This project was developed during the 2019 Neural Network Course held by Prof. Uncini at Sapienza University of Rome.
It is inspired on Deep Regression Segmentation for Cardiac Bi-Ventricle MR Images paper by X. Du et al.
Since the right ventricles dataset is not open source, we adapted the project only on left ventricles (which is open source and is available in this repository).
The project contains only a Jupyter Notebook file. Meet the prerequisite and use it. Google Colaboratory is recommended.
You have two ways of meeting the prerequisites
-
First (recommended, online)
- Use Google Colaboratory
- Done.
-
Second (offline)
- Python3
- Jupyter
pip install jupyterlab
- Tensorflow (via conda or pip)
conda install tensorflow-gpu
pip install tensorflow-gpu
- OpenCV
pip install opencv-python
- Matplotlib
pip install matplotlib
- Numpy
pip install numpy
- Scipy
pip install scipy
- Scikit-image
pip install scikit-image
- Scikit-learn
pip install scikit-learn
- Shapely
pip install shapely
- OSGEO
pip install osgeo
In this section, some recap images of the project are presented.
Some qualitative examples are reported. For quantitative examples (Pearson's correlation coefficient, Dice Metric, Hausdorff distance) check the notebook.
This project is licensed under the MIT License - see the LICENSE.md file for details
- Billie Thompson - Provided README Template - PurpleBooth