In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity-neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.

中文翻译：

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多图谱分割的卡尔曼滤波视角。

在多图集分割中，通常将多个图集配准到新图像，并且将它们各自的分割标签图像进行变换和融合以形成最终分割。在这项工作中，我们为多图集分割提供了一种新的动力系统视角，受到以下事实的启发：将当前图集与新图像对齐的变换不仅可以通过直接配准来计算，还可以从对齐先前图集的变换中推断出来图集到图像以及两个图集之间的转换。这个过程类似于车辆上的全球定位系统，它通过询问卫星并利用先前的位置和速度来获取位置——这两种答案都不是完美的。为了解决这个问题，动力系统方案对于结合这两种信息至关重要。例如，使用卡尔曼滤波方案。因此，在这项工作中，提出了卡尔曼多图谱分割来稳定全局/仿射配准步骤。这项工作的贡献是双重的。首先，它为标准独立多图集配准提供了新的动态系统视角，并通过卡尔曼滤波来解决。其次，只需很少的额外计算，它就可以与大多数现有的多图谱分割方案相结合，以获得更好的配准/分割精度。