Generalised Automatic Anatomy Finder (GAAF): A general framework for 3D location-finding in CT scans
This work addresses the need for automated anatomy localization in medical imaging, but it appears incremental as it builds on existing CNN methods for a specific domain.
The paper tackles the problem of identifying generic anatomical locations in 3D CT scans by presenting GAAF, an end-to-end framework with a custom CNN, and reports accurate and robust localization performance in head and neck applications.
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.