PyALFE

Python implementation of Automated Lesion and Feature Extraction (ALFE) pipeline.

Requirements

PyALFE supports Linux x86-64, Mac x86-64, and Mac arm64 and requires python >= 3.9.

Image registration and processing

PyALFE can be configured to use either Greedy or AntsPy registration tools. Similarly, PyALFE can can be configured to use Convert3D or python native library Nilearn for image processing tasks. To use Greedy and Convert3d, these command line tools should be downloaded using the download command.

Installation

Clone the repo

git clone git@git.ucsf.edu:rauschecker-sugrue-labs/pyalfe.git
cd pyalfe

Then run (we recommend using a python virtual environment)

pip install --upgrade pip

You can either instal pyalfe in development mode or build and install.

Option 1: Development mode installation

First update the setuptools

pip install --upgrade setuptools

Run the following command in the parent pyalfe directory:

pip install -e .

Option 2: Build and install

First update the build tool

pip install --upgrade build

Run the following commands in the parent pyalfe directory to build the whl file and install pyalfe

python -m build
pip install dist/pyalfe-0.0.1-py3-none-any.whl

Download models

To download deep learning models, run

pyalfe download models

Pyradiomics support

To install pyalfe with pyradiomics support, run

pip install -e  '.[radiomics]'

for development installation or

pip install 'dist/pyalfe-0.0.1-py3-none-any.whl[radiomics]'

when performing a build and install.

Usage

Configuration

To configrue the PyALFE pipeline you should run:

pyalfe configure

which prompt the you to enter the following required configurations:

Classified directory

Enter classified image directory: /path/to/my_mri_data

The classified directory (classified_dir) is the input directory to PyALFE and should be organized by accessions (or session ids). Inside the directory for each accession there should be a directory for each available modality. Here is an example:

my_mri_data  
│
│───12345
│   │
│   │───T1
│   │   └── T1.nii.gz
│   │───T1Post
│   │   └── T1Post.nii.gz
│   │───FLAIR
│   │   └── FLAIR.nii.gz  
│   │───ADC
│   │   └── ADC.nii.gz 
│   └───T2
│       └── T2.nii.gz
│
└───12356
.   │
.   │───T1
.   │   └── T1.nii.gz
    │───T1Post
    │   └── T1Post.nii.gz
    │───FLAIR
    │   └── FLAIR.nii.gz  
    │───ADC
    │   └── ADC.nii.gz 
    └───T2
        └── T2.nii.gz

To use this directory the user should provide path/to/my_mri_data as the classified directory. This config value can be overwritten when calling pyalfe run via -cd or --classified-dir option.

Processed directory

Enter classified image directory: /path/to/processed_data_dir

The processed image directory (processed_dir) is where ALFE writes all its output to. It can be any valid path in filesystem that user have write access to. This config value can be overwritten when calling pyalfe run via -pd or --processed-dir option.

Modalities

Enter modalities separated by comma [T1,T1Post,FLAIR,T2,ADC]: T1,T1Post,ADC

All the modalities that should be processed by ALFE. Modalities should be separated by comma. To use the default value of T1,T1Post,T2,FLAIR,ADC, simply press enter. This config value can be overwritten when calling pyalfe run via -m or --modalities option.

Target modalities

Enter target modalities separated by comma [T1Post,FLAIR]:

The target modalities are used to define the abnormalities which are then used to extract features. Currently, only T1Post, FLAIR, or both (default) can be target modality. This config value can be overwritten when calling pyalfe run via -t or --targets option.

Dominant Tissue

Enter the dominant tissue for the lesions (white_matter, gray_matter, auto) [white_matter]:

The dominant tissue where the tumor or lesion is expected to be located at. This information is use in relative signal feature calculations. If you choose auto, pyalfe automatically detect the dominant tissue after segmentation. This config value can be overwritten when calling pyalfe run via -dt or --dominant_tissue option.

Image processor

image processor to use (c3d, nilearn) [c3d]:

Currently, pyalfe can be configures to use either Convert3D (a.k.a. c3d) or Nilearn for image processing tasks. The default is Convert3d aka c3d. In other to use c3d, you have to download it using the download command. To use Nilearn, you do not need to run any extra command since it is already installed when you install pyalfe. This config value can be overwritten when calling pyalfe run via -ip or --image_processing option.

Image Registration

image registration to use (greedy, ants) [greedy]:

Currently, pyalfe can be configures to use either greedy or ants for image registration tasks. The default is greedy. In other to use greedy, you have to download it using the download command. To use ants, install pyalfe with ants support pip install pyalfe[ants]. This config value can be overwritten when calling pyalfe run via -ir or --image-registration option.

Running the pipeline

To run PyALFE for an accession

pyalfe run ACCESSION

If you chose to save the configuration file in a non-standard location you can run

pyalfe run -c path/to/conf.ini ACCESSION

In general, all the config option can be overwritten by command line options. To see a list of command line options, run:

pyalfe run --help

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

BSD 3-Clause

stefanv/pyalfe