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Related Experiment Video

Updated: Sep 8, 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

Artificial Intelligence in Computer Vision: Cardiac MRI and Multimodality Imaging Segmentation.

Alan C Kwan¹, Gerran Salto^1,2,3, Susan Cheng^1,2,3

¹Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.

Current Cardiovascular Risk Reports

|June 13, 2022

Summary

This summary is machine-generated.

Artificial intelligence is revolutionizing cardiac segmentation with U-Net models, streamlining analysis. While clinical benefits are emerging, further progress in data and integration is needed for wider adoption.

Keywords:

Cardiac segmentation artificial intelligence cardiac MRI cardiac imaging computer vision convolutional neural networks

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Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Cardiac anatomical segmentation is crucial in clinical cardiology.
Artificial intelligence (AI) and computer vision are transforming segmentation through automation and new applications.

Purpose of the Study:

To review the history and clinical context of cardiac segmentation.
To survey recent AI research in cardiac segmentation.
To clarify the clinical need for segmentation and guide future research.

Main Methods:

Review of recent manuscripts on AI and cardiac segmentation.
Focus on U-Net architecture as a common segmentation model.
Discussion of innovations in pre-processing and analysis pipelines.

Main Results:

Convolutional neural networks have significantly advanced cardiac segmentation in the last five years.
Cardiac MRI is the most frequently segmented modality, aided by public datasets.
U-Net models are prevalent, with ongoing innovations in data handling and model integration.

Conclusions:

AI-driven segmentation streamlines image analysis and offers potential for improved measurement precision.
Clinical benefits are still developing, with a need for enhanced data availability and model explainability.
Future integration into analysis pipelines holds promise for broader clinical impact.