Hierarchical online contrastive anomaly detection for fetal arrhythmia diagnosis in ultrasound

• Published in: Medical image analysis 2024-10, Vol.97, p.103229, Article 103229
• Main Authors: Yang, Xin, Liu, Lian, Yan, Zhongnuo, Yu, Junxuan, Hu, Xindi, Yu, Xuejuan, Dong, Caixia, Chen, Ju, Liu, Hongmei, Yu, Zhuan, Deng, Xuedong, Ni, Dong, Huang, Xiaoqiong, Gou, Zhongshan
• Format: Article
• Language: English
• Subjects: Algorithms; Anomaly detection; Arrhythmias, Cardiac - diagnostic imaging; Female; Fetal arrhythmia; Humans; Image Interpretation, Computer-Assisted - methods; Online learning; Pregnancy; Ultrasonography, Prenatal - methods; Ultrasound
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2024.103229

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. •An online contrastive anomaly detection method for fetal arrhythmia diagnosis.•A hierarchical framework gradually enhances certainty in measurable cardiac cycle.•With two new anomaly scores, the detection framework adaptively filters anomalies.•The first study to make an intelligent diagnosis of fetal arrhythmia in ultrasound.