A scalable method to improve gray matter segmentation at ultra high field MRI
| Autor: | Ingo Marquardt, Marian Schneider, Roy A.M. Haast, Federico De Martino, Omer Faruk Gulban |
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| Přispěvatelé: | Audition, RS: FPN CN 2, Vision, RS: FPN CN 1, MRI, RS: FPN CN 5, RS: FSE MaCSBio, RS: FPN MaCSBio |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Male
7 TESLA Physiology Computer science lcsh:Medicine AUDITORY AREAS computer.software_genre Nervous System Diagnostic Radiology 030218 nuclear medicine & medical imaging Mathematical and Statistical Techniques 0302 clinical medicine Voxel MULTIDIMENSIONAL TRANSFER-FUNCTIONS Image Processing Computer-Assisted Medicine and Health Sciences Segmentation Gray Matter lcsh:Science Cerebrospinal Fluid Ground truth Transfer Functions Multidisciplinary medicine.diagnostic_test Radiology and Imaging Applied Mathematics Simulation and Modeling Magnetic Resonance Imaging Body Fluids DIGITAL BRAIN PHANTOM Data Acquisition FMRI Physical Sciences Scalability Female Anatomy Algorithms Research Article Computer and Information Sciences HUMAN PARIETAL CORTEX Imaging Techniques IMAGES Decision tree Neuroimaging Research and Analysis Methods 03 medical and health sciences Data visualization Diagnostic Medicine Histogram Journal Article medicine Humans VISUAL-CORTEX business.industry lcsh:R Biology and Life Sciences COMPOSITIONAL DATA Magnetic resonance imaging Pattern recognition MODEL Magnetic Fields Cardiovascular Anatomy Blood Vessels lcsh:Q Artificial intelligence business Mathematical Functions computer Mathematics 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | PLOS ONE, 13(6):0198335. Public Library of Science PLoS ONE PLoS ONE, Vol 13, Iss 6, p e0198335 (2018) |
| ISSN: | 1932-6203 |
| DOI: | 10.1101/245738 |
| Popis: | High-resolution (functional) magnetic resonance imaging (MRI) at ultra high magnetic fields (7 Tesla and above) enables researchers to study how anatomical and functional properties change within the cortical ribbon, along surfaces and across cortical depths. These studies require an accurate delineation of the gray matter ribbon, which often suffers from inclusion of blood vessels, dura mater and other non-brain tissue. Residual segmentation errors are commonly corrected by browsing the data slice-by-slice and manually changing labels. This task becomes increasingly laborious and prone to error at higher resolutions since both work and error scale with the number of voxels. Here we show that many mislabeled, non-brain voxels can be corrected more efficiently and semi-automatically by representing three-dimensional anatomical images using two-dimensional histograms. We propose both a uni-modal (based on first spatial derivative) and multi-modal (based on compositional data analysis) approach to this representation and quantify the benefits in 7 Tesla MRI data of nine volunteers. We present an openly accessible Python implementation of these approaches and demonstrate that editing cortical segmentations using two-dimensional histogram representations as an additional post-processing step aids existing algorithms and yields improved gray matter borders. By making our data and corresponding expert (ground truth) segmentations openly available, we facilitate future efforts to develop and test segmentation algorithms on this challenging type of data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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