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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. © 2014 Springer International Publishing.

Original publication

DOI

10.1007/978-3-319-08554-8_5

Type

Conference paper

Publication Date

01/01/2014

Volume

8545 LNCS

Pages

41 - 50