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Modeling 4D Changes in Pathological Anatomy using Domain Adaptation: Analysis of TBI Imaging using a Tumor Database

Institution:
1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2School of Computing, University of Utah, SAalt lake City, UT, USA.
3Laboratory of Neuro Imaging, University of California at Los Angeles, CA, USA.
4Brain Injury Research Center, University of California at Los Angeles, CA, USA.
Publisher:
MBIA 2013
Publication Date:
Sep-2013
Volume Number:
2013
Issue Number:
8159
Pages:
31-9
Citation:
Multimodal Brain Image Anal (2013). 2013 Sep; 8159: 31-9.
PubMed ID:
25346953
PMCID:
PMC4208427
Appears in Collections:
NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wang B., Prastawa M., Saha A., Awate S.P., Irimia A., Chambers M.C., Vespa P.M., Van Horn J.D., Pascucci V., Gerig G. Modeling 4D Changes in Pathological Anatomy using Domain Adaptation: Analysis of TBI Imaging using a Tumor Database. Multimodal Brain Image Anal (2013). 2013 Sep; 8159: 31-9. PMID: 25346953. PMCID: PMC4208427.
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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 framework 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.

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