AIMC Topic: Ultrasonography, Prenatal

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Multi-task learning for quality assessment of fetal head ultrasound images.

Medical image analysis
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...

Semiautomatic Fetal Intelligent Navigation Echocardiography Has the Potential to Aid Cardiac Evaluations Even in Less Experienced Hands.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
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]).

Self-supervised learning for medical image analysis using image context restoration.

Medical image analysis
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 ...

Automatic evaluation of fetal head biometry from ultrasound images using machine learning.

Physiological measurement
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 ...

Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging.

IEEE transactions on medical imaging
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...

Decision Fusion-Based Fetal Ultrasound Image Plane Classification Using Convolutional Neural Networks.

Ultrasound in medicine & biology
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...

Attention gated networks: Learning to leverage salient regions in medical images.

Medical image analysis
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...

Automated Fetal Head Detection and Circumference Estimation from Free-Hand Ultrasound Sweeps Using Deep Learning in Resource-Limited Countries.

Ultrasound in medicine & biology
Ultrasound imaging remains out of reach for most pregnant women in developing countries because it requires a trained sonographer to acquire and interpret the images. We address this problem by presenting a system that can automatically estimate the ...

Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images.

Physiological measurement
OBJECTIVE: Obstetricians mainly use ultrasound imaging for fetal biometric measurements. However, such measurements are cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated...