Deep Learning for Mitral Annulus Delineation in 3D Transesophageal Echocardiography

Valvular heart disease is a significant health concern, particularly in aging populations, with mitral valve disease being among the most common. Three-dimensional transesophageal echocardiography (TEE) provides high-quality imaging for assessing the mitral valve. Delineating the mitral annulus in 3D images is an important step for accurate quantitative morphological assessment of the valve. However, manual analysis is time-consuming and prone to interobserver variability.

Deep learning is a class of machine learning methods that has led to significant progress across an extensive range of applications. In medical imaging, it has enabled improvements across a wide array of tasks, including classification, segmentation, and landmark detection. In this thesis, deep learning is applied to automate the delineation of the mitral annulus from 3D TEE images.

The thesis presents a progression of methods across three papers, each building on the last, gradually incorporating more anatomical context and spatial structure. The first paper applies a 2D model with post-processing to enforce annular continuity. The second paper introduces a periodic 3D model using cylindrical coordinates, enabling direct segmentation and prediction of anatomical orientation. The third paper integrates anatomical constraints through a graph-based model to improve the robustness and consistency of the predicted annulus.

The methods were evaluated on annotated 3D TEE datasets, showing accurate and consistent delineation of the mitral annulus across multiple subjects. In addition to the specific application, the thesis proposes a novel loss function and coordinate decoding strategy for deep learning with potential applicability in landmark detection tasks, both in medical and natural images. These Developments support the potential for integration into practical workflows, which could assist clinicians in assessing mitral valves while contributing to automation in cardiac imaging.