Extraction of the cerebrum and cerebellum from structural magnetic resonance images is an important initial step in neuroimage analysis. Inaccurate brain extraction (also referred to as skull stripping) can have a very negative effect on subsequent analyses, and for some sensitive studies, manually-assisted extraction remains the only viable option. Despite many reported algorithms and several software packages available for research purposes, there is still widespread variability in the performance of these methods, and none are adequate for comprehensive analyses involving the whole human brain and large numbers of studies. This R21 will develop, code, test, and distribute the SPECTRE (Simple Paradigms for Extra Cranial Tissue REmoval) software for the neuroscience community. Funded as an R21 under the Program Announcement PAR-08-183, this Exploratory Collaboration with the NA-MIC National Center for Biomedical Computing, will jointly develop SPECTRE software within the """"""""NA-MIC Kit"""""""" software environment and will make it freely available as both source code and platform specific executables. The underlying SPECTRE image processing algorithm was recently developed and validated in the Image Analysis and Community Laboratory (IACL) at Johns Hopkins University and has been reported in a leading conference. SPECTRE is innovative in its use of multiple atlases, its combined use of fuzzy classification, watershed segmentation, and morphological image segmentation, and its emphasis on a high penalty for accidentally removing cortical gray matter. Research and develop efforts will accomplish the following specific aims: 1) The existing code and all new code will be ported and written using the standard NA-MIC software methodology;2) The algorithms for isolating and establishing a coordinate system on the cerebellum will be completed;3) The new code will be tested and optimized for differently acquired T1-weighted data;4) An extensive comparison between SPECTRE and existing algorithms will be carried out. The result will be an algorithm that can take an arbitrary T1-weighted MR brain volume and return a volume containing the cerebellum, the cerebrum, or both, and with coordinate systems automatically established on the cerebrum and cerebellum. The SPECTRE software tool will be the first to provide selective isolation of the cerebrum, the cerebellum, or both. It will also be the first automated method for establishing a coordinate system on the cerebellum. It should be noted that the optimization criteria we have developed is particularly designed for very sensitive studies of brain changes, which has led us to incorporate a very high penalty on erroneous gray matter loss during the isolation step. For this reason and also because the software tool will be very robust, easy to use, and will function within the richly appointed environment of the widely used 3D Slicer software, we expect that the SPECTRE software tool will grow to be very popular within the neuroimaging community.
Many advances in brain science are discovered using magnetic resonance images of the brain. Automatic processing of these data, often involving very large numbers of subjects in any given study, is a necessary component in gaining scientific or medical knowledge, and automatic identification of the brain is typically a key first step in the process. This research project will provide software, freely available to the public that will automatically identify the cerebrum and cerebellum of the human brain so that further analysis, both conventional and potentially novel, can then be carried out.
| Carass, Aaron; Cuzzocreo, Jennifer L; Han, Shuo et al. (2018) Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images. Neuroimage 183:150-172 |
| Carass, Aaron; Cuzzocreo, Jennifer; Wheeler, M Bryan et al. (2011) Simple paradigm for extra-cerebral tissue removal: algorithm and analysis. Neuroimage 56:1982-92 |
| Covington, Kelsie; McCreedy, Evan S; Chen, Min et al. (2010) Interfaces and Integration of Medical Image Analysis Frameworks: Challenges and Opportunities. Annu ORNL Biomed Sci Eng Cent Conf 2010:1-4 |
