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. These different imaging modalities provide distinctive yet complementary information to better understand the working dynamics of the brain. Effective processing, fusion, analysis, and visualization of images from multiple sources, however, pose new and challenging problems due to variation in imaging resolutions, spatial-temporal dynamics, and the fundamental biophysical mechanisms 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, e.g., fusion of multimodal structural, functional, diffusion, and/or perfusion MRI data, fusion of fMRI and EEG data, fusion of MRI and PET data, and fusion of radiology and pathology data.
- Methods that model temporal brain dynamics, e.g., modeling brain states via fMRI and/or EEG data, longitudinal analysis of brain image data.
- Structural and functional brain network construction methods, e.g., 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, e.g., joint modeling of structural and functional brain connectivity, relationship between structural and functional connectivity, and dynamics of connectivity.
- Classification and predictive modeling using multimodal brain image data, e.g., 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, multi-omics, biomarker and/or clinical data.
- Multimodal brain image visualization and data management methods, e.g., visual analytics of multimodal image data and visualization of large-volume, dynamic, multimodal image data.
- Registration, segmentation, shape analysis, and signal processing methods, e.g., multimodal image registration, multi-parametric image segmentation, and multiresolution signal processing.
- Validation approaches and benchmark data generation, e.g., cross-validation via multiple image modalities and generation of benchmark data via reproducibility studies.
- Clinical applications, e.g., 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.
This workshop will have proceedings. We would like to arrange the proceedings (including all accepted papers) as part of the MICCAI Satellite Events joint LNCS proceedings to be published by Springer Nature.