FHUSP-NET: A Multi-task model for fetal heart ultrasound standard plane recognition and key anatomical structures detection

Li, Furong; Li, Ping; Wu, Xiuming; Zeng, Pan; Lyu, Guorong; Fan, Yuling; Liu, Peizhong; Song, Haisheng; Liu, Zhonghua

doi:10.1016/j.compbiomed.2023.107741

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…Deep learning has been applied in the field of fetal echocardiography for various tasks, including standard plane identification from a sequence of fetal heart images [ 23 – 25 ], detection of abnormal structures [ 26 – 29 ], and segmentation of cardiac structures [ 30 – 33 ]. Most studies designed for fetal cardiac structure segmentation have focused on the four-chamber view of the fetal heart [ 30 , 31 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for identifying and segmenting three vessels in fetal heart ultrasound images

Yan,

Ling,

Mao

et al. 2024

BioMed Eng OnLine

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Background Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis. Method In this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels—the pulmonary artery, aorta, and superior vena cava—in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images. Results We evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively. Conclusions Our experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations.

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