The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible atlas-to-image transformations and compute the expected label value (ELV), thereby not relying merely on the transformation deemed "optimal" by the registration method. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We evaluate our ELV computation approach by applying it to brain, liver, and pancreas segmentation on datasets of magnetic resonance and computed tomography images.

中文翻译：

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通过计算预期标签值进行多图集图像软分割

在医学图像分割中通常使用多个地图集。这通常需要将地图集（或平均地图集）以可变形方式配准到新图像，这在计算上很昂贵并且易于陷入局部最优状态。我们建议改为考虑所有可能的图集到图像转换的可能性，并计算期望的标签值（ELV），从而不仅仅依赖于配准方法认为“最佳”的转换。而且，我们这样做时并没有实际执行可变形配准，因此避免了相关的计算成本。我们通过将其应用于磁共振和计算机断层扫描图像数据集上的脑，肝和胰腺分割来评估我们的ELV计算方法。