论文标题
广义自动解剖器查找器（GAAF）：CT扫描中3D位置调查的一般框架
Generalised Automatic Anatomy Finder (GAAF): A general framework for 3D location-finding in CT scans
论文作者
论文摘要
我们提出了GAAF（一种广义自动解剖器发现器），用于鉴定3D CT扫描中的通用解剖位置。 GAAF是一条端到端管道，具有用于数据预处理，模型培训和推理的专用模块。 GAAF在其核心上使用了自定义卷积神经网络（CNN）。 CNN型号很小，轻巧，可以调整以适合特定应用。到目前为止，GAAF框架已经在头部和颈部进行了测试，并且能够找到解剖位置，例如脑干的质量。 GAAF在开放式数据集中进行了评估，并且能够准确稳健地定位性能。我们所有的代码都是开源的，可在https://github.com/rrr-uom-projects/gaaf上找到。
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.
