TY - GEN
T1 - 4D continuous medial representation by geodesic shape regression
AU - Hong, Sungmin
AU - Fishbaugh, James
AU - Gerig, Guido
N1 - Funding Information:
This research was supported by NIH grants U01 NS082086, NS40068, NS050568 (PREDICT HD), U54 EB005149 (NA-MIC), and NIH 1R01 DA038215-01A1.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - Longitudinal shape analysis has shown great potential to model anatomical processes from baseline to follow-up observations. Shape regression estimates a continuous trajectory of time-discrete anatomical shapes to quantify temporal changes. The need for shape alignment and point-to-point correspondences represent limitations of current shape analysis methodologies, and present significant challenges in shape evaluation. We propose a method that estimates a continuous trajectory of continuous medial representations (CM-Rep) from a set of time-discrete observed shapes. To avoid the traditional step of aligning individual objects, shape changes are modeled via diffeomorphic ambient space deformations. Using a medial shape representation, we separately capture object pose changes and intrinsic geometry changes. Tests and validation with synthetic and real anatomical shapes demonstrate that the new method captures extrinsic shape changes as well as intrinsic shape changes encoded with CM-Reps, a highly relevant property for studying growth and disease processes.
AB - Longitudinal shape analysis has shown great potential to model anatomical processes from baseline to follow-up observations. Shape regression estimates a continuous trajectory of time-discrete anatomical shapes to quantify temporal changes. The need for shape alignment and point-to-point correspondences represent limitations of current shape analysis methodologies, and present significant challenges in shape evaluation. We propose a method that estimates a continuous trajectory of continuous medial representations (CM-Rep) from a set of time-discrete observed shapes. To avoid the traditional step of aligning individual objects, shape changes are modeled via diffeomorphic ambient space deformations. Using a medial shape representation, we separately capture object pose changes and intrinsic geometry changes. Tests and validation with synthetic and real anatomical shapes demonstrate that the new method captures extrinsic shape changes as well as intrinsic shape changes encoded with CM-Reps, a highly relevant property for studying growth and disease processes.
KW - Brain
KW - Modeling - Anatomical
KW - Physiological and pathological
KW - Shape Analysis
UR - http://www.scopus.com/inward/record.url?scp=85048072512&partnerID=8YFLogxK
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U2 - 10.1109/ISBI.2018.8363743
DO - 10.1109/ISBI.2018.8363743
M3 - Conference contribution
AN - SCOPUS:85048072512
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1014
EP - 1017
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -