Python script to download ABIDE preprocessed ROI time series files using the AWS CLI. Download the phenotypic data file as well. Click here to download the phenotypic CSV file
Right-click and choose “Save As” if it opens as text.
pip install pandas tqdm wget
python download_abide.py
--pheno_csv ./data/Phenotypic_V1_0b_preprocessed1.csv
--out_dir ./abide_data/cpac_cc200
--pipe cpac
--roi cc200
--fg filt_noglobal
Python script to generate Functional Connectivity Networks (FCNs) from ABIDE preprocessed ROI time series .1D files using Nilearn.
pip install pandas tqdm nilearn numpy
python generate_fcn.py
--ts_dir ./Abide/abide_dparsf_cc200
--pheno_csv ./Abide/Phenotypic_V1_0b_preprocessed1.csv
--out_dir ./Abide/outputs/dparsf
--roi cc200
--method correlation
To install dependencies for MRI preprocessing, use one of the following:
pip install -r requirements_mri_preproc.txt conda create -n mri_env --file requirements_mri_preproc.txt
Run in Kaggle https://www.kaggle.com/competitions/rsna-intracranial-aneurysm-detection to zip and download a specific DICOM series folder along with train.csv metadata. Outputs: {SeriesInstanceUID}.zip and train.csv with download links.
Inspects raw DICOM MRI data for one patient, extracts metadata, orientation, voxel spacing, calculates isotropy/anisotropy and visualizes a slice. Prepares data for downstream NIfTI conversion or deep learning tasks in the RSNA aneurysm challenge.