Image segmentation and accuracy prediction via Multi-atlas segmentation (MAS) and Reverse classification accuracy (RCA)

This repository contains an extended version of the source code corresponding to the paper "Segmentación multi-atlas de imágenes médicas con selección de atlas inteligente y control de calidad automático" (La Plata, 2018). You can check out our paper here: http://sedici.unlp.edu.ar/handle/10915/73180.

Description

The key features of the project are as follows:

• Atlas selection by image similarity. Available image measures are: Mean absolute error (MAE), Mean squared error (MSE), Normalized cross correlation (NCC) and Mutual information (MI).
• Deformable image registration (with affine initialization) via SimpleElastix.
• Two label fusion techniques: Voting and STAPLE.
• Quality evaluation for predicted segmentations via RCA.

Instructions

This project uses Python 3.8.10.

Project environment:

1. Create and activate virtual environment: 1) python3 -m venv env 2) source env/bin/activate
2. Install required packages: pip install -r requirements.txt
3. Install project modules (src): pip install -e .
4. Install SimpleElastix toolbox following this guide.

Simulations:

• Multi-atlas: ./01_run_multiatlas.sh
• RCA: ./02_run_rca.sh

Reference

• Mansilla, L., & Ferrante, E. (2018). Segmentación multi-atlas de imágenes médicas con selección de atlas inteligente y control de calidad automático. In XXIV Congreso Argentino de Ciencias de la Computación (La Plata, 2018).

License

MIT