Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks - Zhan, Qianyi; Liu, Yuanyuan; Liu, Yuan; Hu, Wei (Frontiers in Neuroscience, 2021)
18F-FDG PET imaging of brain glucose use and of amyloid accumulation is a research criteria for Alzheimer's Disease (AD) diagnosis. Several PET studies have shown widespread metabolic deficits in the frontal cortex for AD patients. Therefore studying frontal cortex changes is of great importance for AD research. This paper aims to segment frontal cortex from brain PET imaging using deep neural networks. The learning framework called FSPET is proposed to tackle this problem. It combines the anatomical prior of frontal cortex into the segmentation model which based on conditional generative adversarial network (cGAN) and convolutional auto-encoder (CAE). The FSPET method is evaluated on a dataset of 30 brain PET imaging with ground truth annotated by a radiologist. Results which outperform other baselines demonstrate the effectiveness of the FSPET framework.