Fetal yawning is of interest because of its clinical, developmental and theoretical implications. However, the methodological challenges of identifying yawns from ultrasonographic scans have not been systematically addressed. We report two studies th...
Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3%. For the early detection of birth defects and mortality, echocardiography remains the most ...
It is essential to measure anatomical parameters in prenatal ultrasound images for the growth and development of the fetus, which is highly relied on obtaining a standard plane. However, the acquisition of a standard plane is, in turn, highly subject...
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Aug 14, 2019
OBJECTIVES: To investigate the interobserver and intraobserver variability and corresponding learning curve in a semiautomatic approach for a standardized assessment of the fetal heart (fetal intelligent navigation echocardiography [FINE]).
Machine learning, particularly deep learning has boosted medical image analysis over the past years. Training a good model based on deep learning requires large amount of labelled data. However, it is often difficult to obtain a sufficient number of ...
OBJECTIVE: Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD), are frequently used to evaluate gestational age and diagnose fetal central nervous system pathology. Because manual measurements ...
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provid...
Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analyses to objectively derive new indicators of abnormal fetal development that are embedded in ultrasoun...
We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image whi...