BraDiPho: Brain Dissection Photogrammetry

BraDiPho is an innovative framework for exploring human brain anatomy by integrating ex-vivo microdissection and in-vivo tractography data. It leverages photogrammetry to provide detailed 3D models of brain dissections, enabling interactive and educational insights into white matter structures.

Data source and documentation

BraDiPho provides tools, models, and tutorials for integration and exploration of brain anatomy data. Learn more on the official website:

• BraDiPho Website
• BraDiPho OHBM Talk 2023
• BraDiPhoPy Repository
• Registration Files on Zenodo

Running BraDiPho with Docker or Singularity

The most efficient way to use this pipeline is through Docker or Singularity, ensuring all dependencies are managed and isolated.

Building the Docker or Singularity image

To build the required container, use the following commands:

Docker:

docker build . -t "bradipho" --rm --no-cache

Singularity:

singularity build bradipho.sif docker-daemon://bradipho:latest

Example command to launch the pipeline

Singularity Execution:

singularity exec bradipho.sif bash /bdp_registration_utils/launch_registration.sh ${PATH_TO_INPUT_DATA}/ sub-01 ${PATH_TO_OUTPUT_DATA}
# Unless from the internal team or reprocessing data, this is the only command you should use

• sub-01 is an example subject identifier from BraDiPho; adjust as needed. (sub-01, sub-02, sub-04, sub-10, sub-16, sub-17, sub-18, sub-19)
• Replace ${PATH_TO_INPUT_DATA} with the path to your input data directory. Your folder can contains Nifti (.nii, .nii.gz), tractography (.trk, .tck), and surfaces (.vtk, .ply)

  input/
  ├── atlas.nii.gz (labels, uint16)
  ├── laf_m.tck
  ├── lh.ply
  ├── lpt_m.tck
  ├── raf_m.trk
  ├── rh.ply
  ├── rpt_m.trk
  ├── t1_mask.nii.gz (mask, uint8)
  ├── t1.nii.gz (image, float32)
  └── wm.nii (mask, uint8)
  

• You should have at least one 'image' that can be used for registration (T1w, FA, etc.) and this image should be skull-stripped.