LA-CaRe-CNN：用于左心房瘢痕分割的级联细化卷积神经网络
LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation
AI总结：
本文提出LA-CaRe-CNN，一种两阶段级联细化卷积神经网络，用于从LGE MR图像中准确分割左心房及瘢痕组织，通过5折集成达到高精度，助力个性化消融治疗。
摘要：
心房颤动（AF）是最常见的心律失常类型，其治疗可能需要患者接受消融治疗。在这种手术中，心脏组织被有意地局部瘢痕化，以防止电信号引起心律失常。患者特异性心脏数字孪生模型在个性化消融治疗方面显示出巨大潜力，然而，它们需要对健康和瘢痕组织进行准确的语义分割，这些分割通常从晚期钆增强（LGE）磁共振（MR）扫描中获得。在这项工作中，我们提出了左心房级联细化卷积神经网络（LA-CaRe-CNN），旨在从LGE MR扫描中准确分割左心房及左心房瘢痕组织。LA-CaRe-CNN是一个2阶段CNN级联，以3D方式端到端训练，其中阶段1生成左心房的预测，然后在阶段2中结合原始图像信息进行细化，以获得左心房瘢痕组织的预测。为了适应训练中未知领域的域偏移，我们采用强强度和空间增强来增加训练数据集的多样性。我们提出的基于5折集成的方法取得了出色的分割结果，即左心房的DSC为89.21%，ASSD为1.6969毫米，以及更具挑战性的左心房瘢痕组织的DSC为64.59%，G-DSC为91.80%。因此，通过LA-CaRe-CNN获得的分割结果在生成患者特异性心脏数字孪生模型以及个性化靶向消融治疗房颤等下游任务中显示出巨大潜力。
英文摘要：
Atrial fibrillation (AF) represents the most prevalent type of cardiac arrhythmia for which treatment may require patients to undergo ablation therapy. In this surgery cardiac tissues are locally scarred on purpose to prevent electrical signals from causing arrhythmia. Patient-specific cardiac digital twin models show great potential for personalized ablation therapy, however, they demand accurate semantic segmentation of healthy and scarred tissue typically obtained from late gadolinium enhanced (LGE) magnetic resonance (MR) scans. In this work we propose the Left Atrial Cascading Refinement CNN (LA-CaRe-CNN), which aims to accurately segment the left atrium as well as left atrial scar tissue from LGE MR scans. LA-CaRe-CNN is a 2-stage CNN cascade that is trained end-to-end in 3D, where Stage 1 generates a prediction for the left atrium, which is then refined in Stage 2 in conjunction with the original image information to obtain a prediction for the left atrial scar tissue. To account for domain shift towards domains unknown during training, we employ strong intensity and spatial augmentation to increase the diversity of the training dataset. Our proposed method based on a 5-fold ensemble achieves great segmentation results, namely, 89.21% DSC and 1.6969 mm ASSD for the left atrium, as well as 64.59% DSC and 91.80% G-DSC for the more challenging left atrial scar tissue. Thus, segmentations obtained through LA-CaRe-CNN show great potential for the generation of patient-specific cardiac digital twin models and downstream tasks like personalized targeted ablation therapy to treat AF.
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