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.
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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. (Wed,) reported a other. 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.