Deep learning enables fully automated cineCT-based assessment of regional right ventricular function

Background Right ventricular (RV) function is a key factor in the diagnosis and prognosis of heart disease. However, current advanced CT-based assessments rely on semi-automated segmentation of the RV blood pool and manual delineation of the RV free and septal wall boundaries. Both of these steps are time-consuming and prone to inter- and intraobserver variability. Methods: We developed and evaluated a fully automated pipeline consisting of two deep learning methods to automate volumetric and regional strain analysis of the RV from contrast-enhanced, ECG-gated cineCT images. The Right Heart Blood Segmenter (RHBS) uses a 3D convolutional neural network to define the endocardial boundary, while the Right Ventricular Wall Labeler (RVWL) uses a 3D point cloud-based deep learning method to label the free and septal walls. We trained our models using a diverse cohort of patients with different RV phenotypes and tested in an independent cohort of patients with aortic stenosis undergoing TAVR. Results: Our approach demonstrated high accuracy in both cross-validation and independent validation cohorts. RHBS yielded Dice scores of 0.94 and 0.93, respectively, and accurate volumetry metrics. RVWL achieved high Dice scores (>0.90) and high accuracy (>93%) for wall labeling. The combination of RHBS and RVWL provided accurate assessment of free and septal wall regional strain, with a median cosine similarity value of 0.95 in the independent cohort. Conclusions: A fully automated 3D cineCT-based RV regional strain analysis pipeline has the potential to significantly enhance the efficiency and reproducibility of RV function assessment, enabling the evaluation of large cohorts and multi-center studies.