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Modeling 4D Pathological Changes by Leveraging Normative Models

1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2Icahn School of Medicine at Mount Sinai, New York, NY, USA.
3The Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
4Yahoo Labs, Sunnyvale, CA, USA.
5Brain Injury Research Center, University of California at Los Angeles, CA, USA.
6Tandon School of Engineering, Department of Computer Science and Engineering, NYU, NY, USA.
Publication Date:
Comput Vis Image Underst
Volume Number:
Comput Vis Image Underst. 2016 Oct;151:3-13.
PubMed ID:
Image segmentation, brain parcellation, medical imaging
Appears in Collections:
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wang B., Prastawa M., Irimia A., Saha A., Liu W., Goh S.Y.M., Vespa P.M., Van Horn J.D., Gerig G. Modeling 4D Pathological Changes by Leveraging Normative Models. Comput Vis Image Underst. 2016 Oct;151:3-13. PMID: 27818606. PMCID: PMC5094466.
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With the increasing use of efficient multimodal 3D imaging, clinicians are able to access longitudinal imaging to stage pathological diseases, to monitor the efficacy of therapeutic interventions, or to assess and quantify rehabilitation efforts. Analysis of such four-dimensional (4D) image data presenting pathologies, including disappearing and newly appearing lesions, represents a significant challenge due to the presence of complex spatio-temporal changes. Image analysis methods for such 4D image data have to include not only a concept for joint segmentation of 3D datasets to account for inherent correlations of subject-specific repeated scans but also a mechanism to account for large deformations and the destruction and formation of lesions (e.g., edema, bleeding) due to underlying physiological processes associated with damage, intervention, and recovery. In this paper, we propose a novel framework that provides a joint segmentation-registration framework to tackle the inherent problem of image registration in the presence of objects not present in all images of the time series. Our methodology models 4D changes in pathological anatomy across time and and also provides an explicit mapping of a healthy normative template to a subject's image data with pathologies. Since atlas-moderated segmentation methods cannot explain appearance and locality pathological structures that are not represented in the template atlas, the new framework provides different options for initialization via a supervised learning approach, iterative semisupervised active learning, and also transfer learning, which results in a fully automatic 4D segmentation method. We demonstrate the effectiveness of our novel approach with synthetic experiments and a 4D multimodal MRI dataset of severe traumatic brain injury (TBI), including validation via comparison to expert segmentations. However, the proposed methodology is generic in regard to different clinical applications requiring quantitative analysis of 4D imaging representing spatio-temporal changes of pathologies.

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