A Coarse-Fine Collaborative Learning Model for Three Vessel Segmentation in Fetal Cardiac Ultrasound Images.

Congenital heart disease (CHD) is the most frequent birth defect and a leading cause of infant mortality, emphasizing the crucial need for its early diagnosis. Ultrasound is the primary imaging modality for prenatal CHD screening. As a complement to the four-chamber view, the three-vessel view (3VV) plays a vital role in detecting anomalies in the great vessels.

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

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.

A deep learning-based approach for rectus abdominis segmentation and distance measurement in ultrasonography.

Diastasis recti abdominis (DRA) is a common condition in women. Measuring the distance between separated rectus abdominis (RA) in ultrasound images is a reliable method for the diagnosis of this disease. In clinical practice, the RA distance in multiple ultrasound images of a patient is measured by experienced sonographers, which is time-consuming, labor-intensive, and highly dependent on experience of operators.