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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: Aug 16, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

[A generative adversarial network-based unsupervised domain adaptation method for magnetic resonance image

Yubo Sun^1,2, Jianan Liu^1,2, Zewen Sun³

¹College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|December 27, 2022

Summary

This study introduces an unsupervised domain adaptation method for medical image segmentation using generative adversarial networks (GANs). The approach enhances segmentation accuracy, effectively addressing domain shift challenges in magnetic resonance (MR) imaging.

Keywords:

Domain shift Feature level domain adaptation Generative adversarial networks Image level domain adaptation Medical image segmentation

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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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

Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Context:

Domain shift significantly degrades medical image segmentation performance due to distribution differences between source and target domains.
Existing methods struggle to maintain segmentation accuracy across different imaging datasets or scanners.
Unsupervised domain adaptation is crucial for leveraging diverse medical imaging data without manual annotation.

Purpose:

To propose an unsupervised, end-to-end domain adaptation method for medical image segmentation based on generative adversarial networks (GANs).
To improve the robustness and accuracy of medical image segmentation in the presence of domain shift.
To enable effective segmentation of knee magnetic resonance (MR) images across different datasets.

Summary:

A novel unsupervised domain adaptation method using a GAN with segmentation and discriminant networks is presented.
The segmentation network utilizes residual modules for enhanced feature reusability and easier optimization.
Cross-domain features are learned via the discriminant network and a combination of segmentation and adversarial losses, trained without source domain labels.

Impact:

Achieved a mean Dice similarity coefficient (DSC) improvement of 2.52% and 6.10% over classical methods.
Significantly enhances segmentation accuracy for tibia and femur in MR images.
Effectively addresses the domain transfer problem in medical image segmentation, improving clinical applicability.