  Congenital heart disease (CHD) is the most common congenital abnormality
associated with birth defects in the United States. Despite training efforts
and substantial advancement in ultrasound technology over the past years, CHD
remains an abnormality that is frequently missed during prenatal
ultrasonography. Therefore, computer-aided detection of CHD can play a critical
role in prenatal care by improving screening and diagnosis. Since many CHDs
involve structural abnormalities, automatic segmentation of anatomical
structures is an important step in the analysis of fetal echocardiograms. While
existing methods mainly focus on the four-chamber view with a small number of
structures, here we present a more comprehensive deep learning segmentation
framework covering 14 anatomical structures in both three-vessel trachea and
four-chamber views. Specifically, our framework enhances the V-Net with spatial
dropout, group normalization, and deep supervision to train a segmentation
model that can be applied on both views regardless of abnormalities. By
identifying the pitfall of using the Dice loss when some labels are unavailable
in some images, this framework integrates information from multiple views and
is robust to missing structures due to anatomical anomalies, achieving an
average Dice score of 79%.
