GLCV-NET: An automatic diagnosis system for advanced liver fibrosis using global–local cross view in B-mode ultrasound images

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Bianzhe Wu , ZeRong Huang , Jinglin Liang , Hong Yang , Wei Wang , Shuangping Huang , LiDa Chen , Qinghua Huang
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引用次数: 0

Abstract

Background and objective

: Advanced liver fibrosis is a critical stage in the evaluation of chronic liver disease (CLD), holding clinical significance in the development of treatment strategies and estimating the disease progression.

Methods:

This paper proposes an innovative Global–Local Cross-View Network (GLCV-Net) for the automatic diagnosis of advanced liver fibrosis from ultrasound (US) B-mode images. The proposed method consists of three main components: 1. A Segmentation-enhanced Global Hybrid Feature Extractor for segmenting the liver parenchyma and extracting global features; 2. A Heatmap-weighted Local Feature Extractor for selecting candidate regions and automatically identifying suspicious areas to construct local features; 3. A Scale-adaptive Fusion Module to balance the contributions of global and local scales in evaluating advanced liver fibrosis.

Results:

The predictive performance of the model was validated on an internal dataset of 1003 chronic liver disease (CLD) patients and an external dataset of 46 CLD patients, both subjected to liver fibrosis staging through pathological assessment. On the internal dataset, GLCV-Net achieved 86.9% accuracy, 85.0% recall, 85.4% precision, and 85.2% F1-score. Further validation on the external dataset confirmed its robustness, with scores of 86.1% in accuracy, 83.1% in recall, 80.8% in precision, and 81.9% in F1-score.

Conclusion:

These results underscore the GLCV-Net’s potential as a promising approach for non-invasively and accurately diagnosing advanced liver fibrosis in CLD patients, breaking through the limitations of traditional methods by integrating global and local information of liver fibrosis, significantly enhancing diagnostic accuracy.
GLCV-NET:利用 B 型超声波图像中的全局局部交叉视图对晚期肝纤维化进行自动诊断的系统。
背景和目的:晚期肝纤维化是评估慢性肝病(CLD)的关键阶段:晚期肝纤维化是评估慢性肝病(CLD)的一个关键阶段,在制定治疗策略和估计疾病进展方面具有重要的临床意义:本文提出了一种创新的全局-局部交叉视图网络(GLCV-Net),用于从超声(US)B 型图像自动诊断晚期肝纤维化。该方法由三个主要部分组成:1.1.分割增强型全局混合特征提取器,用于分割肝实质并提取全局特征;2.热图加权局部特征提取器,用于选择候选区域并自动识别可疑区域以构建局部特征;3.规模自适应融合模块,用于平衡全局和局部规模在评估晚期肝纤维化中的贡献:该模型的预测性能在一个由 1003 名慢性肝病(CLD)患者组成的内部数据集和一个由 46 名慢性肝病患者组成的外部数据集上得到了验证。在内部数据集上,GLCV-Net 的准确率为 86.9%,召回率为 85.0%,精确率为 85.4%,F1 分数为 85.2%。在外部数据集上的进一步验证证实了其稳健性,准确率为 86.1%,召回率为 83.1%,精确率为 80.8%,F1 分数为 81.9%:这些结果凸显了GLCV-Net作为一种有望无创准确诊断CLD患者晚期肝纤维化的方法的潜力,它突破了传统方法的局限,整合了肝纤维化的全局和局部信息,显著提高了诊断准确性。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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