TY - GEN
T1 - 3D articulated registration of the mouse hind limb for bone morphometric analysis in rheumatoid arthritis
AU - Brown, James M.
AU - Naylor, Amy
AU - Buckley, Chris
AU - Filer, Andrew
AU - Claridge, Ela
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84903708721&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-08554-8_5
DO - 10.1007/978-3-319-08554-8_5
M3 - Conference contribution
AN - SCOPUS:84903708721
SN - 9783319085531
VL - 8545
T3 - Lecture Notes in Computer Science
SP - 41
EP - 50
BT - Biomedical Image Registration
A2 - Ourselin, Sebastien
A2 - Modat, Marc
PB - Springer
T2 - 6th International Workshop on Biomedical Image Registration, WBIR 2014
Y2 - 7 July 2014 through 8 July 2014
ER -