TY - GEN
T1 - Modeling 4D changes in pathological anatomy using domain adaptation
T2 - 3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
AU - Wang, Bo
AU - Prastawa, Marcel
AU - Saha, Avishek
AU - Awate, Suyash P.
AU - Irimia, Andrei
AU - Chambers, Micah C.
AU - Vespa, Paul M.
AU - Van Horn, John D.
AU - Pascucci, Valerio
AU - Gerig, Guido
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84883290092&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883290092&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-02126-3_4
DO - 10.1007/978-3-319-02126-3_4
M3 - Conference contribution
AN - SCOPUS:84883290092
SN - 9783319021256
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 31
EP - 39
BT - Multimodal Brain Image Analysis - Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Proceedings
Y2 - 22 September 2013 through 22 September 2013
ER -