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.
翻译:我们介绍了通用自动解剖发现器GAAF,这是用于识别3DCT扫描中一般解剖地点的通用自动解剖发现器,GAAF是端到端管道,专门设有数据处理前处理、模型培训和推断模块,在其核心部分,GAF使用一种本地化神经网络(CNN),CNN模型小,轻量,可以调整以适应特定应用。GAAF框架迄今已在头部和颈部进行测试,并能够找到诸如脑细胞中心等解剖地点。GAAF在开放的数据集中进行了评估,能够准确和稳健的本地化性能。我们的所有代码都是开源,可在https://github.com/r-uom-projects/GAAF中查阅。