Semi-Automatic Estimation of Device Size for Left Atrial Appendage Occlusion in 3D TEE images.

Left atrial appendage (LAA) occlusion is used to reduce the risk of thromboembolism in patients with nonvalvular atrial fibrillation, by obstructing the LAA through a percutaneously delivered device. Nonetheless, correct device sizing is complex, requiring the manual estimation of different measurements in pre-/peri-procedural images, which is tedious, time-consuming and with high inter- and intra-observer variability. In this work, a semi-automatic solution to estimate the required relevant clinical measurements is described. This solution starts with the 3D segmentation of the LAA in 3D transesophageal echocardiographic (TEE) images, using a constant blind-ended model initialized through a manually defined spline. Then, the segmented LAA surface is aligned with a set of templates, i.e. 3D surfaces plus relevant measurement planes (manually defined by one observer), transferring the latter to the unknown situation. Specifically, the alignment is performed in three consecutive steps, namely: 1) rigid alignment using the LAA clipping plane position, 2) orientation compensation using the circumflex artery location and 3) anatomical refinement through a weighted iterative closest point algorithm. The novel solution was evaluated in a clinical database with 20 volumetric TEE images. Two experiments were set up to assess: 1) the sensitivity of the models parameters and 2) the accuracy of the proposed solution for the estimation of the clinical measurements. Measurement levels manually identified by two observers were used as ground truth. The proposed solution obtained results comparable to the inter-observer variability, presenting narrower limits of agreement for all measurements. Moreover, this solution proved to be fast, taking nearly 40 seconds (manual analysis took 3 minutes) to estimate the relevant measurements while being robust to the variation of the model's parameters. Overall, the proposed solution showed its potential for fast and robust estimation of the clinical measurements for occluding device selection, proving its added value for clinical practice.