Modeling 4D changes in pathological anatomy using domain adaptation: analysis of TBI imaging using a tumor database

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Publication Type pre-print
School or College <blank>
Department <blank>
Creator Gerig, Guido
Other Author Wang, Bo; Prastawa, Marcel; Saha, Avishek; Awate, Suyash P.; Irimia, Andrei; Chambers, Micah C.; Vespa, Paul M.; Van Horn, John D.; Pascucci, Valerio
Title Modeling 4D changes in pathological anatomy using domain adaptation: analysis of TBI imaging using a tumor database
Date 2013-01-01
Description Analysis of 4D medical images presenting pathology (i.e., lesions) is significantly challenging due to the presence of complex changes over time. Image analysis methods for 4D images with lesions need to account for changes in brain structures due to deformation, as well as the formation and deletion of new structures (e.g., edema, bleeding) due to the physiological processes associated with damage, intervention, and recovery. We propose a novel framework that models 4D changes in pathological anatomy across time, and provides explicit mapping from a healthy template to subjects with pathology. Moreover, our frame-work uses transfer learning to leverage rich information from a known source domain, where we have a collection of completely segmented images, to yield effective appearance models for the input target domain. The automatic 4D segmentation method uses a novel domain adaptation technique for generative kernel density models to transfer information between different domains, resulting in a fully automatic method that requires no user interaction. We demonstrate the effectiveness of our novel approach with the analysis of 4D images of traumatic brain injury (TBI), using a synthetic tumor database as the source domain.
Type Text
Publisher Springer
Volume 8159
First Page 31
Last Page 39
Language eng
Bibliographic Citation Wang, Bo, Prastawa, M., Saha, A., Awate, S. P., Irimia, A., Chambers, M. C., Vespa, P. M., Van Horn, J. D., Pascucci, V., & Gerig, G. (2013). Modeling 4D changes in pathological anatomy using domain adaptation: analysis of TBI imaging using a tumor database.Proceedings of the 2013 MICCAI-MBIA Workshop, Lecture Notes in Computer Science (LNCS), 8159, 31-9.
Rights Management (c) Springer (The original publication is available at www.springerlink.com) The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-02126-3_4.
Format Medium application/pdf
Format Extent 2,537,468 bytes
Identifier uspace,19123
ARK ark:/87278/s65j0rcm
Setname ir_uspace
Date Created 2015-02-20
Date Modified 2021-05-06
ID 712826
Reference URL https://collections.lib.utah.edu/ark:/87278/s65j0rcm
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