MRI-Segmentation

Author: Antoine DELPLACE
Last update: 17/01/2020

This repository corresponds to the source code used for the MRI segmentation part of my Master Thesis entitled "Segmentation and Generation of Magnetic Resonance Images by Deep Neural Networks".

Method description

The aim of the project is to achieve state-of-the-art performance in segmenting knee Magnetic Resonance Images (MRIs) thanks to a Neural Network architecture called U-net.

Usage

Dependencies

• Python 3.6.8
• Tensorflow 1.14
• Keras 2.2.4
• Numpy 1.16.2
• Pandas 0.24.2
• Matplotlib 3.0.3
• Scikit-image 0.15.0
• Scikit-learn 0.20.3

File description

1. main_unet.py is the main file dedicated to training the model, saving the weights and plotting a comparison between the ground truth and the generated segmentation.

2. test_boxplots.py is the post-processing program responsible for the statistical analysis and the generation of boxplots.

Results

The model demonstrates state-of-the-art performance in segmenting bones and cartilages of knee MRIs. The hyperparameter tuning, the visual outputs and the qualitative results can be found in my Master thesis.

References

1. A. Delplace. "Segmentation and Generation of Magnetic Resonance Images by Deep Neural Networks", Master thesis at the University of Queensland, October 2019. arXiv:2001.05447