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Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...

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Related Experiment Video

Updated: Jun 20, 2026

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET
09:03

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET

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Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.

Seung-Yeon Lee1,2, Hyeon Kang2, Jong-Hun Jeong3

  • 1Department of Translational Biomedical Sciences, Dong-A University, Busan, Korea.

Plos One
|October 20, 2021
PubMed
Summary

Convolutional neural networks (CNNs) show promise for classifying amyloid brain PET scans in Alzheimer's disease diagnosis. External validation revealed overfitting issues, but fine-tuning improved model performance, enhancing diagnostic accuracy.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Deep learning models demonstrate high accuracy in classifying amyloid brain scans for Alzheimer's disease (AD) diagnosis.
  • Overfitting is a concern in deep learning models trained on limited datasets, potentially affecting generalization.
  • Amyloid PET imaging is crucial for early AD diagnosis, aiding in treatment strategies.

Purpose of the Study:

  • To develop and validate a [18F]Florbetaben amyloid brain PET scan classification model using a convolutional neural network (CNN).
  • To assess the generalization potential of CNN models through external validation.
  • To investigate methods for improving model performance when generalization is limited.

Main Methods:

  • A CNN-based classification model was developed using the Dong-A University Hospital (DAUH) dataset.
  • Three CNN architectures (Inception3D, ResNet3D, VGG3D) were evaluated.
  • External validation was performed using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
  • Preprocessing included spatial normalization, count normalization, and skull stripping; smoothing was applied only to the external dataset.
  • Models were fine-tuned using the external dataset to improve performance.

Main Results:

  • Internal evaluation accuracy for Inception3D, ResNet3D, and VGG3D was 95.4%, 92.0%, and 97.7%, respectively.
  • External validation accuracy was significantly lower: 76.7% for Inception3D, 67.1% for ResNet3D, and 85.3% for VGG3D.
  • Fine-tuning with the external dataset improved Inception3D performance by 15.3% and ResNet3D by 16.9%.

Conclusions:

  • CNN models show potential for [18F]Florbetaben amyloid brain PET scan classification.
  • External validation is critical for assessing the generalization capability of deep learning models.
  • Model structure modification and fine-tuning are effective strategies to enhance classification performance and address overfitting in neuroimaging AI.