Building spatiotemporal anatomical models using joint 4-D segmentation, registration, and subject-specific atlas estimation

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Publication Type pre-print
School or College <blank>
Department <blank>
Creator Gerig, Guido
Other Author Prastawa, Marcel W.; Awate, Suyash P.
Title Building spatiotemporal anatomical models using joint 4-D segmentation, registration, and subject-specific atlas estimation
Date 2012-01-01
Description Longitudinal analysis of anatomical changes is a vital component in many personalized-medicine applications for predicting disease onset, determining growth/atrophy patterns, evaluating disease progression, and monitoring recovery. Estimating anatomical changes in longitudinal studies, especially through magnetic resonance (MR) images, is challenging because of temporal variability in shape (e.g. from growth/atrophy) and appearance (e.g. due to imaging parameters and tissue properties aecting intensity contrast, or from scanner calibration). This pa- per proposes a novel mathematical framework for con- structing subject-specic longitudinal anatomical models. The proposed method solves a generalized problem of joint segmentation, registration, and subjectspecic atlas building, which involves not just two images, but an entire longitudinal image sequence. The proposed framework describes a novel approach that integrates fundamental principles that underpin methods for image segmentation, image registration, and atlas construction. This paper presents evaluation on simulated longitudinal data and on clinical longitudinal brain MRI data. The results demonstrate that the proposed framework effectively integrates information from 4-D spatiotemporal data to generate spatiotemporal models that allow analysis of anatomical changes over time.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 49
Last Page 56
Language eng
Bibliographic Citation Prastawa, M. W., Awate, S. P., & Gerig, G. (2012). Building spatiotemporal anatomical models using joint 4-D segmentation, registration, and subject-specific atlas estimation. Proceedings of the 2012 IEEE Mathematical Methods in Biomedical Image Analysis (MMBIA) Conference, 49-56.
Rights Management (c) 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 852,085 bytes
Identifier uspace,19174
ARK ark:/87278/s69k7mc6
Setname ir_uspace
Date Created 2015-01-12
Date Modified 2015-01-12
ID 712763
Reference URL https://collections.lib.utah.edu/ark:/87278/s69k7mc6
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