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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Contrast U-Net driven by sufficient texture extraction for carotid plaque detection.

WenJun Zhou^1,2, Tianfei Wang², Yuhang He²

¹Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.

Mathematical Biosciences and Engineering : MBE

|November 3, 2023

Summary

A new Contrast U-Net model effectively extracts texture information from carotid ultrasound images for improved atherosclerotic plaque detection. This aids clinicians in identifying high-risk areas, reducing the risk of stroke and ischemic heart disease.

Keywords:

Contrast U-Net Semantic Segmentation carotid plaque contrast block texture information

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Area of Science:

Medical imaging
Artificial intelligence in healthcare
Cardiovascular disease research

Background:

Carotid plaque rupture is a major cause of stroke and ischemic heart disease.
Accurate characterization of carotid plaques is crucial for risk assessment and clinical decision-making.
Current methods often overlook texture information in ultrasound images, which is vital for plaque analysis.

Purpose of the Study:

To develop a novel deep learning network for enhanced carotid plaque detection.
To leverage texture information within carotid ultrasound images for improved accuracy.
To assist clinicians in identifying atherosclerotic areas and determining plaque characteristics.

Main Methods:

A novel U-Net based network, Contrast U-Net, was designed.
The network incorporates a contrast block for precise texture feature extraction.
Squeeze-and-excitation blocks were integrated to enhance channel-wise texture learning in skip connections.

Main Results:

The Contrast U-Net demonstrated superior performance in carotid plaque detection compared to existing models.
Experimental results on intravascular ultrasound image datasets validated the network's effectiveness.
The model successfully utilized texture information for more accurate plaque identification.

Conclusions:

The Contrast U-Net offers a significant advancement in analyzing carotid ultrasound images.
Accurate texture extraction via Contrast U-Net improves atherosclerotic plaque detection.
This technology has the potential to enhance clinical assessment and reduce cardiovascular event risks.