| Resumo: | Left atrial appendage (LAA) has been generally described as “our most lethal attachment”, being considered the major source of thromboembolism in patients with non-valvular atrial fibrillation. Currently, LAA occlusion can be offered as a treatment for these patients, obstructing the LAA through a percutaneously delivered device. Nevertheless, correct device sizing is not straightforward, requiring the manual analysis of peri-procedural images. This approach is sub-optimal, time demanding and highly variable between experts, which can result in lengthy procedures and excess manipulations. In this article, a semi-automatic LAA segmentation technique for 3D transesophageal echocardiography (TEE) images is presented. Specifically, the proposed technique relies on a novel segmentation pipeline where a curvilinear blind-ended model is optimized through a double stage strategy: 1) fast contour evolution using global terms and 2) contour refinement based on regional energies. To reduce its computational cost, and thus make it more attractive to real interventions, the B-spline Explicit Active Surface framework was used. This novel method was evaluated in a clinical database of 20 patients. Manual analysis performed by two observers was used as ground truth. The 3D segmentation results corroborated the accuracy, robustness to the variation of the parameters and computationally attractiveness of the proposed method, taking approximately 14 seconds to segment the LAA with an average accuracy of ∼0.9 mm. Moreover, a performance comparable to the inter-observer variability was found. Finally, the advantages of the segmented model were evaluated while semi-automatically extracting the clinical measurements for device selection, showing a similar accuracy but with a higher reproducibility when compared to the current practice. Overall, the proposed segmentation method shows potential for an improved planning of LAA occlusion, demonstrating its added value for normal clinical practice.
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