Anatomical Fiducials (AFIDs) Data Processing Pipeline

AIMS Lab Research Team at the Robarts Research Institute - 2023-2024

This package is under active development. It should be stable and reproducible, but please let any of the active contributing members know if there are any bugs or unusual behaviour.

This Python package is a data processing pipeline based on Snakemake and SnakeBIDS workflow management tools to prepare data for a Convolutional Neural Network (CNN) deep learning model afids-CNN. Since afids-CNN is under active development, this package contains tunable parameters that are not normally exposed in a data processing pipeline; the user is highly encourage to read docstrings and get familiar with the relevant workflow managements tools prior to using this software. Likewise, there may be frequent updates to this package as the project matures (see the changelog for more details).

Brief Overview of the Pipeline

1. Performs rigid registration (i.e., 6 d.o.f) from MNI template to Native space.
2. Conformes images to a desired isotropic resolution (default: 1mm).
3. Executes image intensity normalization by various supported algorithms (default: min-max normalization).

Table of Contents

1. Installation
2. Building the Docker image
3. Getting the datasets
4. Quick Guide
5. Known issues
6. Roadmap
7. Questions, Issues, Suggestions, and Other Feedback

Installation

⚠️ Warning: Local installation on Apple M1 is currently not supported. You can still run the pipeline by building a docker container, but it'll be an emulated amd64 container and fairly slow.

Installing Poetry

We use poetry tool for dependency management and to package the python project. You can find step by step instructions on how to install it by visiting it's official website.

Local Installation

After installing poetry, clone this repository via:

git clone https://github.com/afids/autoafids_prep.git

You can then install the python package using one of the following commands, which should be executed within the repository folder (i.e., autoafids_prep/).

To install the autoafids_prep package "normally", use:

poetry install

If you want to install in develop mode, use:

poetry install -e

Getting the datasets

All the datasets mentioned below have been deposited at DOI-issuing repositories separately and follow the BIDS directory hierarchy. To download them, follow the links:

1. 100 Unrelated Human Connectome Project (AFIDs-HCP) - You'll need to be authenticated before you can download this dataset. Please contact Alaa Taha for the required access code.
2. Open Access Series of Imaging Studies (AFIDs-OASIS)
3. The Stereotactic Neurosurgery (SNSX) dataset
4. The London Health Sciences Center Parkinson’s disease (LHSCPD) dataset

Quick Guide

To display help information about the autoafids_prep program, use:

autoafids_prep -h

To execute a dry-run of the workflow, use:

autoafids_prep path/to/dataset path/to/dataset/derivatives participant --cores 1 -np

Known Issues

• Slow and medium flags in the pipeline are under development and therefore do not work.

Roadmap

Here are some future plans for autoafids_prep:

• Synchronize the pipeline with afids-CNN to avoid latency between downloading the datasets and preparing them for training the model.

Questions, Issues, Suggestions, and Other Feedback

Please reach out if you have any questions, suggestions, or other feedback related to this software—either through email (dbansal7@uwo.ca) or the discussions page. Larger issues or feature requests can be posted and tracked via the issues page. Finally, you can also reach out to Alaa Taha, the Science Lead for autoafids_prep.