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Model-Based Segmentation of the Left Atrium in CT and MRI Scans

机译:基于模型的CT和MRI扫描中左心房的分割

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Ablation is a minimal invasive interventional method used in cardiac electrophysiology. It is one option for the treatment of patients suffering from paroxysmal or persistent atrial fibrillation through pulmonary vein isolation. During the intervention endocardial surface potentials from a tracked mapping catheter are recorded with respect to a static patient specific surface geometry. The purpose of the presented work is to compare two different automatic segmentation methods working on both CT and MRI volumes. Segmentation of the left atrium is challenging because the shape variability is high. The use of statistical shape models initialized by means of affine image registration was explored as first method. The second method was non-parametric and based on atlas registration and statistical region growing. Segmentation results were validated and compared using a leave-one-out cross validation on the volumes provided with segmentation results achieved manually by experts. The Dice's coefficient was used as error measure. The method based on statistical region growing performed better than statistical shape models. A Dice's coefficient of 0.87 was achieved on both imaging modalities.
机译:消融是心脏电生理学中使用的最小侵入性介入方法。通过肺静脉分离治疗患有阵发性或持续的心房颤动的患者的一种选择。在静态患者特异性表面几何形状上记录来自跟踪映射导管的干预内膜表面电位。所提出的工作的目的是比较在CT和MRI卷上工作的两种不同的自动分段方法。左心房的分割是具有挑战性的,因为形状变异性高。探讨了使用仿真图像配准初始化的统计形状模型作为第一种方法。第二种方法是非参数的,基于阿特拉斯登记和统计区域生长。分割结果验证并使用了由专家手动实现的分段结果的卷上的休假交叉验证进行比较。骰子的系数用作误差测量。基于统计区域生长的方法比统计形状模型更好。在两种成像方式上实现了0.87的骰子系数。

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