Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framewor

Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal Echocardiography

Echocardiography is a powerful prenatal examination tool for early diagnosis of fetal congenital heart diseases (CHDs). The four-chamber (FC) view is a crucial and easily accessible ultrasound (US) image among echocardiography images.Automatic analysis of FC views contributes significantly to the early diagnosis of CHDs. The first step to automatically analyze fetal FC views is locating the fetal four crucial chambers of heart in a US image. However, it is a greatly challenging task due to several key factors, such as numerous speckles in US images, the fetal cardiac chambers with small size and unfixed positions, and category indistinction caused by the similarity of cardiac chambers. These factors hinder the process of capturing robust and discriminative features, hence destroying fetal cardiac anatomical chambers precise localization. Therefore, we first propose a multistage residual hybrid attention module (MRHAM) to improve the feature learning. Then, we present an improved YOLOv4 detection model, namely MRHAM-YOLOv4-Slim. Specially, the residual identity mapping is replaced with the MRHAM in the backbone of MRHAM-YOLOv4-Slim, accurately locating the four important chambers in fetal FC views. Extensive experiments demonstrate that our proposed method outperforms current state-of-the-art, including the precision of 0.919, the recall of 0.971, the F1 score of 0.944, the mAP of 0.953, and the frames per second (FPS) of 43.

使用基于注意力的YOLOv4框架从胎儿超声心动图四腔视图自动检测心脏腔室

超声心动图是一种强大的产前检查工具，可用于早期诊断胎儿先天性心脏病（CHD）。在超声心动图图像中，四腔（FC）视图是至关重要且易于访问的超声（US）图像。.. 对FC视图的自动分析有助于CHD的早期诊断。自动分析胎儿FC视野的第一步是在美国图像中定位胎儿的四个重要心脏腔室。但是，由于以下几个关键因素，这是一项极富挑战性的任务，例如，美国图像中出现大量斑点，具有小尺寸和不固定位置的胎儿心脏腔，以及由于心脏腔的相似性引起的类别不明确。这些因素阻碍了捕获稳健和区分性特征的过程，因此破坏了胎儿心脏解剖腔的精确定位。因此，我们首先提出了一种多阶段残差混合注意力模块（MRHAM）来改善特征学习。然后，我们提出一种改进的YOLOv4检测模型，即MRHAM-YOLOv4-Slim。特别，残留身份映射将被MRHAM-YOLOv4-Slim骨干中的MRHAM取代，从而在胎儿FC视野中准确定位四个重要的腔室。大量实验表明，我们提出的方法优于当前的最新技术，包括0.919的精度，0.971的召回率，0.94的F1得分，0.953的mAP和43的每秒帧数（FPS）。 （阅读更多）