SCI Publications
2010
J.R. Anderson, B.C. Grimm, S. Mohammed, B.W. Jones, T. Tasdizen, J. Spaltenstein, P. Koshevoy, R.T. Whitaker, R.E. Marc.
“The Viking Viewer: Scalable Multiuser Annotation and Summarization of Large Volume Datasets,” In Journal of Microscopy, Vol. 241, No. 1, pp. 13--28. 2010.
DOI: 10.1111/j.1365-2818.2010.03402.x
S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R.T. Whitaker, the Alzheimers Disease Neuroimaging Initiative (ADNI).
“Manifold modeling for brain population analysis,” In Medical Image Analysis, Special Issue on the 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009, Vol. 14, No. 5, Note: Awarded MICCAI 2010, Best of the Journal Issue Award, pp. 643--653. 2010.
ISSN: 1361-8415
DOI: 10.1016/j.media.2010.05.008
PubMed ID: 20579930
S.K. Iyer, E. DiBella, T. Tasdizen.
“Edge enhanced spatio-temporal constrained reconstruction of undersampled dynamic contrast enhanced radial MRI,” In IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, pp. 704--707. 2010.
DOI: 10.1109/ISBI.2010.5490077
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.
“Detection of Neuron Membranes in Electron Microscopy Images Using a Serial Neural Network Architecture,” In Medical Image Analysis, Vol. 14, No. 6, pp. 770--783. 2010.
DOI: 10.1016/j.media.2010.06.002
PubMed ID: 20598935
A.R.C. Paiva, T. Tasdizen.
“Fast Semi-Supervised Image Segmentation by Novelty Selection,” In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, Texas, pp. 1054--1057. March, 2010.
DOI: 10.1109/ICASSP.2010.5495333
A.R.C. Paiva, T. Tasdizen.
“Detection of Salient Image Points using Manifold Structure,” In Proc. IEEE Intl. Conference on Pattern Recognition, Istanbul, Turkey, pp. 1389--1392. 2010.
DOI: 10.1109/ICPR.2010.343
A.R.C. Paiva, E. Jurrus, T. Tasdizen.
“Using Sequential Context for Image Analysis,” In Proc. IEEE Intl. Conference on Pattern Recognition, Istanbul, Turkey, pp. 2800--2803. 2010.
DOI: 10.1109/ICPR.2010.686
C. Schlimper, O. Nemitz, U. Dorenbeck, J. Scorzin, R.T. Whitaker, T. Tasdizen, M. Rumpf, K. Schaller.
“Restoring three-dimensional magnetic resonance angiography images with mean curvature motion,” In Neurological Research, Vol. 32, No. 1, Note: Epub 2009 Nov 26, pp. 87--93. February, 2010.
DOI: 10.1179/016164110X12556180206077
PubMed ID: 19941735
M. Seyedhosseini, A.R.C. Paiva, T. Tasdizen.
“Image Parsing with a Three-State Series Neural Network Classifier,” In Proc. IEEE Intl. Conference on Pattern Recognition, Istanbul, Turkey, pp. 4508--4511. 2010.
DOI: 10.1109/ICPR.2010.1095
T. Tasdizen, P. Koshevoy, B.C. Grimm, J.R. Anderson, B.W. Jones, C.B. Watt, R.T. Whitaker, R.E. Marc.
“Automatic mosaicking and volume assembly for high-throughput serial-section transmission electron microscopy,” In Journal of Neuroscience Methods, Vol. 193, No. 1, pp. 132--144. 2010.
PubMed ID: 20713087
2009
J.R. Anderson, B.W. Jones, J.-H. Yang, M.V. Shaw, C.B. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, Kannan U.V., R.T. Whitaker, D. Mastronarde, T. Tasdizen, R.E. Marc.
“A Computational Framework for Ultrastructural Mapping of Neural Circuitry,” In PLoS Biology, Vol. 7, No. 3, pp. e74. 2009.
PubMed ID: 19855814
J. Anderson, B. Jones, J. Yang, M. Shaw, C. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, Kannan U.V., R.T. Whitaker, D. Mastronarde, T. Tasdizen, R. Marc.
“Ultra Structural Mapping of Neural Circuitry: A Computational Framework,” In IEEE International Symposium on Biomedical Engineering (ISBI 2009), pp. 1135--1137. 2009.
DOI: 10.1109/ISBI.2009.5193257
Complete mapping of neuronal networks requires data acquisition at synaptic resolution with canonical coverage of tissues and robust neuronal classification. Transmission electron microscopy (TEM) remains the optimal tool for network mapping. However, capturing high resolution, large, serial section TEM (ssTEM) image volumes is complicated by the need to precisely mosaic distorted image tiles and subsequently register distorted mosaics. Moreover, most cell or tissue class markers are not optimized for TEM imaging. We present a complete framework for neuronal reconstruction at ultrastructural resolution, allowing the elucidation of complete neuronal circuits. This workflow combines TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration and terabyte-scale image browsing for volume annotation. Networks that previously would require decades of assembly can now be completed in months, enabling large-scale connectivity analyses of both new and legacy data. Additionally, these approaches can be extended to other tissue or biological network systems.
Keywords: crcns, neural circuitry
S. Gerber, T. Tasdizen, S. Joshi, R.T. Whitaker.
“On the Manifold Structure of the Space of Brain Images,” In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009), Springer, pp. 305--312. 2009.
DOI: 10.1007/978-3-642-04268-3_38
PubMed ID: 20426001
S. Gerber, T. Tasdizen, R.T. Whitaker.
“Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds,” In Proceedings of the 2009 International Conference on Computer Vison (ICCV 2009), pp. 529--536. September, 2009.
ISSN: 1550-5499
DOI: 10.1109/ICCV.2009.5459193
We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represented as a parametrized surface represented by a set of parameters that are defined on the input samples. The representation also provides a natural mapping from high to low dimensional space, and a concatenation of these two mappings induces a projection operator onto the manifold. The explicit projection operator allows for a clearly defined objective function in terms of projection distance and reconstruction error. A formulation of the mappings in terms of kernel regression permits a direct optimization of the objective function and the extremal points converge to principal surfaces as the number of data to learn from increases. Principal surfaces have the desirable property that they, informally speaking, pass through the middle of a distribution. We provide a proof on the convergence to principal surfaces and illustrate the effectiveness of the proposed approach on synthetic and real data sets.
E. Jurrus, M. Hardy, T. Tasdizen, P.T. Fletcher, P. Koshevoy, C.-B. Chien, W. Denk, R.T. Whitaker.
“Axon Tracking in Serial Block-Face Scanning Electron Microscopy,” In Medical Image Analysis (MEDIA), Vol. 13, No. 1, Elsevier, pp. 180--188. February, 2009.
PubMed ID: 18617436
E. Jurrus, A.R.C. Paiva, S. Watanabe, R.T. Whitaker, E.M. Jorgensen, T. Tasdizen.
“Serial Neural Network Classifier for Membrane Detection using a Filter Bank.,” SCI Technical Report, No. UUSCI-2009-006, SCI Institute, University of Utah, 2009.
E. Jurrus, A.R.C. Paiva, S. Watanabe, R. Whitaker, E.M. Jorgensen, T. Tasdizen.
“Serial Neural Network Classifier for Membrane Detection using a Filter Bank,” In Proc. Workshop on Microscopic Image Analysis with Applications in Biology, Bethesda, MD, USA, 2009.
J.S. Preston, T. Tasdizen, C.M. Terry, A.K. Cheung, R.M. Kirby.
“Using the stochastic collocation method for the uncertainty quantification of drug concentration due to depot shape variability,” In IEEE Transactions on Biomedical Engineering, Vol. 56, No. 3, Note: Epub 2008 Dec 2, pp. 609--620. 2009.
PubMed ID: 19272865
T. Tasdizen.
“Principal neighborhood dictionaries for nonlocal means image denoising,” In IEEE Transactions on Image Processing, Vol. 18, No. 12, Note: Epub 2009 Jul 24, pp. 2649--2660. 2009.
PubMed ID: 19635697
Kannan U.V., A.R.C. Paiva, E. Jurrus, T. Tasdizen.
“Automatic Markup of Neural Cell Membranes Using Boosted Decision Stumps,” In Proceedings of the IEEE International Symposium on Biomedical Engineering (ISBI 2009), Boston, MA, pp. 1039--1042. 2009.
DOI: 10.1109/ISBI.2009.5193233
To better understand the central nervous system, neurobiologists need to reconstruct the underlying neural circuitry from electron microscopy images. One of the necessary tasks is to segment the individual neurons. For this purpose, we propose a supervised learning approach to detect the cell membranes. The classifier was trained using AdaBoost, on local and context features. The features were selected to highlight the line characteristics of cell membranes. It is shown that using features from context positions allows for more information to be utilized in the classification. Together with the nonlinear discrimination ability of the AdaBoost classifier, this results in clearly noticeable improvements over previously used methods.
Keywords: crcns, neural networks