LASSNet: A Four Steps Deep Neural Network for Left Atrial Segmentation and Scar Quantification

Overview

Journal Left Atr Scar Quantif Segm (2022)

Date 2023 Jun 8

PMID 37287952

Authors

Arthur L Lefebvre

Carolyna A P Yamamoto

Julie K Shade

Ryan P Bradley

Rebecca A Yu

Rheeda L Ali

Dan M Popescu

Adityo Prakosa

Eugene G Kholmovski

Natalia A Trayanova

Affiliations

Soon will be listed here.

Abstract

Accurate quantification of left atrium (LA) scar in patients with atrial fibrillation is essential to guide successful ablation strategies. Prior to LA scar quantification, a proper LA cavity segmentation is required to ensure exact location of scar. Both tasks can be extremely time-consuming and are subject to inter-observer disagreements when done manually. We developed and validated a deep neural network to automatically segment the LA cavity and the LA scar. The global architecture uses a multi-network sequential approach in two stages which segment the LA cavity and the LA Scar. Each stage has two steps: a region of interest Neural Network and a refined segmentation network. We analysed the performances of our network according to different parameters and applied data triaging. 200+ late gadolinium enhancement magnetic resonance images were provided by the LAScarQS 2022 Challenge. Finally, we compared our performances for scar quantification to the literature and demonstrated improved performances.

Citing Articles

Assessing the arrhythmogenic propensity of fibrotic substrate using digital twins to inform a mechanisms-based atrial fibrillation ablation strategy.

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PMID: 39157719 PMC: 11329066. DOI: 10.1038/s44161-024-00489-x.

Elastic shape analysis for unsupervised clustering of left atrial appendage morphology.

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Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.

Trayanova N, Lyon A, Shade J, Heijman J Physiol Rev. 2023; 104(3):1265-1333.

PMID: 38153307 PMC: 11381036. DOI: 10.1152/physrev.00017.2023.

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