alzheimer_disease_classification_tools

This project contains the code to analyze and classify MRI scans to predict the Alzheimer's disease and Mild Cognitive Impairment (MCI) progression. It uses two datasets: ADNI and BIOCARD (see below: Scans preparation). It uses 3D convolutional neural networks (CNN) to classify the scans. Also, it uses XGBoost and SVM to classify volumetric features extracted from the scans using FreeSurfer. Repository contains the whole process - from the scans preparation to the classification and the result analysis.

Repository consists of the following folders, each containing the scripts to perform steps of the analysis:

• scans_preparation - scripts to prepare the scans for CNN training and testing (see below: Scans preparation)
• data_analysis_preparation - scripts to prepare and analyze the data (see below: Data analysis and preparation)
• tabular_models - scripts to train and test the tabular models using extracted volumetric data (see below: Tabular models)
• imaging_models - scripts to train and test the CNN models (see below: CNN models)

Data acquisition

To reproduce the results, you need to download the ADNI and BIOCARD datasets. The ADNI dataset is available at http://adni.loni.usc.edu/. The BIOCARD dataset is available at https://www.biocard-se.org. Both datasets are not publicly available and require a registration and a data use agreement. See docstring in the data_analysis_preparation and scans_preparation directories for more details (which files to download and where to put them).

Scans preparation

This folder contains the scripts used to prepare the scans for CNN training and testing. After the scans are downloaded, they need to be preprocessed. The preprocessing includes the following steps:

• Skull stripping with HD-BET (https://github.com/MIC-DKFZ/HD-BET), (https://doi.org/10.1002/hbm.24750)
• Bias field correction with N4ITK using ANTs (https://pubmed.ncbi.nlm.nih.gov/20378467/), (http://stnava.github.io/ANTs/)
• Registration to MNI152 template with FLIRT (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT)
• Denoising with median filter
• Intensity rescaling
• Histogram equalization

This preprocessing is performed with the run_adni_scans_preprocessing.py and run_biocard_scans_preprocessing.py scripts. For ADNI dataset, the preprocessing is performed on any subset of study, depending on found scans.

You need to perform this step first, before running any other scripts. This is very time-consuming step.

Data analysis and preparation

This folder contains the scripts used to prepare and analyze the data. The data is prepared for the tabular models and for the CNN models. It contains statistical analysis of the data (in notebooks) and the data visualization. To perform next steps, you need to run adni_train_val_test_split.py and biocard_generate_test_set.py scripts. These scripts generate the train, validation and test sets for the tabular models and the CNN models as .csv files.

Tabular models

The tabular models use the volumetric data (only on the ADNI dataset). The volumetric data extracted with FreeSurfer are provided by ADNI. ADNI_tabular_models.ipynb notebook contains the code to train and test the XGBoost and SVM models. This notebook also contains the code to extract the most important features from the models to explain the predictions.

This step is optional.

CNN models

The CNN models uses the preprocessed scans. The CNN models are trained and tested with the imaging_models/CNN_training.ipynb notebook. Additional evaluation of the models is performed with the scans from the BIOCARD dataset. The BIOCARD dataset is not used for training, only for testing, as independent test set. It uses my implementation of the Pseudo-3D CNN models (https://github.com/damianpolakpjwstk/pseudo3d_pytorch) with attention blocks. See the notebook, imaging_models/model.py and README in pseudo3d_pytorch repository for more details.

There is also a notebook called imaging_models/CNN_pretrain.ipynb. It is used to pretrain the CNN models on the IXI dataset (https://brain-development.org/ixi-dataset/). This step is optional.

Another notebook called imaging_models/architecture_visualization.ipynb is used to visualize the CNN models architecture with torchviz.

TODO: add explanation of the CNN models with Grad-CAM visualization.

Installation

Repository uses Python 3.10 and Pytorch 2.0.1. It was tested on Ubuntu 20.04.

To use the code, you need to install the following tools:

• FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation)
• ANTs (http://stnava.github.io/ANTs/)
• HD-BET (https://github.com/MIC-DKFZ/HD-BET)
• Python packages from requirements.txt file
• Download submodules with git submodule update --init --recursive
• Install pseudo3d_pytorch requirements

After installing the tools, you need to set the paths to the tools in scans_preparation/configuration.py file.