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

WenJun Zhou; Tianfei Wang; Yuhang He; Shenghua Xie; Anguo Luo; Bo Peng; Lixue Yin; WenJun Zhou; Tianfei Wang; Yuhang He; Shenghua Xie; Anguo Luo; Bo Peng; Lixue Yin

doi:10.3934/mbe.2023697

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 15623-15640. doi: 10.3934/mbe.2023697

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Research article

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

1.
Ultrasound 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
2.
School of Computer Science, Southwest Petroleum University, Chengdu 610500, China

Received: 20 March 2023 Revised: 02 July 2023 Accepted: 10 July 2023 Published: 28 July 2023

Ischemic heart disease or stroke caused by the rupture or dislodgement of a carotid plaque poses a huge risk to human health. To obtain accurate information on the carotid plaque characteristics of patients and to assist clinicians in the determination and identification of atherosclerotic areas, which is one significant foundation work. Existing work in this field has not deliberately extracted texture information of carotid from the ultrasound images. However, texture information is a very important part of carotid ultrasound images. To make full use of the texture information in carotid ultrasound images, a novel network based on U-Net called Contrast U-Net is designed in this paper. First, the proposed network mainly relies on a contrast block to extract accurate texture information. Moreover, to make the network better learn the texture information of each channel, the squeeze-and-excitation block is introduced to assist in the jump connection from encoding to decoding. Experimental results from intravascular ultrasound image datasets show that the proposed network can achieve superior performance compared with other popular models in carotid plaque detection.
- carotid plaque,
- texture information,
- Semantic Segmentation,
- contrast block,
- Contrast U-Net
Citation: WenJun Zhou, Tianfei Wang, Yuhang He, Shenghua Xie, Anguo Luo, Bo Peng, Lixue Yin. Contrast U-Net driven by sufficient texture extraction for carotid plaque detection[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 15623-15640. doi: 10.3934/mbe.2023697

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Abstract

Ischemic heart disease or stroke caused by the rupture or dislodgement of a carotid plaque poses a huge risk to human health. To obtain accurate information on the carotid plaque characteristics of patients and to assist clinicians in the determination and identification of atherosclerotic areas, which is one significant foundation work. Existing work in this field has not deliberately extracted texture information of carotid from the ultrasound images. However, texture information is a very important part of carotid ultrasound images. To make full use of the texture information in carotid ultrasound images, a novel network based on U-Net called Contrast U-Net is designed in this paper. First, the proposed network mainly relies on a contrast block to extract accurate texture information. Moreover, to make the network better learn the texture information of each channel, the squeeze-and-excitation block is introduced to assist in the jump connection from encoding to decoding. Experimental results from intravascular ultrasound image datasets show that the proposed network can achieve superior performance compared with other popular models in carotid plaque detection.

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