Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks

Area of Science:

• Neuroimaging
• Artificial Intelligence in Medicine
• Alzheimer's Disease Research

Background:

• ¹⁸F-FDG Positron Emission Tomography (PET) is vital for assessing brain glucose metabolism and amyloid burden in Alzheimer's disease (AD).
• AD patients exhibit significant metabolic deficits in the frontal cortex, highlighting its importance in AD research.
• Accurate segmentation of the frontal cortex in PET imaging is crucial for detailed analysis.

Purpose of the Study:

• To develop and evaluate a deep neural network model for precise segmentation of the frontal cortex from brain PET imaging.
• To introduce the Frontal cortex Segmentation model of brain PET imaging (FSPET) framework.
• To improve the analysis of frontal cortex changes in Alzheimer's disease.

Main Methods:

• A novel deep learning framework, FSPET, was developed.
• FSPET integrates anatomical priors of the frontal cortex into a segmentation model.
• The model is based on a conditional generative adversarial network and a convolutional auto-encoder architecture.

Main Results:

• The FSPET method was evaluated on a dataset of 30 brain PET images.
• Ground truth for annotation was provided by a radiologist.
• FSPET demonstrated superior performance compared to existing baseline methods, validating its effectiveness.

Conclusions:

• The FSPET framework offers an effective approach for segmenting the frontal cortex in brain PET imaging.
• This method holds significant potential for advancing Alzheimer's disease research by enabling detailed analysis of frontal cortex.
• Deep learning, specifically FSPET, shows promise in improving diagnostic and research tools for neurodegenerative diseases.