dc.contributor.author | Dalca, Adrian Vasile | |
dc.contributor.author | Sridharan, Ramesh | |
dc.contributor.author | Sabuncu, Mert R | |
dc.contributor.author | Golland, Polina | |
dc.date.accessioned | 2018-06-06T15:21:45Z | |
dc.date.available | 2018-06-06T15:21:45Z | |
dc.date.issued | 2015-11 | |
dc.identifier.isbn | 978-3-319-24573-7 | |
dc.identifier.isbn | 978-3-319-24574-4 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/116142 | |
dc.description.abstract | We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject’s health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient’s scans to the predicted subject-specific healthy anatomical trajectory. Keywords: Population Trend, Baseline Image, Kernel Machine, Good Linear Unbiased Predictor, Segmentation Label | en_US |
dc.description.sponsorship | National Institute for Biomedical Imaging and Bioengineering (U.S.) (Grant 1K25EB013649-01) | en_US |
dc.description.sponsorship | BrightFocus Foundation (AHAF-A2012333) | en_US |
dc.description.sponsorship | Neuroimaging Analysis Center (U.S.) (P41EB015902) | en_US |
dc.description.sponsorship | National Institutes of Health (U.S.) (DA022759) | en_US |
dc.description.sponsorship | Wistron Corporation | en_US |
dc.language.iso | en_US | |
dc.publisher | Springer | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1007/978-3-319-24574-4_62 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | PMC | en_US |
dc.title | Predictive Modeling of Anatomy with Genetic and Clinical Data | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Dalca, Adrian V., et al. “Predictive Modeling of Anatomy with Genetic and Clinical Data.” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by Nassir Navab et al., vol. 9351, Springer International Publishing, 2015, pp. 519–26. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.mitauthor | Dalca, Adrian Vasile | |
dc.contributor.mitauthor | Sridharan, Ramesh | |
dc.contributor.mitauthor | Sabuncu, Mert R | |
dc.contributor.mitauthor | Golland, Polina | |
dc.relation.journal | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dspace.orderedauthors | Dalca, Adrian V.; Sridharan, Ramesh; Sabuncu, Mert R.; Golland, Polina | en_US |
dspace.embargo.terms | N | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-8422-0136 | |
dc.identifier.orcid | https://orcid.org/0000-0002-5002-1227 | |
dc.identifier.orcid | https://orcid.org/0000-0003-2516-731X | |
mit.license | OPEN_ACCESS_POLICY | en_US |