A comparative study of deep-learning methods for left atrial appendage segmentation in 3D TEE images

Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting ~2.5% of the world's population. In patients with nonvalvular AF, about 90% of thrombi are located in the left atrial appendage (LAA). LAA closure (LAAC) has emerged as an alternative, performed through percutaneous implantation of an occlusion device guided by fluoroscopy, but it relies heavily on user experience and involves high radiation exposure. Transesophageal echocardiography (TEE) is an imaging modality that offers 3D real-time visualization, radiation-free, essential for guiding the LAAC procedure. Therefore, LAA segmentation from ultrasound(US) imaging is crucial. Thus, we conducted a comparative analysis of four deep learning (DL) networks for segmenting 3D US-TEE images of the LAA: UNET, UNETR, DynUNET, and ViTAutoEnc. Using a dataset of 188 3D US-TEE image volumes, DynUNET stood out with an average distance error of 1.24±0.57mm, Dice Coefficient of 83.48±7.16%, and 95% Hausdorff Distance of 3.51±2.07mm, demonstrating the potential of DL methods for LAA segmentation.