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Modeling 4D Changes
in Pathological Anatomy using Domain Adaptation: Analysis of TBI Imaging using a Tumor Database
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Abstract
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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Conceptual figure
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BibTex
@inproceedings{WangMBIA2013,
author = {Wang, Bo and Prastawa, Marcel and Saha,
Avishek and Awate, Suyash P and Irimia, Andrei and Chambers,
Micah C and Vespa, Paul M and Van Horn, John D and Pascucci, Valerio and Gerig, Guido},
title = {Modeling 4D Changes in Pathological Anatomy Using Domain
Adaptation: Analysis of TBI Imaging Using a Tumor Database},
booktitle = {Multimodal Brain Image Analysis},
pages = {31-39},
year = {2013},
publisher={Springer}
}
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