Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Image Analysis

SCI's imaging work addresses fundamental questions in 2D and 3D image processing, including filtering, segmentation, surface reconstruction, and shape analysis. In low-level image processing, this effort has produce new nonparametric methods for modeling image statistics, which have resulted in better algorithms for denoising and reconstruction. Work with particle systems has led to new methods for visualizing and analyzing 3D surfaces. Our work in image processing also includes applications of advanced computing to 3D images, which has resulted in new parallel algorithms and real-time implementations on graphics processing units (GPUs). Application areas include medical image analysis, biological image processing, defense, environmental monitoring, and oil and gas.


Ross Whitaker


Sarang Joshi

Shape Statistics
Brain Atlasing

Tolga Tasdizen

Image Processing
Machine Learning

Chris Johnson

Diffusion Tensor Analysis

Shireen Elhabian

Image Analysis
Computer Vision

Funded Research Projects:

Publications in Image Analysis:

Spatial Intensity Prior Correction for Tissue Segmentation in the Developing human Brain
S.H. Kim, V. Fonov, J. Piven, J. Gilmore, C. Vachet, G. Gerig, D.L. Collins, M. Styner. In Proceedings of IEEE ISBI 2011, pp. 2049--2052. 2011.
DOI: 10.1109/ISBI.2011.5872815

CENTS: Cortical Enhanced Neonatal Tissue Segmentation
F. Shi, D. Shen, P.-T. Yap, Y. Fan, J.-Z. Cheng, H. An, L.L. Wald, G. Gerig, J.H. Gilmore, W. Lin. In Human Brain Mapping HBM, Vol. 32, No. 3, Note: ePub 5 Aug 2010, pp. 382--396. March, 2011.
DOI: 10.1002/hbm.21023
PubMed ID: 20690143

Multi-scale Series Contextual Model for Image Parsing
SCI Technical Report, M. Seyedhosseini, A.R.C. Paiva, T. Tasdizen. No. UUSCI-2011-004, SCI Institute, University of Utah, 2011.

Exploring the Retinal Connectome
J.R. Anderson, B.W. Jones, C.B. Watt, M.V. Shaw, J.-H. Yang, D. DeMill, J.S. Lauritzen, Y. Lin, K.D. Rapp, D. Mastronarde, P. Koshevoy, B. Grimm, T. Tasdizen, R.T. Whitaker, R.E. Marc. In Molecular Vision, Vol. 17, pp. 355--379. 2011.
PubMed ID: 21311605

Purpose: A connectome is a comprehensive description of synaptic connectivity for a neural domain. Our goal was to produce a connectome data set for the inner plexiform layer of the mammalian retina. This paper describes our first retinal connectome, validates the method, and provides key initial findings.

Methods: We acquired and assembled a 16.5 terabyte connectome data set RC1 for the rabbit retina at .2 nm resolution using automated transmission electron microscope imaging, automated mosaicking, and automated volume registration. RC1 represents a column of tissue 0.25 mm in diameter, spanning the inner nuclear, inner plexiform, and ganglion cell layers. To enhance ultrastructural tracing, we included molecular markers for 4-aminobutyrate (GABA), glutamate, glycine, taurine, glutamine, and the in vivo activity marker, 1-amino-4-guanidobutane. This enabled us to distinguish GABAergic and glycinergic amacrine cells; to identify ON bipolar cells coupled to glycinergic cells; and to discriminate different kinds of bipolar, amacrine, and ganglion cells based on their molecular signatures and activity. The data set was explored and annotated with Viking, our multiuser navigation tool. Annotations were exported to additional applications to render cells, visualize network graphs, and query the database.

Results: Exploration of RC1 showed that the 2 nm resolution readily recapitulated well known connections and revealed several new features of retinal organization: (1) The well known AII amacrine cell pathway displayed more complexity than previously reported, with no less than 17 distinct signaling modes, including ribbon synapse inputs from OFF bipolar cells, wide-field ON cone bipolar cells and rod bipolar cells, and extensive input from cone-pathway amacrine cells. (2) The axons of most cone bipolar cells formed a distinct signal integration compartment, with ON cone bipolar cell axonal synapses targeting diverse cell types. Both ON and OFF bipolar cells receive axonal veto synapses. (3) Chains of conventional synapses were very common, with intercalated glycinergic-GABAergic chains and very long chains associated with starburst amacrine cells. Glycinergic amacrine cells clearly play a major role in ON-OFF crossover inhibition. (4) Molecular and excitation mapping clearly segregates ultrastructurally defined bipolar cell groups into different response clusters. (5) Finally, low-resolution electron or optical imaging cannot reliably map synaptic connections by process geometry, as adjacency without synaptic contact is abundant in the retina. Only direct visualization of synapses and gap junctions suffices.

Conclusions: Connectome assembly and analysis using conventional transmission electron microscopy is now practical for network discovery. Our surveys of volume RC1 demonstrate that previously studied systems such as the AII amacrine cell network involve more network motifs than previously known. The AII network, primarily considered a scotopic pathway, clearly derives ribbon synapse input from photopic ON and OFF cone bipolar cell networks and extensive photopic GABAergic amacrine cell inputs. Further, bipolar cells show extensive inputs and outputs along their axons, similar to multistratified nonmammalian bipolar cells. Physiologic evidence of significant ON-OFF channel crossover is strongly supported by our anatomic data, showing alternating glycine-to-GABA paths. Long chains of amacrine cell networks likely arise from homocellular GABAergic synapses between starburst amacrine cells. Deeper analysis of RC1 offers the opportunity for more complete descriptions of specific networks.

Keywords: neuroscience, retina, vision, blindness, visus, crcns

Trace Driven Registration of Neuron Confocal Microscopy Stacks
L. Hogrebe, A. Paiva, E. Jurrus, C. Christensen, M. Bridge, J.R. Korenberg, T. Tasdizen. In IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1345--1348. 2011.
DOI: 10.1109/ISBI.2011.5872649

The Viking Viewer: Scalable Multiuser Annotation and Summarization of Large Volume Datasets
J.R. Anderson, B.C. Grimm, S. Mohammed, B.W. Jones, T. Tasdizen, J. Spaltenstein, P. Koshevoy, R.T. Whitaker, R.E. Marc. In Journal of Microscopy, Vol. 241, No. 1, pp. 13--28. 2010.
DOI: 10.1111/j.1365-2818.2010.03402.x

Edge enhanced spatio-temporal constrained reconstruction of undersampled dynamic contrast enhanced radial MRI
S.K. Iyer, E. DiBella, T. Tasdizen. In IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, pp. 704--707. 2010.
DOI: 10.1109/ISBI.2010.5490077

Reconstruction of 3D Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Nonlocal Means
G. Adluru, T. Tasdizen, M.C. Schabel, E.V.R. DiBella. In Journal of Magnetic Resonance Imaging, Vol. 32, pp. 1217--1227. 2010.
DOI: 10.1002/jmri.22358

Improving Undersampled MRI Reconstruction Using Non-Local Means
G. Adluru, T. Tasdizen, R. Whitaker, E. DiBella. In Proceedings of the 2010 International Conference on Pattern Recognition, pp. 4000--4003. 2010.
DOI: 10.1109/ICPR.2010.973

Metrics for Uncertainty Analysis and Visualization of Diffusion Tensor Images
F. Jiao, J.M. Phillips, J.G. Stinstra, J. Kueger, R. Varma, E. Hsu, J. Korenberg, C.R. Johnson. In Proceedings of the 5th international conference on Medical imaging and augmented reality (MIAR), Beijing, China, Springer-Verlag, Berlin, Heidelberg pp. 179--190. September, 2010.

POCS-enhanced correction of motion artifacts in parallel MRI
A.A. Samsonov, J.V. Velikina, Y.K. Jung, E.G. Kholmovski, C.R. Johnson, W.F. Block. In Magnetic Resonance in Medicine, Vol. 63, No. 4, pp. 1104--1110. May, 2010.

Brain volumes in psychotic youth with schizophrenia and mood disorders
M. El-Sayed, R.G. Steen, M.D. Poe, T.C. Bethea, G. Gerig, J. Lieberman, L. Sikich. In Journal of Psychiatry and Neuroscience, Vol. 35, No. 4, pp. 229--236. July, 2010.
PubMed ID: 20569649

Deficits in gray matter volume in psychotic youth with schizophrenia-spectrum disorders are not evident in psychotic youth with mood disorders
M. El-Sayed, R.G. Steen, M.D. Poe, T.C. Bethea, G. Gerig, J. Lieberman, L. Sikich. In J Psychiatry Neurosci, July, 2010.

Detection of Neuron Membranes in Electron Microscopy Images Using a Serial Neural Network Architecture,
E. Jurrus, A.R.C. Paiva, S. Watanabe, J.R. Anderson, B.W. Jones, R.T. Whitaker, E.M. Jorgensen, R.E. Marc, T. Tasdizen. In Medical Image Analysis, Vol. 14, No. 6, pp. 770--783. 2010.
DOI: 10.1016/
PubMed ID: 20598935

Prenatal and Neonatal Brain Structure and White Matter Maturation in Children at High Risk for Schizophrenia
J.H. Gilmore, C. Kang, D.D. Evans, H.M. Wolfe, M.D. Smith, J.A. Lieberman, W. Lin, R.M. Hamer, M. Styner, G. Gerig. In American Journal of Psychiatry, Vol. 167, No. 9, Note: Epub 2010 Jun 1, pp. 1083--1091. September, 2010.
PubMed ID: 20516153

Multi-object analysis of volume, pose, and shape using statistical discrimination
K. Gorczowski, M. Styner, J.Y. Jeong, J.S. Marron, J. Piven, H.C. Hazlett, S.M. Pizer, G. Gerig. In IEEE Trans Pattern Anal Mach Intell., Vol. 32, No. 4, pp. 652--661. April, 2010.
DOI: 10.1109/TPAMI.2009.92
PubMed ID: 20224121

Genetic and environmental contributions to neonatal brain structure: A twin study
J.H. Gilmore, J.E. Schmitt, R.C. Knickmeyer, J.K. Smith, W. Lin, M. Styner, G. Gerig, M.C. Neale. In Human Brain Mapping, Vol. 31, No. 8, Note: ePub 8 Jan 2010, pp. 1174--1182. 2010.
PubMed ID: 20063301

Principal neighborhood dictionaries for nonlocal means image denoising
T. Tasdizen. In IEEE Transactions on Image Processing, Vol. 18, No. 12, Note: Epub 2009 Jul 24, pp. 2649--2660. 2009.
PubMed ID: 19635697

Serial Neural Network Classifier for Membrane Detection using a Filter Bank
E. Jurrus, A.R.C. Paiva, S. Watanabe, R. Whitaker, E.M. Jorgensen, T. Tasdizen. In Proc. Workshop on Microscopic Image Analysis with Applications in Biology, Bethesda, MD, USA, 2009.

Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples
R.S. MacLeod, J.G. Stinstra, S. Lew, R.T. Whitaker, D.J. Swenson, M.J. Cole, J. Krüger, D.H. Brooks, C.R. Johnson. In Philosophical Transactions of The Royal Society A, Mathematical, Physical & Engineering Sciences, Vol. 367, No. 1896, pp. 2293--2310. 2009.