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Automated endocardial cushion segmentation and cellularization quantification in developing hearts using optical
Shan Ling1, Jiawei Chen1, Maryse Lapierre-Landry1
1Department of Biomedical Engineering, School of Engineering and School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
Insights
This study introduces an automated deep learning method to analyze endocardial cushion development in congenital heart defects (CHDs). The new technique rapidly quantifies cushion anatomy, revealing previously unreported spatial asymmetries in developing heart structures.
Area of Science:
- Cardiovascular Research
- Developmental Biology
- Medical Imaging
Background:
- Congenital heart defects (CHDs), particularly valve and septal anomalies, represent a significant disease burden.
- Endocardial cushions, crucial for heart valve and septum formation, develop through complex cellular processes.
- Current manual analysis of cushion development is laborious and limits large-scale studies.
Purpose of the Study:
- To develop and validate an automated strategy for characterizing endocardial cushion anatomy from optical coherence tomography (OCT) images.
- To enable rapid, quantitative analysis of cushion development for large cohort studies.
- To investigate spatial organization within endocardial cushions during heart development.
Main Methods:
- A two-step deep learning model was employed for heart localization and endocardial cushion segmentation in OCT images.
- K-means clustering was utilized to differentiate acellular and cellular regions within the cushions.
- The automated method quantifies cushion volume, cellularity, and 3D spatial distribution.
Main Results:
- The automated method successfully segmented endocardial cushions and quantified their development.
- Analysis revealed a novel spatial asymmetry in acellular cardiac jelly within developing endocardial cushions.
- This finding provides new insights into the complex morphogenetic processes of early heart development.
Conclusions:
- Automated analysis of OCT images offers a powerful, efficient tool for studying endocardial cushion development.
- The discovery of spatial asymmetry highlights previously unrecognized heterogeneity in cushion composition.
- This approach can advance our understanding of CHD etiology and inform future therapeutic strategies.
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.
