Topological Data Analysis of Vasculature

This repository is NOT maintained. A current version is maintained by Dr Bernadette J. Stolz here.

This software package was used to quantity tumour vasculature in mesoscopic photoacoustic imaging here. The package was originally written by Dr Bernadette J. Stolz and applied by Dr Paul W. Sweeney to analyse 3D images of tumour vasculature obtained using raster-scanning optoacoustic mesoscopy (RSOM).

In summary, the package is split into four parts:

1. Data Preprocessing - segmented tiff stacks are initially converted to a .nii format.
2. Data Extraction - segmentations are skeletonised.
3. Data Analysis - calculates vascular descriptors.
4. Void Analysis - computes and analyses voids.

TDA_main_script.m acts as a wrapper to perform the analysis detailed in in Brown, Sweeney & Lefebvre et al. (2021). Note, (4) was not used.

References

To reference this repository please use the below citations.

Quantification of vascular networks in photoacoustic mesoscopy
Emma L. Brown, Thierry L. Lefebvre, Paul W. Sweeney et al.

Multiscale Topology Characterises Dynamic Tumour Vascular Networks
Bernadette J, Stolz et al.

Prerequisites

The following softwares are the minimal requirements:

• Matlab 2021b.
• Python 3.6.
• NetworkX 2.4.
• Tensorflow 2.3.1
• GUDHI 3.2.0

A package list for a Python environment has been provided and can be installed using the method described below.

Installation

The 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/Vascular-TDA.git

The 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

This also contains the Spyder IDE to run the Python script.