Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep learning-based analysis of multi-parametric magnetic resonance imaging

This github repository contains the Python implementatino of the following paper submission: Kwak, S. et al., "Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep learning-based analysis of multi-parametric magnetic resonance imaging" Journal of Medical Imaging.

How to use

For this version of code, Python 3.9.7 or above is required. Required python packages can be installed by using requirements.txt. Once can be installed by running

pip install -r requirements.txt

Requirements

Code is assumed user has access to MRI scans (FLAIR, T1, T1CE, T2, and ADC), ROIs (Far, and Near), and brain tumor segmentation. Please contact [email protected] for gaining access.

Usage

Please run code in following order:

1. Infiltrative_ROI/: Near_far_selection.ipynb -> Near_far_patchMaking.ipynb -> Training_NearFar.ipynb -> Testing_nearFar_byInst.ipynb -> Post_processing.ipynb
2. Noninfiltrative_ROI/: Create_Non_infiltrative_ROI.ipynb
3. Evaluation map/: Create_eval_map.ipynb
4. Main/: Training.ipynb -> Testing_InfiltrativeNoninfiltrative.ipynb -> Post_processing.ipynb

Make sure to put appropriate input and output directory as suggested.