ORIGINAL RESEARCH article

Front. Physiol.

Sec. Computational Physiology and Medicine

Volume 16 - 2025 | doi: 10.3389/fphys.2025.1629637

DualPlaqueNet With Dual-Branch Structure and Attention Mechanism for Carotid Plaque Semantic Segmentation and Size Prediction

Provisionally accepted
Lili  DengLili Deng1Xingyu  DuanXingyu Duan2Yongxiang  SunYongxiang Sun1Yunling  WangYunling Wang1Dongmei  SongDongmei Song1Xiaokai  DuanXiaokai Duan1*
  • 1The First People's Hospital of Zhengzhou, Zhengzhou, China
  • 2Ningxia Medical University, Yinchuan, China

The final, formatted version of the article will be published soon.

Background: With global aging and lifestyle changes, carotid atherosclerotic plaques are a major cause of cerebrovascular disease and ischemic stroke. However, ultrasound images suffer from high noise, low contrast, and blurred edges, making it difficult for traditional image processing methods to accurately extract plaque information. Objective: To establish a deep learning-based DualPlaqueNet model for semantic segmentation and size prediction of plaques in carotid ultrasound images, thereby providing comprehensive and accurate auxiliary information for clinical risk assessment and personalized diagnosis and treatment. Methods: DualPlaqueNet uses a dual-branch architecture combined with attention mechanisms and joint loss functions to optimize segmentation and regression. Notably, a multi-layer one-dimensional convolutional structure is introduced within the Efficient Channel Attention (ECA) module. The original dataset contained 287 carotid ultrasound images from patients at Zhengzhou First People's Hospital, which were divided into training, validation, and test sets. Model training, validation, and testing were performed after preprocessing and data augmentation of the training set. Its performance was compared with three other models. Results: In the plaque semantic segmentation task, DualPlaqueNet outperformed the other three models across all metrics, achieving MIoU of 88.91±1.027(%), IoU (excluding background) of 88.22±1.065(%), DSC of 89.95±1.102(%), and Accuracy of 95.98±0.073(%). For plaque size prediction, this model demonstrated lower MSE and MAE, along with a higher coefficient of determination R², proving its ability to accurately extract plaque size information from ultrasound images. Conclusion: The dual-branch design and attention mechanisms of DualPlaqueNet effectively address the challenges of ultrasound images, achieving precise segmentation and size prediction, demonstrating its potential as an auxiliary tool for future clinical applications.

Keywords: carotid plaque, Semantic segmentation, carotid ultrasound, image analysis, deep learning

Received: 20 May 2025; Accepted: 03 Jul 2025.

Copyright: © 2025 Deng, Duan, Sun, Wang, Song and Duan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Xiaokai Duan, The First People's Hospital of Zhengzhou, Zhengzhou, China

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