Fast Segmentation of the Left Atrial Appendage in 3-D Transesophageal Echocardiographic Images

• Type: Article in International Scientific Journal
• Date: 2018-12

Authors

Pedro Morais (Author) Other; Sandro Queirós (Author) Other; Pieter De Meester (Author) Other; Werner Budts (Author) Other; João L. Vilaça (Author) Other; João Manuel R. S. Tavares (Author) FEUP; Jan D'hooge (Author) Other

Journal

Title: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

Vol. 65 No. 12

Pages: 2332-2342

ISSN: 0885-3010

Publisher: IEEE

Indexing

ISI Web of Knowledge - 1 Citation

ISI Web of Science

Scopus - 11 Citations

INSPEC

Scientific classification

CORDIS: Technological sciences

FOS: Medical and Health sciences

Other information

Authenticus ID: P-00Q-1ZM

DOI: 10.1109/tuffc.2018.2872816

Abstract (EN): Left atrial appendage (LAA) has been generally described as "our most lethal attachment," being considered the major source of thromboembolism in patients with nonvalvular 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 manual analysis of peri-procedural images. This approach is suboptimal, time demanding, and highly variable between experts, which can result in lengthy procedures and excess manipulations. In this paper, a semiautomatic LAA segmentation technique for 3-D 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 3-D segmentation results corroborated the accuracy, robustness to the variation of the parameters, and computationally attractiveness of the proposed method, taking approximately 14 s to segment the LAA with an average accuracy of similar to 0.9 mm. Moreover, a performance comparable to the interobserver variability was found. Finally, the advantages of the segmented model were evaluated, while semiautomatically 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11