Abstract
Of all congenital heart defects (CHDs), anomalies in heart valves and septa are among the most common and contribute about fifty percent to the total burden of CHDs. Progenitors to heart valves and septa are endocardial cushions formed in looping hearts through a multi-step process that includes localized expansion of cardiac jelly, endothelial-to-mesenchymal transition, cell migration and proliferation. To characterize the development of endocardial cushions, previous studies manually measured cushion size or cushion cell density from images obtained using histology, immunohistochemistry, or optical coherence tomography (OCT). Manual methods are time-consuming and labor-intensive, impeding their applications in cohort studies that require large sample sizes. This study presents an automated strategy to rapidly characterize the anatomy of endocardial cushions from OCT images. A two-step deep learning technique was used to detect the location of the heart and segment endocardial cushions. The acellular and cellular cushion regions were then segregated by K-means clustering. The proposed method can quantify cushion development by measuring the cushion volume and cellularized fraction, and also map 3D spatial organization of the acellular and cellular cushion regions. The application of this method to study the developing looping hearts allowed us to discover a spatial asymmetry of the acellular cardiac jelly in endocardial cushions during these critical stages, which has not been reported before.