any experience with tractography!
Onboarding documentation
https://github.com/gabtaylor1/tractfigure-studio/blob/main/PREHACKATHON_GUIDE.md
What will participants learn?
- Build confidence with Python by making visible improvements to a scientific application.
- Learn Git and GitHub collaboration through guided issues, branches, and code review.
- Explore diffusion MRI tractography by inspecting and visualizing white matter pathways from multiple datasets.
- Explore interactive 3D graphics in PyVista, VTK, and Trame.
- Develop user-interface features such as layer controls and visibility toggles.
- Work with medical-image coordinate systems and learn to perform visual quality control.
Public data to use
All files are obtained via DIPY's data.fetcher module: https://docs.dipy.org/stable/reference/dipy.data.html#module-dipy.data.fetcher
Number of collaborators
4+
Credit to collaborators
All project contributors will be listed in the README for the Git repository. If a version this project is submitted for publication to a conference or journal, each contributor will be offered co-authorship on the work (provided he/she/they can be contacted by the project leader).
Image
Project Summary
Introducing TractFigure Studio: a Python application for publication-ready tractography figures. Contributors will build on validated starter code, adding anatomical presets, registration controls, and more.
Type
visualization
Development status
1_basic structure
Topic
tractography, data_visualisation
Tools
DIPY, ANTs
Programming language
Python, unix_command_line
Modalities
DWI, MRI
Git skills
1_commit_push, 2_branches_PRs, 3_continuous_integration
Anything else?
No response
Things to do after the project is submitted and ready to review.
Title
TractFigure Studio: a multi-tool for tractography visualization
Leaders
Gabriella Taylor (@gabtaylor1)
Collaborators
No response
Project description
TractFigure Studio is a cross-platform Python application for loading, inspecting, rendering, and exporting publication-ready diffusion MRI tractography figures. At least, it will be by the end of BrainHack.
To date, there is no singular tractography visualization software that does it all - a veritable "multi-tool" capable of loading any tract file or tractography dataset, making high-quality figures exactly to the user's specifications, and storing all settings in a interpretable format to be used on multiple sets of tracts. We owe it to ourselves and to diffusion researchers everywhere to make that "multi-tool" a reality.
Contributors will build on a validated starter application, incorporating anatomical presets, advanced rendering options, registration controls, scalable tract management, and batch rendering features. This project has something for everyone: whether you come from a neuroscience, computer science, engineering, or UX background, you are bound to find a niche here!
Link to project repository/sources
https://github.com/gabtaylor1/tractfigure-studio/tree/main
Concerete goals with specific tasks for Brainhack Vanderbilt 2026
The starter code already provides installation, data fetching, automatic coordinate detection, and platform cross-integration, as well as independent visibility options, reset behavior, and save/export options. The objective for the hackathon is to build upon this basic foundation in several key areas, including:
Additionally, we propose the following advanced "stretch goals" to be completed only after meeting all of the above milestones:
Detailed descriptions and acceptance criteria for the above goals can be found in the attached onboarding document: PREHACKATHON_GUIDE.md
Good first issues
Issue 1: follow the instructions in PREHACKATHON_GUIDE.md from beginning to end. The guide covers all steps in the current project workflow from cloning the repository through submitting a pull request.
Beyond this point, issues are divided by area of focus. Your starting place will depend on your interests. Good first issues per area are:
Skills
This project is an excellent learning opportunity for beginners.
We will use Git extensively throughout the hackathon. Knowledge of CI/CD preferred but not required.
You must be able to access and navigate a UNIX-like environment. If you don't have this skill, this is a good chance to learn!
Bonus points if you have any experience with tractography!
Onboarding documentation
https://github.com/gabtaylor1/tractfigure-studio/blob/main/PREHACKATHON_GUIDE.md
What will participants learn?
Public data to use
All files are obtained via DIPY's
data.fetchermodule: https://docs.dipy.org/stable/reference/dipy.data.html#module-dipy.data.fetcherhttps://zenodo.org/records/3352379
https://figshare.com/articles/dataset/Advanced_Atlas_of_80_Bundles_in_MNI_space/7375883?file=13638644
License: Creative Commons Attribution 4.0 International
ICBM 152 nonlinear 2009a template, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University.
https://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009
Number of collaborators
4+
Credit to collaborators
All project contributors will be listed in the README for the Git repository. If a version this project is submitted for publication to a conference or journal, each contributor will be offered co-authorship on the work (provided he/she/they can be contacted by the project leader).
Image
Project Summary
Introducing TractFigure Studio: a Python application for publication-ready tractography figures. Contributors will build on validated starter code, adding anatomical presets, registration controls, and more.
Type
visualization
Development status
1_basic structure
Topic
tractography, data_visualisation
Tools
DIPY, ANTs
Programming language
Python, unix_command_line
Modalities
DWI, MRI
Git skills
1_commit_push, 2_branches_PRs, 3_continuous_integration
Anything else?
No response
Things to do after the project is submitted and ready to review.
Hi @brainhack-vandy/project-monitors my project is ready!