National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China, Shenzhen, China; Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China.
Shenzhen RayShape Medical Technology Co., Ltd, Shenzhen, Guangdong, China.
Med Image Anal. 2024 Oct;97:103229. doi: 10.1016/j.media.2024.103229. Epub 2024 Jun 8.
Arrhythmia is a major cardiac abnormality in fetuses. Therefore, early diagnosis of arrhythmia is clinically crucial. Pulsed-wave Doppler ultrasound is a commonly used diagnostic tool for fetal arrhythmia. Its key step for diagnosis involves identifying adjacent measurable cardiac cycles (MCCs). As cardiac activity is complex and the experience of sonographers is often varied, automation can improve user-independence and diagnostic-validity. However, arrhythmias pose several challenges for automation because of complex waveform variations, which can cause major localization bias and missed or false detection of MCCs. Filtering out non-MCC anomalies is difficult because of large intra-class and small inter-class variations between MCCs and non-MCCs caused by agnostic morphological waveform variations. Moreover, rare arrhythmia cases are insufficient for classification algorithms to adequately learn discriminative features. Using only normal cases for training, we propose a novel hierarchical online contrastive anomaly detection (HOCAD) framework for arrhythmia diagnosis during test time. The contribution of this study is three-fold. First, we develop a coarse-to-fine framework inspired by hierarchical diagnostic logic, which can refine localization and avoid missed detection of MCCs. Second, we propose an online learning-based contrastive anomaly detection with two new anomaly scores, which can adaptively filter out non-MCC anomalies on a single image during testing. With these complementary efforts, we precisely determine MCCs for correct measurements and diagnosis. Third, to the best of our knowledge, this is the first reported study investigating intelligent diagnosis of fetal arrhythmia on a large-scale and multi-center ultrasound dataset. Extensive experiments on 3850 cases, including 266 cases covering three typical types of arrhythmias, demonstrate the effectiveness of the proposed framework.
心律失常是胎儿的主要心脏异常。因此，心律失常的早期诊断在临床上至关重要。脉冲波多普勒超声是胎儿心律失常的常用诊断工具。其诊断的关键步骤包括识别相邻的可测量心脏周期（MCC）。由于心脏活动复杂，且超声医师的经验常常不同，自动化可以提高用户独立性和诊断有效性。然而，由于复杂的波形变化，心律失常对自动化提出了一些挑战，这可能导致主要的定位偏差和 MCC 的漏检或误检。由于 MCC 和非 MCC 之间的类内变化大、类间变化小，因此很难过滤掉非 MCC 异常。由于分类算法无法充分学习到有区别的特征，罕见的心律失常病例也不足以进行分类。仅使用正常病例进行训练，我们提出了一种新颖的分层在线对比异常检测（HOCAD）框架，用于在测试时进行心律失常诊断。本研究的贡献有三点。首先，我们开发了一种基于分层诊断逻辑的粗到精框架，可以细化定位并避免 MCC 的漏检。其次，我们提出了一种基于在线学习的对比异常检测方法，具有两个新的异常分数，可以在测试时自适应地过滤单个图像中的非 MCC 异常。通过这些互补的努力，我们精确地确定了 MCC 以进行正确的测量和诊断。第三，据我们所知，这是第一项针对大规模多中心超声数据集的胎儿心律失常智能诊断的研究报告。对 3850 例病例（包括 266 例涵盖三种典型类型的心律失常）的广泛实验表明，所提出的框架是有效的。
