基于颈动脉超声视频的斑块回声分类关键特征指导下的多维聚合网络

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-06-01 DOI:10.1016/j.irbm.2024.100841
Ying Li , Xudong Liang , Haibing Chen , Jiang Xie , Zhuo Bi
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引用次数: 0

摘要

目的不稳定斑块可导致急性心脑血管疾病。斑块的稳定性和不稳定性与超声波中斑块的回声状态有关。与静态图像相比,颈动脉视频能提供详细的斑块信息。由于噪声、干扰帧、小目标(斑块)和复杂的形状变化,基于超声的斑块回声分类具有挑战性。本研究提出了一种基于关键特征的多维聚合网络(MA-Net),用于基于颈动脉超声视频的斑块诊断,该网络仅使用视频级标签。MA-Net 由关键特征 (KF) 模块和时间-通道-空间 (TCS) 模块组成。KF 模块学习每个帧对特征级分类的贡献,自适应地推断每个帧的重要性得分,从而减少干扰帧的影响。TCS 模块包括时空通道(TC)和时空空间(TS)子模块。除了研究时间维度外,它还深入研究信道和空间维度之间的关系。TC 分析通道之间的时间依赖性并过滤噪声。结果 MA-Net 在 SHU-Ultrasound-Video-2020 数据集上的表现优于最先进的视频分类模型,准确率至少提高了 5%,准确率达到 87.36%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Multi-Dimensional Aggregation Network Guided by Key Features for Plaque Echo Classification Based on Carotid Ultrasound Video

A Multi-Dimensional Aggregation Network Guided by Key Features for Plaque Echo Classification Based on Carotid Ultrasound Video

Objective

Unstable plaques can cause acute cardiovascular and cerebrovascular diseases. The stability and instability of plaque are related to the plaque echo status in ultrasound. Carotid videos provide detailed plaque information compared to static images. Ultrasound-based plaque echo classification is challenging due to noise, interference frames, small targets (plaques), and complex shape changes.

Methods

This study proposes a Multi-dimensional Aggregation Network (MA-Net) guided by key features for plaque diagnosis based on carotid ultrasound video, which uses only video-level labels. MA-Net consists of Key-Feature (KF) and Temporal-Channel-Spatial (TCS) modules. The KF module learns the contribution of each frame to the classification at the feature level, adaptively infers the importance score of each frame, thereby reducing the influence of interference frames. The TCS module includes the Temporal-Channel (TC) and Temporal-Spatial (TS) sub-modules. In addition to studying the temporal dimension, it delves into the relationship between the channel and spatial dimensions. TC analyses the temporal dependencies among the channels and filters noise. Moreover, TS extracts features more accurately through the spatio-temporal information contained in the surrounding environment of the plaque.

Results

The performance of MA-Net on the SHU-Ultrasound-Video-2020 dataset is better than that of the state-of-the-art models of video classification, showing at least a 5% increase in accuracy, with an accuracy rate of 87.36%.

Conclusion

The outstanding diagnostic capability of the proposed model will help provide a more robust and reproducible diagnostic process with a lower labour cost for clinical carotid plaque diagnosis.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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