A Deep Learning-Based and Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis from 3D Echocardiography

3D transesophageal echocardiography (3DTEE), is the recommended method for diagnosing mitral regurgitation (MR). 3DTEE provides a high-quality 3D image of the mitral valve (MV), allowing for precise segmentation and measurement of the regurgitant valve anatomy. However, manual TEE segmentations are time-consuming and prone to intra-operator variability, affecting the reliability of the measurements. To address this, we developed a fully automated pipeline using a 3D convolutional neural network (CNN) to segment MV substructures (annulus, anterior leaflet, and posterior leaflet) and quantify MV anatomy. The 3D CNN, based on a multi-decoder residual U-Net architecture, was trained and tested on a dataset comprising 100 3DTEE images with corresponding segmentations. Within the pipeline, a custom algorithm refines the CNN-based segmentations and extracts MV models, from which anatomical landmarks and features are quantified. The accuracy of the proposed method was assessed using Dice score and mean surface distance (MSD) against ground truth segmentations, and the extracted anatomical parameters were compared against a semiautomated commercial software TomTec Image Arena. The trained 3D CNN achieved an average Dice score of 0.79 and MSD of 0.47 mm for the combined segmentation of the annulus, anterior and posterior leaflet. The proposed CNN architecture outperformed a baseline residual U-Net architecture in MV substructure segmentation, and the refinement of the predicted annulus segmentation improved MSD by 8.36%. The annular and leaflet linear measurements differed by less than 7.94 mm and 3.67 mm, respectively, compared to the 3D measurements obtained with TomTec Image Arena. The proposed pipeline was faster than the commercial software, with a modeling time of 12.54 s and a quantification time of 54.42 s.

A CT-based deep learning system for automatic assessment of aortic root morphology for TAVI planning

Accurate planning of transcatheter aortic implantation (TAVI) is important to minimize complications, and it requires anatomic evaluation of the aortic root (AR), commonly done through 3D computed tomography (CT) image analysis. Currently, there is no standard automated solution for this process. Two convolutional neural networks (CNNs) with 3D U-Net architectures (model 1 and model 2) were trained on 310 CT scans for AR analysis. Model 1 performed AR segmentation and model 2 identified the aortic annulus and sinotubular junction (STJ) contours. Results were validated against manual measurements of 178 TAVI candidates. After training, the two models were integrated into a fully automated pipeline for geometric analysis of the AR. The trained CNNs effectively segmented the AR, annulus and STJ, resulting in mean Dice scores of 0.93 for the AR, and mean surface distances of 1.16 mm and 1.30 mm for the annulus and STJ, respectively. Automatic measurements were in good agreement with manual annotations, yielding annulus diameters that differed by 0.52 [-2.96, 4.00] mm (bias and 95% limits of agreement for manual minus algorithm). Evaluating the area-derived diameter, bias and limits of agreement were 0.07 [-0.25, 0.39] mm. STJ and sinuses diameters computed by the automatic method yielded differences of 0.16 [-2.03, 2.34] and 0.1 [-2.93, 3.13] mm, respectively. The proposed tool is a fully automatic solution to quantify morphological biomarkers for pre-TAVI planning. The method was validated against manual annotation from clinical experts and showed to be quick and effective in assessing AR anatomy, with potential for time and cost savings.

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta

Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoracic aortic aneurysms (ATAA). To accurately reproduce hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements still makes researchers resort to idealized BCs. In this study we generated and thoroughly characterized a large dataset of synthetic 4D aortic velocity profiles suitable to be used as BCs for CFD simulations. 4D flow MRI scans of 30 subjects with ATAA were processed to extract cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. We built a data-driven generative model of 4D aortic velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.