Martin Styner is an assistant professor in the Department of Psychiatry with a joint appointment in the Department of Computer Science at the University of North Carolina at Chapel Hill (UNC). As co-director of the UNC Neuro Image Research and Analysis Laboratory and associate director of the Developmental Neuroimaging Core in the Carolina Institute for Developmental Disabilities at UNC, he oversees medical imaging research projects in the field of neurodevelopment. Dr. Styner began his research in the field of medical image analysis in 1994 as a graduate student at the Swiss Federal Institute of Technology ETH Zurich, Switzerland. He received his Masters in 1997 from ETH Zurich and subsequently his Ph.D. in 2001 from UNC. From 2001-2002, Dr. Styner held the position of project leader at the Duke Image Analysis Laboratory in Durham, NC. From 2002 to 2004, he founded and headed a thriving research group in Medical Image Analysis for 2 years at the M.E. Müller Research Center, University of Bern, Switzerland. In 2004, Dr. Styner joined the faculty at the University of North Carolina. He has participated in leading positions in several national and international projects with close interdisciplinary cooperation with researchers in the fields of medicine, engineering, industry and computer science. Dr. Styner has co-authored 28 papers in peer reviewed journals and 64 papers in peer reviewed conferences. He is on the editorial board of “Medical Image Analysis”, the premier journal in the field of medical image analysis. His main field of expertise is in medical image processing and analysis. He has an extensive background in anatomical structure and tissue segmentation, structural brain morphometry, modeling, atlas building, diffusion tensor imaging, small animal and primate imaging, as well as intra and inter-modality registration.
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This work does not contribute any novelty with respect to the visualization methodology, is rather a new resource for the neuroimaging community. This work is submitted to the SPIE Biomedical Applications in Molecular, Structural, and Functional Imaging conference. The source code of this application is available in NITRC∗
However, to use this toolbox, a user must have an intermediate knowledge in scripting languages (MATLAB). FADTTSter was created to overcome this issue and make the statistical analysis accessible to any non-technical researcher. FADTTSter is actively being used by researchers at the University of North Carolina. FADTTSter guides non-technical users through a series of steps including quality control of subjects and fibers in order to setup the necessary parameters to run FADTTS.
Additionally, FADTTSter implements interactive charts for FADTTS’ outputs. This interactive chart enhances the researcher experience and facilitates the analysis of the results. FADTTSter’s motivation is to improve usability and provide a new analysis tool to the community that complements FADTTS.
Ultimately, by enabling FADTTS to a broader audience, FADTTSter seeks to accelerate hypothesis testing in neuroimaging studies involving heterogeneous clinical data and diffusion tensor imaging.
This work is submitted to the Biomedical Applications in Molecular, Structural, and Functional Imaging conference. The source code of this application is available in NITRC.
Quantitative evaluation of our approach was performed based on a dataset of T1- and T2-weighted MRI scans from 12-month-old macaques where labeling by our anatomical experts was used as independent standard. In this dataset, LOGISMOS-B has an average signed surface error of 0.01 ± 0.03mm and an unsigned surface error of 0.42 ± 0.03mm over the whole brain.
Excluding the rather problematic temporal pole region further improves unsigned surface distance to 0.34 ± 0.03mm. This high level of accuracy reached by our algorithm even in this challenging developmental dataset illustrates its robustness and its potential for primate brain studies.
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The development of Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) has opened up the possibility of studying the complex organization of the brain's white matter in-vivo. By measuring the diffusion of water molecules in tissues, the technique gives insights into the structure and orientation of major white matter pathways, and DT-MRI findings have the potential to play a critical role in the extraction of meaningful information for diagnosis, prognosis and following of treatment response.
The course will guide participants through the fundamental aspects of DT-MRI data analysis, as well as the challenges of transferring cutting-edge DT-MRI techniques to clinical routine. The format will include a series of hands-on sessions with the participants running DT-MRI analysis on their own laptops, to provide a practical experience of extracting useful clinical information from Diffusion MR images. Participants will be guided through an integrated workflow for exploring the brain white matter in a series of datasets that will be provided as part of the course. The hands-on sessions will use DT-MRI tools from the NA-MIC toolkit, which include the 3DSlicer software, an open-source platform for medical image processing and 3D visualization used in biomedical and clinical research.
This event is part of the on-going effort of the NIH-funded National Alliance for Medical Image Computing (NA-MIC) to transfer the latest advances in biomedical image analysis to the scientific and clinical community.
<i>This course is intended as a companion to SC1063, Diffusion Imaging. Attendees will benefit maximally by attending both courses.</i>
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