基于深度学习的洗刷分类在肝脏超声造影检查中的决策支持。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Journal of Medical Imaging Pub Date : 2025-07-01 Epub Date: 2025-07-22 DOI:10.1117/1.JMI.12.4.044502
Hannah Strohm, Sven Rothlübbers, Jürgen Jenne, Dirk-André Clevert, Thomas Fischer, Niklas Hitschrich, Bernhard Mumm, Paul Spiesecke, Matthias Günther
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引用次数: 0

摘要

目的:对比增强超声(CEUS)是诊断局灶性肝脏病变的可靠工具,在正常b超中表现不明确。然而,动态对比序列的解释可能具有挑战性,阻碍了超声造影的广泛应用。我们研究了基于深度学习的图像分类器的使用,以确定从CEUS获取的诊断相关特征冲洗。方法:我们引入了一种数据表示,它与病变大小、亚型和序列长度的数据异质性无关。然后,利用基于图像的分类器进行冲洗分类。系统地评估了处理稀疏注释和运动的策略,以及使用灌注模型覆盖缺失时间点的潜在好处。结果:结果表明,与文献中发现的研究相比,性能良好,验证的最大平衡精度为84.0%,测试集的最大平衡精度为82.0%。基于相关性的帧选择提高了分类性能,而进一步的运动补偿在实验中没有显示出任何好处。结论:基于深度学习的水洗分类在原则上是可行的。与良性与恶性分类相比,它提供了一种简单的可解释性形式。分类个体特征而不是诊断本身的概念可以扩展到其他特征,如动脉流入行为。将其与现有方法区分开来的主要因素是数据表示和任务制定,以及来自两个算法开发和测试中心的500个肝脏病变的大型数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep-learning-based washout classification for decision support in contrast-enhanced ultrasound examinations of the liver.

Purpose: Contrast-enhanced ultrasound (CEUS) is a reliable tool to diagnose focal liver lesions, which appear ambiguous in normal B-mode ultrasound. However, interpretation of the dynamic contrast sequences can be challenging, hindering the widespread application of CEUS. We investigate the use of a deep-learning-based image classifier for determining the diagnosis-relevant feature washout from CEUS acquisitions.

Approach: We introduce a data representation, which is agnostic to data heterogeneity regarding lesion size, subtype, and length of the sequences. Then, an image-based classifier is exploited for washout classification. Strategies to cope with sparse annotations and motion are systematically evaluated, as well as the potential benefits of using a perfusion model to cover missing time points.

Results: Results indicate decent performance comparable to studies found in the literature, with a maximum balanced accuracy of 84.0% on the validation and 82.0% on the test set. Correlation-based frame selection yielded improvements in classification performance, whereas further motion compensation did not show any benefit in the conducted experiments.

Conclusions: It is shown that deep-learning-based washout classification is feasible in principle. It offers a simple form of interpretability compared with benign versus malignant classifications. The concept of classifying individual features instead of the diagnosis itself could be extended to other features such as the arterial inflow behavior. The main factors distinguishing it from existing approaches are the data representation and task formulation, as well as a large dataset size with 500 liver lesions from two centers for algorithmic development and testing.

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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
期刊介绍: JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.
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