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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Medical Image Synthesis via Deep Learning.

Biting Yu¹, Yan Wang², Lei Wang¹

¹School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia.

Advances in Experimental Medicine and Biology

|February 8, 2020

Summary

This summary is machine-generated.

Deep learning models like convolutional neural networks (CNNs) and generative adversarial networks (GANs) are revolutionizing medical image synthesis. This work reviews these methods and presents new applications in PET and MR image synthesis.

Keywords:

Brain Convolutional neural networks (CNNs)Deep learning Generative adversarial networks (GANs)Machine learning Magnetic resonance imaging (MRI)Medical image synthesis Positron emission tomography (PET)

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Area of Science:

Medical imaging and computational analysis.
Artificial intelligence in healthcare.
Advanced visualization techniques.

Background:

Medical imaging is crucial for visualizing internal body tissues.
Diverse imaging modalities offer unique visualization characteristics.
High-quality or multi-modal imaging can be costly, driving the need for synthesis methods.

Purpose of the Study:

To review existing medical image synthesis methods.
To focus on deep learning approaches, specifically CNNs and GANs.
To present novel applications in low-dose to high-dose PET and cross-modality MR image synthesis.

Main Methods:

Review of general medical image synthesis techniques.
Detailed introduction of typical CNN and GAN models.
Application of these models for specific synthesis tasks.

Main Results:

Demonstration of CNNs and GANs as dominant methods in medical image synthesis.
Successful synthesis of low-dose to high-dose PET images.
Effective synthesis of cross-modality MR images.

Conclusions:

Deep learning, particularly CNNs and GANs, shows significant promise for medical image synthesis.
These methods can potentially reduce costs and improve imaging quality.
The presented work contributes to advancing synthesis techniques for clinical applications.