Investigation on ultrasound images for detection of fetal congenital heart defects

• Published in: Biomedical physics & engineering express 2024-05, Vol.10 (4), p.42001
• Main Authors: S, Satish, Rufus, N Herald Anantha
• Format: Article
• Language: English
• Subjects: Algorithms; congenital heart defects; convolution neural network; Deep Learning; deep neural network; denoising method; Echocardiography - methods; Female; Fetal Heart - diagnostic imaging; Fetus - diagnostic imaging; Heart Defects, Congenital - diagnostic imaging; Humans; Image Processing, Computer-Assisted - methods; medical image processing; Neural Networks, Computer; Pregnancy; Ultrasonography, Prenatal - methods; ultrasound images
• ISSN: 2057-1976; 2057-1976
• DOI: 10.1088/2057-1976/ad4f91

Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CHD detection reduces death rates and morbidity but is hampered by the relatively low detection rates (i.e., 60%) of current screening technology. The detection rate could be increased by supplementing ultrasound imaging with fetal ultrasound image evaluation (FUSI) using deep learning techniques. As a result, the non-invasive foetal ultrasound image has clear potential in the diagnosis of CHD and should be considered in addition to foetal echocardiography. This review paper highlights cutting-edge technologies for detecting CHD using ultrasound images, which involve pre-processing, localization, segmentation, and classification. Existing technique of preprocessing includes spatial domain filter, non-linear mean filter, transform domain filter, and denoising methods based on Convolutional Neural Network (CNN); segmentation includes thresholding-based techniques, region growing-based techniques, edge detection techniques, Artificial Neural Network (ANN) based segmentation methods, non-deep learning approaches and deep learning approaches. The paper also suggests future research directions for improving current methodologies.