AUTHOR=Zhan Qianyi , Liu Yuanyuan , Liu Yuan , Hu Wei 

TITLE=Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.796172

DOI=10.3389/fnins.2021.796172

ISSN=1662-453X

ABSTRACT=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.