Label propagation using deep registration
Project description
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
Project details
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