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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: Jul 21, 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

Image-to-Image Translation for Simplified MRI Muscle Segmentation.

Michael Gadermayr^1,2, Lotte Heckmann², Kexin Li²

¹Department of Information Technology and Systems Management, Salzburg University of Applied Sciences, Salzburg, Austria.

Frontiers in Radiology

|July 26, 2023

Summary

This summary is machine-generated.

This study introduces an unpaired image-to-image translation method to simplify medical image segmentation, reducing the need for labeled data. This approach improves segmentation accuracy for pathological muscle tissue in MRI scans.

Keywords:

MRI convolutional neural networks fatty-infiltration generative adversarial networks image processing muscle segmentation thigh

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Last Updated: Jul 21, 2025

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Published on: March 21, 2021

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Deep neural networks (DNNs) show promise in radiology but require extensive labeled data for training.
The need for large labeled datasets hinders the practical application of DNNs in medical imaging.
Automated segmentation of pathological muscle tissue in T1-weighted MR images is challenging.

Purpose of the Study:

To develop a method for simplifying medical image segmentation without requiring labeled data.
To create an "easier-to-segment" intermediate image representation using unpaired image-to-image translation.
To evaluate the effectiveness of this approach for segmenting pathological muscle tissue in thigh MR images.

Main Methods:

Utilized an unpaired image-to-image translation approach.
Developed a novel domain-specific loss formulation.
Applied the method to segment pathological muscle tissue in T1-weighted MR images.
Investigated fully automated segmentation approaches.

Main Results:

The proposed method significantly improved performance for supervised segmentation techniques.
Achieved comparable results using a basic unsupervised segmentation approach.
Demonstrated the creation of an "easier-to-segment" intermediate image representation.

Conclusions:

Unpaired image-to-image translation with a novel loss function can effectively simplify medical image segmentation tasks.
This technique reduces the dependency on labeled data, enabling practical applications of DNNs in radiology.
The approach shows potential for both supervised and unsupervised segmentation of pathological tissues.