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ClinicaDL

Framework for the reproducible classification of Alzheimer's disease using deep learning

Documentation | Tutorial | Forum | See also: AD-ML, Clinica

About the project

This repository hosts the source code of a framework for the reproducible evaluation of deep learning classification experiments using anatomical MRI data for the computer-aided diagnosis of Alzheimer's disease (AD). This work has been published in Medical Image Analysis and is also available on arXiv.

Automatic classification of AD using classical machine learning approaches can be performed using the framework available here: https://github.com/aramis-lab/AD-ML.

Disclaimer: this software is under development. Some features can change between different commits. A stable version is planned to be released soon. The release v.0.0.1 corresponds to the date of submission of the publication but in the meantime important changes are being done to facilitate the use of the package.

The complete documentation of the project can be found on this page. If you find a problem when using it or if you want to provide us feedback, please open an issue or write on the forum.

Getting started

ClinicaDL currently supports macOS and Linux.

We recommend to use conda or virtualenv for the installation of ClinicaDL as it guarantees the correct management of libraries depending on common packages:

conda create --name ClinicaDL python=3.7
conda activate ClinicaDL
pip install clinicadl

:warning: NEW!: :warning:

:reminder_ribbon: Visit our hands-on tutorial web site to start using ClinicaDL directly in a Google Colab instance!

Overview

How to use ClinicaDL?

clinicadl is an utility that is used through the command line. Several tasks can be performed:

• Preparation of your imaging data

  • T1w-weighted MR image preprocessing. The preprocessing task processes a dataset of T1 images stored in BIDS format and prepares to extract the tensors (see paper for details on the preprocessing). Output is stored using the CAPS hierarchy.
  • Quality check of preprocessed data. The quality_check task uses a pretrained network (Fonov et al, 2018) to classify adequately registered images.
  • Tensor extraction from preprocessed data. The extract task allows to create files in PyTorch format (.pt) with different options: the complete MRI, 2D slices and/or 3D patches. This files are also stored in the CAPS hierarchy.

• Train & test your classifier

  • Train neural networks. The train task is designed to perform training of CNN models using different kind of inputs, e.g., a full MRI (3D-image), patches from a MRI (3D-patch), specific regions of a MRI (ROI-based) or slices extracted from the MRI (2D-slices). Parameters used during the training are configurable. This task allow also to train autoencoders.
  • MRI classification. The classify task uses previously trained models to perform the inference of a particular or a set of MRI.

• Utilitaries used for the preparation of imaging data and/or training your classifier

  • Process TSV files. tsvtool includes many functions to get labels from BIDS, perform k-fold or single splits, produce demographic analysis of extracted labels and reproduce the restrictions made on AIBL and OASIS in the original paper.
  • Generate a synthetic dataset. The generate task is useful to obtain synthetic datasets frequently used in functional tests.

Pretrained models

Some of the pretained models for the CNN networks described in (Wen et al., 2020) are available on Zenodo: https://zenodo.org/record/3491003

Updated versions of the models will be published soon.

Related Repositories

• Clinica: Software platform for clinical neuroimaging studies
• AD-ML: Framework for the reproducible classification of Alzheimer's disease using machine learning