LabelProp - CLI and Server

Requirements

Python >= 3.8.*
Pytorch >=1.10

Installation

To install this project with CUDA 11.1 :

git clone https://github.com/nathandecaux/labelprop
cd labelprop
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -e .

Usage

CLI

Basic operations can be done using the command-line interface provided in labelprop.py at the root of the project.

Pretraining

$ labelprop pretrain --help
Usage: labelprop.py pretrain [OPTIONS] IMG_LIST

Pretrain the model on a list of images. The images are assumed to be
  greyscale nifti files. IMG_LIST is a text file containing line-separated
  paths to the images.

Options:
  -s, --shape INTEGER         Image size (default: 256)
  -z, --z_axis INTEGER        Axis along which to propagate (default: 2)
  -o, --output_dir DIRECTORY  Output directory for checkpoint
  -n, --name TEXT             Checkpoint name (default : datetime)
  -e, --max_epochs INTEGER

Training

$ labelprop train --help
Usage: labelprop.py train [OPTIONS] IMG_PATH MASK_PATH

Train a model and save the checkpoint and predicted masks. IMG_PATH is a
  greyscale nifti (.nii.gz or .nii) image, while MASKPATH is it related sparse
  segmentation.

Options:
  -s, --shape INTEGER         Image size (default: 256)
  -c, --pretrained_ckpt FILE  Path to the pretrained checkpoint (.ckpt)
  -e, --max_epochs INTEGER
  -z, --z_axis INTEGER        Axis along which to propagate (default: 2)
  -o, --output_dir DIRECTORY  Output directory for checkpoint and predicted
                              masks
  -n, --name TEXT             Prefix for the output files (checkpoint and
                              masks)

Propagating (inference)

$ labelprop propagate --help
Usage: labelprop.py propagate [OPTIONS] IMG_PATH MASK_PATH CHECKPOINT

Propagate labels from sparse segmentation.  IMG_PATH is a greyscale nifti
  (.nii.gz or .nii) image, while MASKPATH is it related sparse segmentation.
  CHECKPOINT is the path to the checkpoint (.ckpt) file.

Options:
  -s, --shape INTEGER         Image size (default: 256)
  -z, --z_axis INTEGER        Axis along which to propagate (default: 2)
  -l, --label INTEGER         Label to propagate (default: 0 = all)
  -o, --output_dir DIRECTORY  Output directory for predicted masks (up, down
                              and fused)
  -n, --name TEXT             Prefix for the output files (masks)

GUI

See this repo

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118