Generalised Automatic Anatomy Finder (GAAF): A general framework for 3D location-finding in CT scans

TL;DR

GAAF is a versatile, lightweight framework utilizing CNNs for accurate 3D anatomical location detection in CT scans, demonstrated on head and neck datasets.

Contribution

Introduces GAAF, a flexible, end-to-end framework with customizable CNN modules for anatomical localization in 3D CT scans.

Findings

Effective in head and neck scans

Capable of locating brainstem center-of-mass

Open-source implementation available

Abstract

We present GAAF, a Generalised Automatic Anatomy Finder, for the identification of generic anatomical locations in 3D CT scans. GAAF is an end-to-end pipeline, with dedicated modules for data pre-processing, model training, and inference. At it's core, GAAF uses a custom a localisation convolutional neural network (CNN). The CNN model is small, lightweight and can be adjusted to suit the particular application. The GAAF framework has so far been tested in the head and neck, and is able to find anatomical locations such as the centre-of-mass of the brainstem. GAAF was evaluated in an open-access dataset and is capable of accurate and robust localisation performance. All our code is open source and available at https://github.com/rrr-uom-projects/GAAF.