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3D Articulated Registration of the Mouse Hind Limb for Bone Morphometric Analysis in Rheumatoid Arthritis

机译:类风湿性关节炎的骨形态计量分析的小鼠后肢的3D关节式配准

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We describe an automated method for building a statistical model of the mouse hind limb from micro-CT data, based on articulated registration. The model was initialised by hand-labelling the constituent bones and joints of a single sample. A coarse alignment of the entire model mesh to a sample mesh was followed by consecutive registration of individual bones and their descendants down a hierarchy. Transformation parameters for subsequent bones were constrained to a subset of vertices within a frustum projecting from a terminal joint of an already registered parent bone. Samples were segmented and transformed into a common coordinate frame, and a statistical shape model was constructed. The results of ten registered samples are presented, with a mean registration error of less than 40 μm (~ 3 voxels) for all samples. The shape variation amongst the samples was extracted by PCA to create a statistical shape model. Registration of the model to three unseen normal samples gives rise to a mean registration error of 5.84 μm, in contrast to 27.18 μm for three unseen arthritic samples. This may suggest that pathological bone shape changes in models of RA are detectable as departures from the model statistics.
机译:我们描述了一种基于铰接式配准的基于微型CT数据的小鼠后肢统计模型的自动构建方法。通过手工标记单个样品的骨骼和关节来初始化模型。整个模型网格与样本网格的粗略对齐之后,依次逐级注册各个骨骼及其后代。用于后续骨骼的转换参数被限制在从已注册的父骨骼的末端关节伸出的视锥中的顶点子集。将样本分割并转换为公共坐标系,然后构建统计形状模型。给出了十个注册样本的结果,所有样本的平均注册误差均小于40μm(〜3个体素)。通过PCA提取样品之间的形状变化以创建统计形状模型。将模型与三个看不见的正常样本进行配准会产生5.84μm的平均配准误差,而对于三个看不见的关节炎样本则为27.18μm。这可能表明,RA模型中的病理性骨骼形状变化可从模型统计数据中检测出来。

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