Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

Subject-Specific Longitudinal Shape Analysis by Coupling Spatiotemporal Shape Modeling with Medial Analysis

Institution:
1Computer Science and Engineering, Tandon School of Engineering, New York University, NY, USA.
2School of Computer Science, McGill University, Montreal, Canada.
3Department of Psychiatry, Carver College of Medicine, University of Iowa, IA, USA.
Publication Date:
Apr-2017
Volume Number:
10133
Pages:
101331A
Citation:
Proc SPIE Int Soc Opt Eng. 2017 Apr; 10133: 101331A.
PubMed ID:
28966430
PMCID:
PMC5617643
Appears in Collections:
NA-MIC
Sponsors:
U01 NS082086/NS/NINDS NIH HHS/United States
R01 NS040068/NS/NINDS NIH HHS/United States
R01 NS050568/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
R01 NS054893/NS/NINDS NIH HHS/United States
Generated Citation:
Hong S., Fishbaugh J., Rezanejad M., Siddiqi K., Johnson H., Paulsen J., Kim E.Y., Gerig G. Subject-Specific Longitudinal Shape Analysis by Coupling Spatiotemporal Shape Modeling with Medial Analysis. Proc SPIE Int Soc Opt Eng. 2017 Apr; 10133: 101331A. PMID: 28966430 . PMCID: PMC5617643.
Downloaded: 371 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

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. Shape changes to anatomy may also be affected by external structural changes from neighboring structures, which may cause non-linear pose variations. 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 separately quantify 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 simultaneously 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 quantitative and qualitative comparison.

Additional Material
1 File (51.101kB)
Hong-SPIEIntSocOptEng2017-fig3.jpg (51.101kB)