Convolutional Neural Networks for Automated Fetal Cardiac Assessment using 4D B-Mode Ultrasound

Structural and functional assessment of the fetal heart is currently performed using ultrasound-based imaging biomarkers that require annotation by a trained observer. This requires experience and expertise and is subject to observer variability. In this work, a Convolutional Neural Network (CNN) was implemented for segmentation of the fetal annulus in 250 cases to provide automated measurements of the excursion of the mitral and tricuspid valve annular planes (TAPSE/MAPSE). These were evaluated against manual annotation. After training, the CNN achieved a Dice score of 0.78 (±0.02;σ) and the excursion measures had an RMSE of <; 0.16 cm. Results show the feasibility of using a CNN to detect the fetal annuli and measure TAPSE/MAPSE for cardiac functional assessment.