TY - GEN
T1 - Reducing variability in anatomical definitions over time using longitudinal diffeomorphic mapping
AU - Tward, Daniel J.
AU - Sicat, Chelsea S.
AU - Brown, Timothy
AU - Bakker, Arnold
AU - Miller, Michael I.
N1 - Funding Information:
This project was supported by the National Center for Research Resources and the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health through Grant Number P41 EB015909. This work was supported by the Kavli Foundation. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) [], which is supported by National Science Foundation grant number ACI-1053575.
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - We address the challenge of variability in the definition of anatomical structures over time in a single subject, using a templatebased diffeomorphic mapping algorithm to filter out inconsistencies. Shape changes are parametrized through 2D surfaces, while data attachment is specified through dense 3D images. The mapping uses two geodesic trajectories through diffeomorphism space: template to baseline, and baseline through the timeseries. We apply this algorithm to a study of atrophy in the entorhinal and surrounding cortex in patients with mild cognitive impairment, characterized by rate of change of log-volume. We compare the uncertainty in atrophy rate measured from manual segmentations, to that computed with segmentations filtered using our longitudinal method, and to that computed from FreeSurfer. Our method correlates well with manual (correlation coefficient 0.9881, and results in significantly less variability than manual (p 8.86e-05) and FreeSurfer (p 1.03e-04).
AB - We address the challenge of variability in the definition of anatomical structures over time in a single subject, using a templatebased diffeomorphic mapping algorithm to filter out inconsistencies. Shape changes are parametrized through 2D surfaces, while data attachment is specified through dense 3D images. The mapping uses two geodesic trajectories through diffeomorphism space: template to baseline, and baseline through the timeseries. We apply this algorithm to a study of atrophy in the entorhinal and surrounding cortex in patients with mild cognitive impairment, characterized by rate of change of log-volume. We compare the uncertainty in atrophy rate measured from manual segmentations, to that computed with segmentations filtered using our longitudinal method, and to that computed from FreeSurfer. Our method correlates well with manual (correlation coefficient 0.9881, and results in significantly less variability than manual (p 8.86e-05) and FreeSurfer (p 1.03e-04).
UR - http://www.scopus.com/inward/record.url?scp=85007309743&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007309743&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-51237-2_5
DO - 10.1007/978-3-319-51237-2_5
M3 - Conference contribution
AN - SCOPUS:85007309743
SN - 9783319512365
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 51
EP - 62
BT - Spectral and Shape Analysis in Medical Imaging - First International Workshop, SeSAMI 2016 Held in Conjunction with MICCAI 2016, Revised Selected Papers
A2 - Lombaert, Herve
A2 - Wachinger, Christian
A2 - Reuter, Martin
PB - Springer Verlag
T2 - 1st International Workshop on Spectral and Shape Analysis in Medical Imaging, SeSAMI 2016 Held in Conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Interventions, MICCAI 2016
Y2 - 21 October 2016 through 21 October 2016
ER -