Multiview and Multiclass Image Segmentation using Deep Learning in Fetal Echocardiography
This addresses the problem of missed congenital heart disease diagnoses in prenatal care by improving segmentation for computer-aided detection, though it is incremental as it builds on existing deep learning methods.
The paper tackled automatic segmentation of anatomical structures in fetal echocardiograms to aid in detecting congenital heart disease, achieving an average Dice score of 79% for 14 structures across multiple views.
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%.