Lefebvre Arthur L, Yamamoto Carolyna A P, Shade Julie K, Bradley Ryan P, Yu Rebecca A, Ali Rheeda L, Popescu Dan M, Prakosa Adityo, Kholmovski Eugene G, Trayanova Natalia A
Faculté polytechnique de Mons, UMONS, Mons, Belgium.
Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA.
Left Atr Scar Quantif Segm (2022). 2023;13586:1-15. doi: 10.1007/978-3-031-31778-1_1. Epub 2023 May 5.
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.
准确量化心房颤动患者的左心房（LA）瘢痕对于指导成功的消融策略至关重要。在进行LA瘢痕量化之前，需要进行适当的LA腔分割以确保瘢痕的准确位置。这两项任务都可能极其耗时，并且手动完成时会存在观察者间的分歧。我们开发并验证了一种深度神经网络，以自动分割LA腔和LA瘢痕。整体架构在两个阶段采用多网络顺序方法，分别分割LA腔和LA瘢痕。每个阶段有两个步骤：感兴趣区域神经网络和精细分割网络。我们根据不同参数分析了网络的性能并应用了数据分类。LAScarQS 2022挑战赛提供了200多张延迟钆增强磁共振图像。最后，我们将瘢痕量化的性能与文献进行了比较，并证明了性能的提升。
