Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment.

Volumetric placental measurement using 3-D ultrasound has proven clinical utility in predicting adverse pregnancy outcomes. However, this metric cannot currently be employed as part of a screening test due to a lack of robust and real-time segmentation tools. We present a multiclass (MC) convolutional neural network (CNN) developed to segment the placenta, amniotic fluid, and fetus.

Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning.

We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications.

Two-Dimensional Echocardiography Estimates of Fetal Ventricular Mass throughout Gestation.

Background: Two-dimensional (2D) ultrasound quality has improved in recent years. Quantification of cardiac dimensions is important to screen and monitor certain fetal conditions. We assessed the feasibility and reproducibility of fetal ventricular measures using 2D echocardiography, reported normal ranges in our cohort, and compared estimates to other modalities.