This Python package utilised a 3-dimensional convolution neural network (CNN) to perform segmentation of 3D images using Keras. The 3D CNN is based on the U-Net architecture but extended for volumetric delineation with 3D spatial convolutions.
Hyperparameters can be tuned using Talos which integrates with Keras. This automates and performs a quantitative evaluation of the CNN during training.
To-date, this script has been used to train a 3D CNN which predicts a tumour region-of-interest from 3D raster-scan optoacoustic mesoscopy (RSOM) images, found here.
To reference this repository please use the below citation.
Quantification of vascular networks in photoacoustic mesoscopy
Emma L. Brown, Thierry L. Lefebvre, Paul W. Sweeney et al.
The 3D CNN was trained using Keras using the following package versions:
- Python 3.6.
- Keras 2.3.1.
- Tensorflow-GPU 1.14.0.
A package list for a Python environment has been provided and can be installed using the method described below.
The ROI package is compatible with Python3, and has been tested on Ubuntu 18.04 LTS. Other distributions of Linux, macOS, Windows should work as well.
To install the package from source, download zip file on GitHub page or run the following in a terminal:
git clone https://github.com/psweens/Predict-RSOM-ROI.gitThe required Python packages can be found here. The package list can be installed, for example, using creating a Conda environment by running:
conda create --name <env> --file REQUIREMENTS.txt