Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks.

Qianyi Zhan,Wei Hu,Yuanyuan Liu,Yuan Liu

doi:10.3389/fnins.2021.796172

Abstract

18F-FDG positron emission tomography (PET) imaging of brain glucose use and 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 Frontal cortex Segmentation model of brain PET imaging (FSPET) is proposed to tackle this problem. It combines the anatomical prior to frontal cortex into the segmentation model, which is based on conditional generative adversarial network and convolutional auto-encoder. The FSPET method is evaluated on a dataset of 30 brain PET imaging with ground truth annotated by a radiologist. Results that outperform other baselines demonstrate the effectiveness of the FSPET framework.

Highlights

Alzheimer’s disease (AD) is a progressive disease that destroys memory and other important mental functions
Medical imaging including computed tomography (CT) or magnetic resonance imaging (MRI), and with singlephoton emission computed tomography (SPECT) or positron emission tomography (PET), can be used to help doctors understand the pathophysiology of AD, for example, Aβ plaques, neurofibrillary tangles, and neuroinflammation
The proposed FSPET model adopts the framework in Conze et al (2021) based on conditional generative adversarial network (cGAN), which consists of two neural networks: the generator G and the discriminator D

Summary

Introduction

Alzheimer’s disease (AD) is a progressive disease that destroys memory and other important mental functions. As of 2019, it ranked as the sixth leading cause of death in China (Vos et al, 2020). There are more than 10 million patients with AD in China, a country with the most AD patients in the world (Jia et al, 2020). AD is usually diagnosed based on the clinical manifestation. Medical imaging including computed tomography (CT) or magnetic resonance imaging (MRI), and with singlephoton emission computed tomography (SPECT) or positron emission tomography (PET), can be used to help doctors understand the pathophysiology of AD, for example, Aβ plaques, neurofibrillary tangles, and neuroinflammation. The pathophysiology of AD is believed that starts years ahead of the of clinical observation, and helps detect AD earlier than conventional diagnostic tools (Marcus et al, 2014)

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Results