Noninvasive brain imaging techniques including structural MRI, diffusion MRI, perfusion MRI, functional MRI (fMRI), EEG, MEG, PET, SPECT, and CT are playing increasingly important roles in elucidating structural and functional properties in normal and diseased brains. It is widely believed that these different imaging modalities provide distinctive yet complementary information that is conducive to the understanding of the working dynamics of the brain. Effective processing, fusion, analysis, and visualization of images from multiple sources, however, pose as new challenging problems due to variation in imaging resolutions, spatial-temporal dynamics, as well as the fundamental biophysical mechanisms that are involved in determining the character of the images.
The objective of this MICCAI workshop on MBIA is to move forward the state of the art in multimodal brain image analysis, in terms of analysis methodologies, algorithms, software systems, validation approaches, benchmark datasets, neuroscience, and clinical applications. We hope that MBIA will become a forum for researchers to exchange ideas, data, and software, in order to speed up the development of innovative technologies for hypothesis testing and data-driven discovery in brain science.
Topics include but are not limited to:
- Multimodal brain data fusion methodologies – fusion of multimodal structural, functional, diffusion, and/or perfusion MRI data, fusion of fMRI and EEG data, and fusion of MRI and PET data.
- Methods for modeling temporal brain dynamics – modeling brain states via fMRI and/or EEG data, longitudinal analysis of brain image data.
- Structural and functional brain network construction methods – identification and optimization of network nodes, assessment of network properties, and creation of graph models for description of structural and functional brain networks.
- Brain connectivity analysis methods – joint modeling of structural and functional brain connectivity, relationship between structural and functional connectivity, and dynamics of connectivity.
- Multimodal brain image pattern classification and prediction methods – disease classification via multimodal image features, feature extraction and/or feature selection for dimensionality reduction of multimodal image data, multimodal image predictors of clinical measures, and integrative analysis of multimodal neuroimaging, genetics and biomarker data.
- Multimodal brain image visualization and data management methods – visual analytics of multimodal image data and visualization of large-volume, dynamic, multimodal image data.
- Registration, segmentation, shape analysis, and signal processing methods – multimodal image registration, multi-parametric image segmentation, and multi-resolution signal processing. (Note: For segmentation, we have particular interest in multi-parametric methods for brain applications. Authors with other segmentation focuses may also consider the SATA, BRATS, and MRBrainS workshops.)
- Validation approaches and benchmark data generation – cross-validation via multiple image modalities and generation of benchmark data via reproducibility studies.
- Clinical applications – computer aided diagnosis and follow-up of brain diseases via multimodal images, early diagnosis of brain diseases via multimodal images, and differential diagnosis of brain diseases via multimodal images.
Accepted papers are published as a volume in the Springer LNCS.
The direct URL to LNCS 8159 (MBIA 2013 Proceedings) is http://link.springer.com/book/10.1007/978-3-319-02126-3/page/1.
Best Paper Award
A “Best Paper Award” will be given at the end of the workshop. The winner will be chosen by the organizers based on relevance, novelty, and scientific contribution.
Congratulations to Bo Wang, Marcel Prastawa, Avishek Saha, Suyash P. Awate, Andrei Irimia, Micah C. Chambers, Paul M. Vespa, John D. Van Horn, Valerio Pascucci, and Guido Gerig, from the University of Utah, whose work “Modeling 4D changes in pathological anatomy using domain adaptation: analysis of TBI imaging using a tumor database” won the MBIA 2013 Best Paper Award!