Automated mitral valve segmentation in PLAX-view transthoracic echocardiography for anatomical assessment and risk stratification.

Accurate segmentation of the mitral valve in transthoracic echocardiography (TTE) enables the extraction of various anatomical parameters that are important for guiding clinical management. However, manual mitral valve segmentation is time-consuming and prone to interobserver variability. To support robust automatic analysis of mitral valve anatomy, we propose a novel AI-based method for mitral valve segmentation and anatomical measurement extraction. We retrospectively collected a set of echocardiographic exams from 1756 consecutive patients with suspected coronary artery disease. For these patients, we retrieved expert-defined scores for mitral regurgitation (MR) severity and follow-up characteristics. PLAX-view videos were automatically identified, and the inside border of the mitral valve leaflets were manually segmented in 182 patients. To automatically segment mitral valve leaflets, we designed a deep neural network that takes a video frame and outputs a distance- and classification-map for each leaflet, supervised by manual segmentations. From the resulting automatic segmentations, we extracted leaflet length, annulus diameter, tenting area, and coaptation length. To demonstrate the clinical relevance of these automatically extracted measurements, we performed univariable and multivariable Cox Regression survival analysis, with the clinical endpoint defined as heart-failure hospitalization or all-cause mortality. We trained the segmentation model on annotated frames of 111 patients, and tested segmentation performance on a set of 71 patients. For the survival analysis, we included 1,117 patients (mean age 64.1 ± 12.4 years, 58% male, median follow-up 3.3 years). The trained model achieved an average surface distance of 0.89 mm, a Hausdorff distance of 3.34 mm, and a temporal consistency score of 97%. Additionally, leaflet coaptation was accurately detected in 93% of annotated frames. In univariable Cox regression, automated annulus diameter (>35 mm, hazard ratio (HR) = 2.38, p<0.001), tenting area (>2.4 cm, HR = 2.48, p<0.001), tenting height (>10 mm, HR = 1.91, p<0.001), and coaptation length (>3 mm, HR = 1.53, p = 0.007) were significantly associated with the defined clinical endpoint. For reference, significant MR by expert assessment resulted in an HR of 2.31 (p<0.001). In multivariable Cox Regression analysis, automated annulus diameter and coaptation length predicted the defined endpoint as independent parameters (p = 0.03 and p = 0.05, respectively). Our method allows accurate segmentation of the mitral valve in TTE, and enables fully automated quantification of key measurements describing mitral valve anatomy. This has the potential to improve risk stratification for cardiac patients.