The fully automated deep learning pipeline achieved Dice scores of 0.96 and over 93% labeling accuracy, enabling precise cineCT-based assessment of regional right ventricular function.
Does a fully automated deep learning pipeline accurately assess regional right ventricular function and strain from cineCT images?
A fully automated deep learning pipeline can accurately assess right ventricular regional strain and volumetry from cineCT images, potentially improving the efficiency and reproducibility of RV function assessment.
Abstract 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. These steps are time-consuming and prone to inter- and intra-observer 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) is a 3D high resolution configuration of nnU-Net to define the endocardial boundary, while the Right Ventricular Wall Labeler (RVWL) is 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 and RVWL both yielded Dice scores of 0.96, and accurate volumetry metrics. RVWL achieved high Dice scores (0.90) and high accuracy (93%) for wall labeling. The combination of RHBS+RVWL provided accurate assessment of free and septal wall regional strain, with a median cosine similarity value of 0.97 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.
Craine et al. (2026) studied this question. The fully automated deep learning pipeline achieved Dice scores of 0.96 and over 93% labeling accuracy, enabling precise cineCT-based assessment of regional right ventricular function.