24 February 2017 Subject-specific longitudinal shape analysis by coupling spatiotemporal shape modeling with medial analysis
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Abstract
Modeling subject-specific shape change is one of the most important challenges in longitudinal shape analysis of disease progression. Whereas anatomical change over time can be a function of normal aging, anatomy can also be impacted by disease related degeneration. Anatomical shape change may also be affected by structural changes from neighboring shapes, which may cause non-linear variations in pose. In this paper, we propose a framework to analyze disease related shape changes by coupling extrinsic modeling of the ambient anatomical space via spatiotemporal deformations with intrinsic shape properties from medial surface analysis. We compare intrinsic shape properties of a subject-specific shape trajectory to a normative 4D shape atlas representing normal aging to isolate shape changes related to disease. The spatiotemporal shape modeling establishes inter/intra subject anatomical correspondence, which in turn enables comparisons between subjects and the 4D shape atlas, and also quantitative analysis of disease related shape change. The medial surface analysis captures intrinsic shape properties related to local patterns of deformation. The proposed framework jointly models extrinsic longitudinal shape changes in the ambient anatomical space, as well as intrinsic shape properties to give localized measurements of degeneration. Six high risk subjects and six controls are randomly sampled from a Huntington’s disease image database for qualitative and quantitative comparison.
Conference Presentation
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Sungmin Hong, Sungmin Hong, James Fishbaugh, James Fishbaugh, Morteza Rezanejad, Morteza Rezanejad, Kaleem Siddiqi, Kaleem Siddiqi, Hans Johnson, Hans Johnson, Jane Paulsen, Jane Paulsen, Eun Young Kim, Eun Young Kim, Guido Gerig, Guido Gerig, } "Subject-specific longitudinal shape analysis by coupling spatiotemporal shape modeling with medial analysis", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331A (24 February 2017); doi: 10.1117/12.2254675; https://doi.org/10.1117/12.2254675
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