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Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data.

机译：来自低对比​​度Micro-CT数据的基于Atlas的小鼠全身分割。

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This paper presents a fully automated method for atlas-based whole-body segmentation in non-contrast-enhanced Micro-CT data of mice. The position and posture of mice in such studies may vary to a large extent, complicating data comparison in cross-sectional and follow-up studies. Moreover, Micro-CT typically yields only poor soft-tissue contrast for abdominal organs. To overcome these challenges, we propose a method that divides the problem into an atlas constrained registration based on high-contrast organs in Micro-CT (skeleton, lungs and skin), and a soft tissue approximation step for low-contrast organs. We first present a modification of the MOBY mouse atlas (Segars et al., 2004) by partitioning the skeleton into individual bones, by adding anatomically realistic joint types and by defining a hierarchical atlas tree description. The individual bones as well as the lungs of this adapted MOBY atlas are then registered one by one traversing the model tree hierarchy. To this end, we employ the Iterative Closest Point method and constrain the Degrees of Freedom of the local registration, dependent on the joint type and motion range. This atlas-based strategy renders the method highly robust to exceptionally large postural differences among scans and to moderate pathological bone deformations. The skin of the torso is registered by employing a novel method for matching distributions of geodesic distances locally, constrained by the registered skeleton. Because of the absence of image contrast between abdominal organs, they are interpolated from the atlas to the subject domain using Thin-Plate-Spline approximation, defined by correspondences on the already established registration of high-contrast structures (bones, lungs and skin). We extensively evaluate the proposed registration method, using 26 non-contrast-enhanced Micro-CT datasets of mice, and the skin registration and organ interpolation, using contrast-enhanced Micro-CT datasets of 15 mice. The posture and shape varied significantly among the animals and the data was acquired in vivo. After registration, the mean Euclidean distance was less than two voxel dimensions for the skeleton and the lungs respectively and less than one voxel dimension for the skin. Dice coefficients of volume overlap between manually segmented and interpolated skeleton and organs vary between 0.47+/-0.08 for the kidneys and 0.73+/-0.04 for the brain. These experiments demonstrate the method's effectiveness for overcoming exceptionally large variations in posture, yielding acceptable approximation accuracy even in the absence of soft-tissue contrast in in vivo Micro-CT data without requiring user initialization.
机译：本文提出了一种基于Atlas的全身分割的完全自动化方法，该方法在小鼠的非增强Micro-CT数据中。在此类研究中，小鼠的位置和姿势可能会有很大差异，从而使横断面研究和后续研究中的数据比较复杂化。此外，Micro-CT通常仅对腹部器官产生较差的软组织对比度。为了克服这些挑战，我们提出了一种将问题分为基于Micro-CT中高对比度器官（骨骼，肺和皮肤）的图集约束配准以及针对低对比度器官的软组织近似步骤的方法。我们首先介绍了MOBY小鼠地图集的修改（Segars等，2004），方法是将骨骼划分成单独的骨骼，添加解剖学上逼真的关节类型，并定义分层的地图集树描述。然后，将这些适应的MOBY地图集的各个骨骼和肺部一一遍历模型树层次结构。为此，我们采用迭代最近点方法，并根据关节类型和运动范围来限制局部配准的自由度。这种基于图谱的策略使该方法对于扫描之间异常大的姿势差异和中等程度的病理性骨骼变形具有很高的鲁棒性。躯干的皮肤通过采用一种新颖的方法进行配准，该方法用于匹配受配准骨骼约束的局部测地距离的分布。由于腹腔器官之间不存在图像对比，因此使用薄板样条线近似将它们从地图集内插到受检区域，该方法由与高对比度结构（骨骼，肺和皮肤）的已建立配准相对应的定义。我们使用26个非增强型Micro-CT小鼠数据集广泛评估了拟议的配准方法，并使用了15个小鼠的增强型Micro-CT数据集进行了皮肤配准和器官插值。动物之间的姿势和形状差异很大，并且在体内获得了数据。配准后，骨骼和肺部的平均欧几里德距离分别小于两个体素尺寸，而皮肤的平均欧氏距离小于一个素体尺寸。手动分割和内插的骨骼与器官之间的体积骰子重叠系数在肾脏的0.47 +/- 0.08与大脑的0.73 +/- 0.04之间变化。这些实验证明了该方法可克服姿势中异常大的变化，即使在无需用户初始化的情况下，即使在体内Micro-CT数据中不存在软组织对比度的情况下，也可获得可接受的近似精度。

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