Lesion Correspondence TBP 3D

This code-base implements the method presented in the paper Skin Lesion Correspondence Localization in Total Body Photography published in MICCAI 2023. Given a set of lesions of interest in a soruce textured 3D mesh, we would like to localize lesion correspondence in a target textured 3D mesh.

Installation

• conda: conda-forge channel
• Essential: trimesh, vtk, pyqt5, pyvista, pyvistaqt, yaml, potpourri3d
• Preprocessing: pymeshlab, pandas
• submodule: skin3d, ECHODescriptors

conda create --name tbp_lesion_corr -c conda-forge python=3 pyvista pyvistaqt trimesh pyqt
conda activate tbp_lesion_corr
pip install potpourri3d pymeshlab pandas yaml

Please compile ECHODescriptors and modify the path under echo/bin in params.yml to the path of the GetDescriptor exe/bin file.

Data

The textured 3D human mesh model can be downloaded: 3DBodyTex.v1. The original skin lesion annotation on the 3DBodyTex data for longitudinal tracking can be found in skin3d.

NOTE: We use the high resolution meshes in the 3DBodyTex dataset.

The directory structure for each subject should follow the hierarchy below. Please refer to Preprocess data. Annotation of landmarks and lesion of interest after pre-processing can be found here.

${Subject}
|-- echo_descriptors_tau_x.xxxxx_h_x (optional, will be generated during run-time if missing)
|   |-- vert_xxxxx.txt
|   `-- ...
|-- echo_descriptors_tau_y.yyyyy_h_y (optional, will be generated during run-time if missing)
|   |-- vert_xxxxx.txt
|   `-- ...
|-- echo_descriptors_tau_z.zzzzz_h_z (optional, will be generated during run-time if missing)
|   |-- vert_xxxxx.txt
|   `-- ...
|-- real_scale_in_mm.ply
|-- model_highres_0_normalized.png
|-- texture_signal_per_vertex.txt
|-- landmarks.txt
`-- lesion_of_interest.txt

Preprocess data

In script/

• Transform data: (convert to mm scale)

python transform_3dbodytex.py -i path_to_3dbodytex_data

• Get scalar per vertex texture signal:

python get_color_per_vertex.py -i path_to_output_from_previous_step

Usage

Run the proposed algorithm

• Localize lesion correspondence:

python iterative_localization_alg.py -s path_to_source_folder -t path_to_target_folder -o path_to_output_folder

The localized lesion correspondence in each iteration is saved in the pkl file (one pkl file per iteration), with a final iteration to complete the correspondence of the remaining LOIs using weighted average of geometric score and texture score.

Visualization

In script/

• Visualize landmarks and LOIs:

python visualize_landmarks_and_lois.py -s path_to_source_folder -t path_to_target_folder

• Visualize correspondence:

python visualize_correspondence.py -s path_to_source_folder -t path_to_target_folder -r path_to_output_pkl_file

Acknowledgement

The repo is built on the excellent work and dataset: Skin3D, potpourri3d, ECHODescriptors, and 3DBodyTex.v1. Thanks for these great projects.