Tolga Tasdizen portrait
  • Adjunct Professor, School Of Computing
  • Professor, Elect & Computer Engineering
  • Research Assistant Professor, School Of Computing

Publications

  • QC Nguyen, T Belnap, P Dwivedi, AHN Deligani, A Kumar, D Li, RT Whitaker, J Keralis, H Mane, X Xue, TT Nguyen, T Tasdizen & KD Brunisholz (2022). Google Street View Images as Predictors of Patient Health Outcomes, 2017–2019. Special Issue: Machine and Deep Learning in Computer Vision Applications, Big Data Cogn. Comput. Published, 03/2022.
  • Cuong Ly , CA Nizinski, A Toydemir, C Vachet, LW McDonald & Tolga Tasdizen (2021). Determining the Composition of a Mixed Material with Synthetic Data. Microscopy and Microanalysis, Cambridge University Press. Published, 12/2021.
  • TT Nguyễn, QC Nguyen, A Rubinsky, T Tasdizen, AHN Deligani, P Dwivedi, R Whitaker, JD Fields, MC DeRouen, H Mane, C Lyles, K Brunisholz & K Bibbins-Domingo (2021). Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes. Int J Environ Res Public Health. Published, 10/2021.
  • Cody Nizinski, Cuong Ly, Luther MacDonald & Tolga Tasdizen (2021). Computational Image Techniques for Analyzing Lanthanide and Actinide Morphology. ACS Symposium Series; American Chemical Society. Published, 10/2021.
  • Cuong Ly, CA Nizinski, C Vachet, LW McDonald & T Tasdizen (2021). Learning to estimate the composition of a mixture with synthetic data. Microscopy and Microanalysis, Cambridge University Press. Published, 08/2021.
  • Nicholas Petrick,, BreastPathQ Challenge Group & Tolga Tasdizen (2021). SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment. J. Med. Imag. Published, 05/2021.
  • N Ramesh / M Chen & T Tasdizen (2021). Detection and Segmentation in Microscopy Images. (pp. 43-72). Elsevier. Published, 01/2021.
  • V Keshavarzzadeh, M Alirezai, T Tasdizen & R Kirby (2021). Image-Based Multiresolution Topology Optimization using Deep Disjunctive Normal Shape Model,. Computer-Aided Design. Published, 01/2021.
  • RB Lanfredi, J Schroeder, C Vachet & T Tasdizen (2020). Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields. MICCAI. Published, 10/2020.
  • C Nizinski, B Fullmer, N Mecham, T Tasdizen & L McDonald IV (2020). Effects of process history on the surface morphology of uranium ore concentrates extracted from ore. Minerals Engineering. Published, 09/2020.
  • Z Wu, J Wei, W Yuan, J Wang & T Tasdizen (2020). Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy. 24th European Conference on Artificial Intelligence. Published, 09/2020.
  • Yuan W, Wei J, Wang J, Ma Q & Tasdizen T (2020). Unified generative adversarial networks for multimodal segmentation from unpaired 3D medical images. Medical image analysis. Vol. 64, 101731. Published, 08/01/2020.
  • J Tate, J Aguiar, M Gong & T Tasdizen (2020). High Throughput Crystal Structure Classification. Microscopy and Microanalysis, Cambridge University. Published, 08/2020.
  • M Hosseini, A Umunnakwe, M Parvania & T Tasdizen (2020). Intelligent Damage Classification and Estimation in Power Distribution Poles Using Unmanned Aerial Vehicles and Convolutional Neural Networks. IEEE Transactions on Smart Grid. Published, 07/2020.
  • C Ly, C Vachet, I Schwerdt, E Abbott, A Brenkmann, L McDonald IV & T Tasdizen (2020). Determining Uranium Ore Concentrates and Their Calcinatiosn Products via Image Classification of Multiple Magnifications. Journal of Nuclear Materials. Published, 03/2020.
  • JA Aguiar , ML Gong & T Tasdizen (2020). Crystallographic prediction from diffraction and chemistry data for higher throughput classification using machine learning. Computational Materials Science (Elsevier). Published, 02/2020.
  • Mehran Javanmardi, D Huang, P Dviwedi, S Khanna , K Brunisholz, R Whitaker, Q Nguyen & T Tasdizen (2019). Analyzing Associations Between Chronic Disease Prevalence and Neighborhood Quality Through Google Street View Images. IEEE Access. Published, 12/16/2019.
  • E Erdil, S Yildirim, T Tasdizen & M Cetin (2019). Pseudo-marginal MCMC Sampling for Image Segmentation using Nonparametric Shape Prior. IEEEE Trans Image Processing. Published, 11/2019.
  • JA Aguiar , ML Gong, D Masiel , B Reed, B Miller & T Tasdizen (2019). Decoding Crystallography from High Resolution Electron Imaging and Diffraction Datasets with Deep learning. Science Advances. Published, 10/2019.
  • W Yuan, J Wei, J Wang, Q Ma & T Tasdizen (2019). Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation with Multimodal Unpaired Medical Image. MICCAI. Published, 10/2019.
  • RB Lanfredi, JD Schroeder, C Vachet & T Tasdizen (2019). Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays. MICCAI. Published, 10/2019.
  • N Ramesh & T Tasdizen (2019). Cell segmentation using multi-task learning with a convolutional neural networks. IEEE Journal of Biomedical and Health Informatics. Published, 07/2019.
  • A Hanson, R Lee, C Vachet , I Schwerdt, T Tasdizen & LW McDonald (2019). Quantifying Impurity Effects on the Surface Morphology of U3O8, Analytical Chemistry. ACS Analytical Chemistry. Published, 06/28/2019.
  • Q Nguyen, S Khanna, P Dwivedi, D Huang, Y Huang, T Tasdizen, K Brunisholz, F Li, W Gorman, TX Nguyen & C Jiang (2019). Using Google Street View to Examine Associations between Built Environment Characteristics and U.S. Health Outcomes. Elsevier Preventive Medicine Reports. Published, 06/2019.
  • E Erdil, AO Argunsah, T Tasdizen, D Unay & M Cetin (2019). Combining Nonparametric Spatial Context Priors With Nonparametric Shape Priors For Dendritic Spine Segmentation In 2-Photon Microscopy Images. IEEE ISBI. Published, 05/2019.
  • ST Heffernan, N-C Ly, BJ Mowera, C Vachet, IJ Schwerdt, LW McDonald & T Tasdizen (2019). Identifying Surface Morphological Characteristics to Differentiate Between Mixtures of U3O8 Synthesized from Ammonium Diuranate and Uranyl Peroxide. Radiochimica Acta. Published, 05/2019.
  • EC Abbott, A Brenkmann, C Galbraith, J Ong, IJ Schwerdt, B Albrecht, T Tasdizen & LW McDonald (2019). Dependence of U O2 Surface Morphology on Synthetic Route. Radiochimica Acta. Published, 04/2019.
  • N-C Ly, I Schwerdt, A Olsen, R Porter, K Sentz, LW McDonald & T Tasdizen (2019). A New Approach for Quantifying Morphological Features of U3O8 for Nuclear Forensics using A Deep Learning Model, Journal of Nuclear Materials. Elsevier Journal of Nuclear Materials. Published, 02/2019.
  • Nisha Ramesh & Tolga Tasdizen (2018). Cell segmentation using multi-task learning with a convolutional neural networks. IEEE Journal of Biomedical and Health Informatics. Published, 12/2018.
  • M Gong, SJ Yoon, RR Unocic, H Ishii, JP Bradley, BD Miller, D Masiel, B Reed, T Tasdizen & JA Aguiar (2018). Pioneering the use of Neural Network Architectures and Feature Engineering for Real-Time Augmented Microscopy and Analysis, Microscopy and Microanalysis. Microscopy and Microanalysis. Published, 08/2018.
  • IJ Schwerdt, A Brenkmann, S Martinson, BD Albrecht, S Heffernan, MR Klosterman, T Kirkham, T Tasdizen & L McDonald (2018). Nuclear proliferomics: A new field of study to identify signatures of nuclear materials as demonstrated on alpha-UO3. Elsevier Talanta. Published, 08/2018.
  • M Javanmardi (2018). Image Segmentation by Deep Learning of Disjunctive Normal Shape Model Shape Representation. CVPR 4th International Workshop on Differential Geometry in Computer Vision and Machine Learning. Published, 06/2018.
  • M Javanmardi & T Tasdizen (2018). Domain Adaptation for Biomedical Image Segmentation using Adversarial Training. ISBI. Published, 05/2018.
  • N Ramesh & T Tasdizen (2018). Semi-Supervised Learning For Cell Tracking In Microscopy Images. ISBI. Published, 05/2018.
  • T Tasdizen, M Sajjadi, M Javanmardi & N Ramesh (2018). Improving the robustness of convolutional networks to appearance variability in biomedical images. ISBI. Published, 05/2018.
  • Q Nguyen, T Nguyen, W Yu, M Pham, M McCullough, H-W Meng, M Wen, F Li, K Smith, K Brunisholz, M Sajjadi & T Tasdizen (2018). Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects Research. J Epidemiol Community Health. Published, 03/01/2018.
  • D Ayyagari, N Ramesh, D Yatsenko, T Tasdizen & C Atrria (2018). Image reconstruction using priors from deep learning. SPIE Medical Imaging. Published, 02/2018.
  • E Erdil, S Yildirim, T Tasdizen & M Cetin (2017). Image Segmentation with Pseudo-marginal MCMC Sampling and Nonparametric Shape Priors. AABI. Published, 12/01/2017.
  • Q Nguyen & T Tasdizen (2017). Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects Research. American Public Health Association. Published, 11/2017.
  • E Erdil, F Mesadi, T Tasdizen & M Cetin (2017). Disjunctive Normal Shape Boltzmann Machine. ICASSP. Published, 03/01/2017.
  • M Sajjadi, M Javanmardi and T Tasdizen, Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning, NIPS 2016. Published, 12/2016.
  • MU Ghani, SD Kanik, AO Argunsah, A Hobbiss, I Israely, D Unay, F Mesadi, T Tasdizen and M Cetin, Dendritic Spine Classification using Shape and Appearance Features based on Two-Photon Microscopy, J Neuroscience Methods, December 2016. Published, 12/2016.
  • M Sajjadi, SM Seyedhosseini and T Tasdizen, Disjunctive Normal Networks, accepted, 218(19):276–285, Neurocomputing, December 2016. Published, 12/2016.
  • T Liu, M Zhang, M Javanmardi , N Ramesh and T Tasdizen, SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation, ECCV 2016. Published, 10/2016.
  • MU Ghani, E Erdil, SD Kanik, AO Argunsah, A Hobbiss, I Israely, D Unay, T Tasdizen and M Cetin, Dendritic Spine Shape Analysis: A Clustering Perspective, ECCV BioImage Computing Workshop, 2016. Published, 10/2016.
  • T Liu, SM Seyedhosseini and T Tasdizen, Image Segmentation Using Hierarchical Merge Tree, 25(10): 4596--4607, IEEE Trans Image Processing, October 2016. Published, 10/2016.
  • F Mesadi, M Cetin and T Tasdizen, Disjunctive Normal Level Set: An Efficient Parametric Implicit Method, ICIP 2016. Published, 09/2016.
  • M Sajjadi, M Javanmardi and T Tasdizen, Mutual exclusivity loss for semi-supervised deep learning, ICIP 2016. Published, 09/2016.
  • E Erdil, S Yildirim, M Cetin and T Tasdizen, MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors, CVPR 2016. Published, 06/2016.
  • M Elwardy, T Tasdizen and M Cetin, Disjunctive Normal Unsupervised LDA for P300-based Brain-Computer Interfaces, MLUB 2016. Published, 05/2016.
  • Seyedhosseini M and T Tasdizen. Semantic Image Segmentation with Contextual Hierarchical Models. IEEE transactions on pattern analysis and machine intelligence. Published, 05/2016.
  • E Erdil, L Rada, AO Argunsah, D Unay, T Tasdizen and M Cetin, Joint Nonparametric Shape And Feature Density Estimation For Segmentation Of Dendritic Spines, ISBI 2016. Published, 04/2016.
  • MU Ghani, AO Argunsah, I Israely, D Unay, T Tasdizen and M Cetin, On Comparison Of Manifold Learning Techniques For Dendritic Spine Classification, ISBI 2016. Published, 04/2016.
  • MU Ghani, F Mesadi, SD Kanik, AO Argunsah, I Israely, D Unay, T Tasdizen and M Cetin, Dendritic Spine Shape Analysis Using Disjunctive Normal Shape Models, ISBI 2016. Published, 04/2016.
  • SK Iyer, T Tasdizen, D Likhite and EVR DiBella, Split Bregman Multicoil Accelerated Reconstruction Technique (SMART): A new framework for rapid reconstruction of cardiac perfusion MRI, Medical Physics, 43(4):1969--1981, April 2016. Published, 04/2016.
  • M Barjatia, T Tasdizen, B Song and KM Golden, Network Modeling of Arctic Melt Ponds, Cold Regions Science and Technology, 124:40-53, April 2016. Published, 04/2016.
  • I Arganda-Carreras, SC Turaga, DR Berger, D Ciresan, A Giusti, LM Gambardella, J Schmidhuber, D Laptev, S Dwivedi, J Buhmann, T Liu, M Seyedhosseini, T Tasdizen, L Kamentsky, R Burget, V Uher, X Tan, C Sun, TD Pham, E Bas, MG Uzunbas, A Cardona, J Schindelin, HS Seung, Electron Microscopy Challenge: Crowdsourcing the creation of machine intelligence for connectomics, Frontiers in Neuroanatomy, 9:00142. Published, 11/2015.
  • Arganda-Carreras I, Turaga SC, Berger DR, Cireşan D, Giusti A, Gambardella LM, Schmidhuber J, Laptev D, Dwivedi S, Buhmann JM, Liu T, Seyedhosseini M, Tasdizen T, Kamentsky L, Burget R, Uher V, Tan X, Sun C, Pham TD, Bas E, Uzunbas MG, Cardona A, Schindelin J & Seung HS (2015). Crowdsourcing the creation of image segmentation algorithms for connectomics. Frontiers in neuroanatomy. Vol. 9, 142. Published, 11/01/2015.
  • F Mesadi, M Cetin and T Tasdizen, Disjunctive Normal Shape and Appearance Priors with Applications to Image Segmentation, MICCAI 2015. Published, 10/05/2015.
  • M Sajjadi, SM Seyedhosseini and T Tasdizen, Nonlinear regression with logistic product basis networks, 22:8, pp 1011–1015, IEEE Signal Processing Letters, August 2015. Published, 08/2015.
  • MU Ghani, SD Kanik, AO Argunsah, T Tasdizen, D Unay and M Cetin, Dendritic Spine Shape Classification from Two-Photon Microscopy Images, SIU 2015. Published, 05/16/2015.
  • I Yilmaz, SD Kanik, T Tasdizen and M Cetin, Semi-supervised Adaptation of Motor Imagery Based BCI Systems, SIU 2015. Published, 05/16/2015.
  • E Erdil, AO Argunsah, T Tasdizen, D Unay and M Cetin, A Joint Classification And Segmentation Approach For Dendritic Spine Segmentation In 2-Photon Microscopy Images, ISBI 2015. Published, 04/16/2015.
  • N Ramesh, F Mesadi, M Cetin and T Tasdizen, Disjunctive Normal Shape Model, ISBI 2015. Published, 04/16/2015.
  • Jones C, Liu T, Cohan NW, Ellisman M & Tasdizen T (2015). Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images. Journal of neuroscience methods. Vol. 246, 13-21. Published, 04/01/2015.
  • Seyedhosseini M, Shushruth S, Davis T, Ichida JM, House PA, Greger B, Angelucci A & Tasdizen T (2015). Informative features of local field potential signals in primary visual cortex during natural image stimulation. Journal of neurophysiology. Vol. 113, 1520-32. Published, 03/01/2015.
  • SM Seyedhosseini and T Tasdizen, Disjunctive Normal Random Forests, Pattern Recognition 48:3, pp 976–983, March 2015. Published, 03/2015.
  • AJ Perez, SM Seyedhosseini, TJ Deerinck, EA Bushong, T Tasdizen and MH Ellisman, A Workflow for the Automatic Segmentation of Organelles in Electron Microscopy Image Stacks, Frontiers in Neuroanatomy, 8:126, 2014. Published, 11/2014.
  • T Liu, C Jones, SM Seyedhosseini and T Tasdizen, A Modular Hierarchical Approach to 3D Electron Microscopy Image Segmentation, J Neuroscience Methods, 226, pp. 88-102, 2014. Published, 04/2014.
  • N Ramesh and T Tasdizen, Cell Tracking Using Particle Filters With Implicit Convex Shape Model In 4D Confocal Microscopy Images, submitted to ICIP 2014. Published, 02/2014.
  • T Tasdizen, SM Seyedhosseini, T Liu, C Jones and E Jurrus, Image segmentation for connectomics using machine learning, in Computational Intelligence in Biomedical Imaging, pp 237–278, ed. K Suzuki, Springer New York, 2014. Published, 01/2014.
  • SM Seyedhosseini, M Sajjadi and T Tasdizen, Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks, ICCV 2013. Published, 12/2013.
  • SM Seyedhosseini, MH Ellisman and T Tasdizen, Multi-Class Multi-Scale Series Contextual Model for Image Segmentation, IEEE Trans Image Processing, 22:11 pp. 4486–4496, November 2013. Published, 11/2013.
  • E. Jurrus, S. Watanabe, A. R. C. Paiva, M. Ellisman, E. M. Jorgensen and T. Tasdizen, Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images, Neuroinformatics. 2013 Jan;11(1):5-29. Published, 01/2013.
  • SK Iyer, T Tasdizen, N Burgon, G Adluru and E Dibella, Fast Reconstruction of 3D LGE Images of the Left Atrium in a Compressed Sensing Framework using Split Bregman, accepted to ISMRM 2013. Published, 01/2013.
  • SM Seyedhosseini, RJ Giuly, MH Ellisman and T Tasdizen, Segmentation of Mitochondria In Electron Microscopy Images Using Algebraic Curves, accepted to ISBI 2013. Published, 01/2013.
  • T Liu, SM Seyedhosseini, MH Ellisman and T Tasdizen, Watershed Merge Forest Classification For Electron Microscopy Image Stack Segmentation, submitted to ICIP 2013. Published, 01/2013.
  • N Ramesh and T Tasdizen, Three-Dimensional Alignment Of Confocal Microscopy Stacks, submitted to ICIP 2013. Published, 01/2013.
  • C Jones, SM Seyedhosseini, MH Ellisman and T Tasdizen, Neuron Segmentation In Em Images Using Partial Differential Equations, accepted to ISBI 2013. Published, 01/2013.
  • C Jones, T Liu, MH Ellisman and T Tasdizen, Semi-Automatic Neuron Segmentation In Em Images Via Sparse Labeling, ISBI 2013. Published, 01/2013.
  • T Liu, S. M. Seyedhosseini, E Jurrus, MH Ellisman and T Tasdizen, Watershed Merge Tree Classification for Electron Microscopy Image Segmentation, ICPR 2012. Published, 09/2012.
  • ARC Paiva and T Tasdizen, Fingerprint Image Segmentation using Data Manifold Characteristic Features, International Journal of Pattern Recognition and Artificial Intelligence, 26:4, pp 12560, 2012. Published, 06/2012.
  • L. Hogrebe, A. R.C. Paiva, E. Jurrus, C. Christensen, M. Bridge, J.R. Korenberg, P. R. Hof, B. Roysam, T. Tasdizen, Serial Section Registration of Axonal Confocal Microscopy Datasets for Long Range Neural Circuit Reconstruction, J Neuroscience methods, 207, pp. 200-210, 2012. Published, 06/2012.
  • T Tasdizen, T Liu, SM Seyedhosseini, E Jurrus and M Ellisman, Neuron Segmentation in Electron Microscopy Images, MASFOR 2012. Published, 06/2012.
  • T Liu, S. M. Seyedhosseini, E Jurrus and T Tasdizen, Neuron Segmentation in EM Images using Series of Classifiers and Watershed Tree, ISBI EM Segmentation Challenge Workshop, 2012. Published, 05/2012.
  • N Ramesh, ME Salama and T Tasdizen, Segmentation of Haematopoeitic Cells in Bone Marrow Using Circle Detection and Splitting Techniques, ISBI 2012. Published, 05/2012.
  • SK Iyer, T Tasdizen, G Adluru and E DiBella, A Block Reordering Technique in a Compressed Sensing Framework, ISMRM 2012. Published, 05/2012.
  • S. K. Iyer, T. Tasdizen and E. V. R. DiBella, Edge Enhanced Spatio-Temporal Constrained Reconstruction of Undersampled Dynamic Contrast Enhanced Radial MRI, Magnetic Resonance Imaging 30, pp. 610-619, 2012. Published, 01/2012.
  • N Ramesh, BJ Dangott, M Salama and T Tasdizen, Segmentation and Two-Step Classification of White Blood Cells in Peripheral Blood Smear, Journal of Pathology Informatics 3:13, 2012. Published, 01/2012.
  • S. M. Seyedhosseini, R. Kumar, E. Jurrus, R. Guily, M. Ellisman, H. Pfister and T. Tasdizen, ”Detection of Neuron Membranes in Electron Microscopy Images using Multi-scale Context and Radon-like Features,” Int. Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2011. Published, 09/2011.
  • S. M. Seyedhosseini, A. R. C. Paiva and T. Tasdizen, ”Fast AdaBoost Training using Weighted Novelty Selection,” International Joint Conference on Neural Networks 2011. Published, 08/2011.
  • E Jurrus, S Watanabe, R Guily, ARC Paiva, M Ellisman, E Jorgensen, T Tasdizen, Semi-automated Neuron Boundary Detection and Slice Traversal Algorithm for Segmentation of Neurons from Electron Microscopy Images, Microscopic Image Analysis with Applications in Biology (MIAAB) Workshop, Chicago, August 1, 2011. Published, 08/2011.
  • Z. Leng, J. Korenberg, B. Roysam and T. Tasdizen, ”A Rapid 2-D Centerline Extraction Method Based On Tensor Voting,” accepted to IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, 2011. Published, 04/2011.
  • L. Hogrebe, A. Paiva, E. Jurrus, C. Christensen, M. Bridge, J. Korenberg and T. Tasdizen, ”Trace Driven Registration Of Neuron Confocal Microscopy Stacks,” accepted to IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, 2011. Published, 04/2011.
  • M. L. Berlanga, S. Phan, E. A. Bushong, S. Lamont, S. Wu, O. Kwon, B. S. Phung, M. Terada, T. Tasdizen, E. Martone and M. H. Ellisman, ”Three-dimensional reconstruction of serial mouse brain sections using high-resolution large-scale mosaics,” submitted to Frontiers in Neuroscience Methods. Published, 03/2011.
  • 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. Mastonarde, P. Koshevoy, B. Grimm, T. Tasdizen, R. Whitaker and R. E. Marc, Exploring the Retinal Connectome, Molecular Vision. Published, 02/2011.
  • J. R. Anderson, B. C. Grimm, S. Mohammed, B.W. Jones, T. Tasdizen, J Spaltenstein, P. Koshevoy, R. Whitaker and R.E. Marc, ”The Viking Viewer: Scalable Multiuser Annotation and Summarization of Large Volume Datasets,” Journal of Microscopy, 2010. Published, 01/2011.
  • E. Jurrus and A. R. C. Paiva and S. Watanabe, J. R. Anderson, B. W. Jones, R. T. Whitaker, E. M. Jorgensen, R. E. Marc and T. Tasdizen, ”Detection of Neuron Membranes in Electron Microscopy Images using Auto-context,” Medical Image Analysis, 14:6, pp. 770-783, 2010. Published, 12/2010.
  • G. Adluru, T. Tasdizen, M. Schabel and E. V. R. DiBella, ”Reconstruction of 3D Dynamic Contrast Enhanced MRI using Non-Local Means,” Journal of Magnetic Resonance Imaging, 2010. Published, 11/2010.
  • Samuel Gerber, Tolga Tasdizen, P. Thomas Fletcher, Sarang Joshi, Ross Whitaker and the Alzheimers Disease Neuroimaging Initiative (ADNI), Manifold modeling for brain population analysis, Medical Image Analysis, Volume 14, Issue 5, Special Issue on the 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009, October 2010, Pages 643-653. Best paper of the special issue award. Published, 10/2010.
  • C. Schlimper, O. Nemitz, U. Dorenbeck, J. Scorzin, R. Whitaker, T. Tasdizen, M. Rumpf and K. Schaller, ”Restoring three-dimensional magnetic resonance angiography images with mean curvature motion,” Neurological Research, vol. 32, no. 1, pp. 87-93, February 2010. Published, 02/2010.
  • J. R. Anderon, B. W. Jones, J-H Yang, C. B. Watt, P. Koshevoy, J. Spaltenstein, U. V. Kannan, R. Whitaker, D. Mastronarde, T. Tasdizen and R. E Marc, A Computational Framework for Ultrastructural Mapping of Neural Circuitry, PLoS Biology 7:3, pp. e74. Published, 2009.
  • Tolga Tasdizen, "Principal Neighborhood Dictionaries for Non-local Means Image Denoising," IEEE Trans Image Processing, 18:12 pp. 2649-60. Published, 2009.
  • J.S Preston, T. Tasdizen, C. M. Terry, A. K. Cheung and R. M. Kirby, "Using the Stochastic Collocation Method for the Uncertainty Quantification of Drug Concentration due to Depot Shape Variability" IEEE Trans. Biomedical Engineering, 56:3 pp 609-619. Published, 2009.
  • E. Jurrus, T. Tasdizen, P. Koshevoy, P. T. Fletcher, M. Hardy, C. Chien, W. Denk, and R. Whitaker, "Axon Tracking in Serial Block-Face Scanning Electron Microscopy," ; Medical Image Analysis, Volume 13, Issue 1, February 2009, Pages 180-188, ISSN 1361-8415, DOI: 10.1016/j.media.2008.05.002. Published, 2009.
  • N. L. Foster, A. Y. Wang, T. Tasdizen , P. T. Fletcher, J. M. Hoffman and R. A. Koeppe, "Realizing the potential of positron emission tomography wuth F-fluorodeoxyglucose to improve the treatment of Alzheimer's disease," The Journal of the Alzheimer's Association, Vol 4:1, Suppl. 1, pp. 29-36, 2008. Published, 2008.
  • T. Tasdizen and Ross T. Whitaker, “Higher-order Nonlinear Priors for Surface Reconstruction,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 26, Num. 7, pp. 878-891, July 2004. Published, 2004.
  • Won-Ki jeong, T. Tasdizen and Ross T. Whitaker, “Feature Preserving Smoothing of Height Field Data using Multigrid Solver on GPU,” Proceedings of ACM Workshop on General Purpose Computing on Graphics Processors, 2004. Published, 2004.
  • T. Tasdizen, Ross T. Whitaker, Paul Burchard and Stanley Osher, “Geometric Surface Processing via Normal Maps,” ACM Transactions on Graphics, Vol. 22, Num. 4, pp.1012-1033, October 2003. Published, 2003.
  • T. Tasdizen, Jean-Philippe Tarel and David B. Cooper, “Improving the Stability of Algebraic Curves for Applications,” IEEE Transactions on Image Processing, Vol. 9, Num. 3, pp.405-416, March 2000. Published, 2000.

Research Statement

My research interests are in the general areas of image processing and pattern recognition. More specifically we study geometry-based and statistics-based methods for image filtering, segmentation and feature extraction. Examples of our geometry-based methods include the use of high-order partial differential equations for image and surface reconstruction. Examples of our statistics-based methods include the use of patches for image denoising, texture segmentation and feature extraction. Papers on these subjects can be found here.

One important application of this research is biological image analysis. For instance, models of neural circuits are central to the study of the central nervous system. However, relatively little is known about the connectivities of neurons and state-of-the-art models are largely not based on anatomical ground truth. Neural circuit reconstruction, a.k.a connectome, research offers great promise for providing this anatomical ground truth. Serial-section electron microscopy images can provide the data necessary for reconstruction of large-scale neural circuits. However, the complexity and vast size of these images make human interpretation an extremely labor intensive task. The pipeline for reconstructing neural circuits from serial-section electron microscopy includes preprocessing the images, assembling 3D volumes, segmenting individual neurons and identifying synapses between the neurons. With this motivation, we have established a collaboration with researchers in ophthalmology and neurobiology  --funded by the National Institutes of Health--  aimed at building automate approaches for reconstructing the wiring diagram of neurons in the rabbit retina and the zebrafish optic tract from serial-section microscopy images. You can find out more about this line of research here. The neural circuit reconstruction toolset is a set of publicly available tools we have developed with funding from this project. The tool include image preprocessing, registration and mosaicking methods. We plan to add neuron segmentation and annottaion methods to the publicly available toolset in the near future. These tools were first used to buld a volume of the rabbit retina.

Presentations

  • Goergen Institute for Data Science Research Talk - Interpretable Prediction of Obstructive Lung Disease from Chest Radiographs with Deep Learning. Invited Talk/Keynote, Presented, 04/2021.
  • Neural network models for neighborhood effects research Applied Machine Learning Days, EPFL. Invited Talk/Keynote, Presented, 01/27/2020.
  • Radiology and Imaging Sciences Seminar, University of Utah. Other, Presented, 04/12/2019.
  • Disjunctive models for classification and regression, LANL Machine Learning Summer School. Other, Presented, 06/22/2017.
  • Semi-supervised learning and domain invariance with convolutional networks, Los Alamos National LAb. Invited Talk/Keynote, Presented, 06/21/2017.
  • Semi-supervised Learning for Hierarchical Cell Detection and Segmentation, Computer Vision for Microscopy Image Analysis Workshop at CVPR. Invited Talk/Keynote, Presented, 06/2016.
  • Scene Labeling with Supervised Contextual Models, Koc University. Invited Talk/Keynote, Presented, 03/25/2015.
  • Scene Labeling with Supervised Contextual Models, Bogazici University. Invited Talk/Keynote, Presented, 03/16/2015.
  • Scene Labeling with Supervised Contextual Models, Oxford University. Invited Talk/Keynote, Presented, 03/09/2015.
  • EE/CS Seminar Sabanci University. Invited Talk/Keynote, Presented, 12/2014.
  • University College London. Invited Talk/Keynote, Presented, 10/2013.
  • Bahcesehir University, Istanbul, Turkey . Invited Talk/Keynote, Presented, 06/2013.
  • Sabanci University, Istanbul, Turkey. Invited Talk/Keynote, Presented, 06/2013.
  • Fraunhofer, MEVIS. Invited Talk/Keynote, Presented, 03/2013.
  • S. M. Seyedhosseini, R. Kumar, E. Jurrus, R. Guily, M. Ellisman, H. Pfister and T. Tasdizen, ”Detection of Neuron Membranes in Electron Microscopy Images using Multi-scale Context and Radon-like Features,” Int. Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2011. Conference Paper, Refereed, Presented, 09/2011.
  • Janelia Farm. Invited Talk/Keynote, Presented, 09/2011.
  • Z. Leng, J. Korenberg, B. Roysam and T. Tasdizen, ”A Rapid 2-D Centerline Extraction Method Based On Tensor Voting,” IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, 2011. Conference Paper, Refereed, Presented, 05/2011.
  • Northeastern University. Invited Talk/Keynote, Presented, 09/2010.
  • A. R. C. Paiva and T. Tasdizen, ”Detection of Salient Image Points using Manifold Structure,” Int. Conf. on Pattern Recognition 2010 (oral). Conference Paper, Refereed, Presented, 08/2010.
  • S. M. Seyedhosseini, A. R. C. Paiva and T. Tasdizen, ”Image Parsing with a Three-State Series Neural Network Classifier,” Int. Conf. on Pattern Recognition 2010 (poster). Conference Paper, Refereed, Presented, 08/2010.
  • G Adluru, T. Tasdizen, R. T. Whitaker and E. DiBella, ”Improving Undersampled MRI Reconstruction Using Non-Local Means,” Int. Conf. on Pattern Recognition 2010 (oral). Conference Paper, Refereed, Presented, 08/2010.
  • 4. A. R. C. Paiva, E. Jurrus and T. Tasdizen, ”Using Sequential Context for Image Analysis,” Int. Conf. on Pattern Recognition 2010 (oral). Conference Paper, Refereed, Accepted, 08/2010.
  • K.. U. Venkataraju, A. Paiva, E. Jurrus. T. Tasdizen, ”Automatic Markup of Neural Cell Membranes using Boosted Decision Stumps,” IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro. Conference Paper, Refereed, Other, 2009.
  • A. Paiva and T. Tasdizen, ”Fast semi-supervised image segmentation by novelty selection,” ICASSP. Conference Paper, Refereed, Accepted, 2009.
  • J. Anderson, B. Jones, J. H. Yang, M. Shaw, C. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, K. U. Venkataraju, R. Whitaker, D. Mastronarde, T. Tasdizen, R. Marc, ”Ultrastructural mapping of neural circuitry: A computational framework,” IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro. Conference Paper, Refereed, Other, 2009.
  • S. K. Iyer, E. DiBella, T. Tasdizen, ”Edge enhanced spatio-temporal constrained reconstruction of undersampled dynamic contrast enhanced radial MRI,” accepted to IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro. Conference Paper, Refereed, Accepted, 2009.
  • E. Jurrus, A. R. C. Paiva, S. Watanabe, R. Whitaker, E. M. Jorgensen and T. Tasdizen, ”Serial Neural Network Classifier for Membrane Detection using a Filter Bank,” Int. Workshop on Microscopic Image Analysis with Applications in Biology. Conference Paper, Refereed, Presented, 2009.
  • S. Gerber, T. Tasdizen and R. T. Whitaker, ”Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds,” Int. Conf. Computer Vision. Conference Paper, Refereed, Other, 2009.
  • S. Gerber, T. Tasdizen, S. Joshi and R. T. Whitaker, ”On the Manifold Structure of the Space of Brain Images” MICCAI . Conference Paper, Refereed, Other, 2009.
  • T. Tasdizen , E. Jurrus and R. T. Whitaker, "Non-uniform Illumination Correction in Transmission Electron Microscopy," MICCAI Workshop on Microscopic Image Analysis with Applications in Biology, 2008. Conference Paper, Presented, 2008.
  • E. Jurrus, R.T. Whitaker, B. W. Jones, R. E. Marc and T. Tasdizen , "An Optimal-Path Approach for Neural Circuit Reconstruction," Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1609-1612, 2008. Conference Paper, Other, 2008.
  • T. Tasdizen , "Principal Components for Non-Local Means Image Denoising," Proceedings of the IEEE International Conference on Image Processing, 2008. Conference Paper, Other, 2008.
  • N. Sadeghi, N. L. Foster, A. Y. Wang, A. P. Lieberman and T. Tasdizen , "Automatic Classification of Alzheimer's Disease vs. Frontotemporal Dementia: A Spatial Decision Tree Aprroach with FDG-PET," Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 408-411, 2008. Conference Paper, Other, 2008.
  • E. Jurrus, T. Tasdizen , S. Watanabe, M. W. Davis, E. M. Jorgensen and R. T. Whitaker, "Semi-automated reconstruction of the neuromuscular junctions in C. elegans," MICCAI Workshop on Microscopic Image Analysis with Applications in Biology, 2008. Other, Presented, 2008.
  • N. L. Foster, A. Y. Wang, T. Tasdizen , K. Chen, W. Jagust, R. A. Koeppe, E. Reiman, M. W. Weiner and S. Minoshima, "Cerebral Hypometabolism suggesting Frontotemporal Dementia in an Alzheimer's Disease Clinical Trial," American Academy of Neurology, 2008. Other, Presented, 2008.
  • Joe M. Kniss, Robert Van Uitert, Abe Stephens, Guo-Shi Li, T. Tasdizen and Charles Hansen, “Statistically Quantitative Volume Visualization,” Proceedings of IEEE Visualization, 2006. Conference Paper, Other, 2006.
  • T. Tasdizen, Suyash P. Awate, Ross T. Whitaker and Norman L. Foster, “MRI Tissue Classification with Neighborhood Statistics: A Nonparametric, Entropy-Minimizing Approach,” Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Vol. 2, pp. 517-525, 2005. Conference Paper, Other, 2005.
  • Leo Grady and T. Tasdizen, “A Geometric Multigrid Approach to Solving the 2D Inhomogeneous Laplace Equation with Internal Drichlet Boundary Conditions,” Proceedings of International Conference on Image Processing (ICIP), Vol. 2, pp. 642-645, September 2005. Conference Paper, Other, 2005.
  • T. Tasdizen, Ross T. Whitaker, Robert E. Marc and Bryan W. Jones, “Enhancement of Cell Boundaries in Transmission Electron Microscopy Images,” Proceedings of International Conference on Image Processing (ICIP), Vol. 2, pp. 129-132, September 2005. Conference Paper, Other, 2005.
  • G. Kindlmann, A. L. Alexander, M. Lazar, J. Lee, T. Tasdizen and R. T. Whitaker, “An Algorithm for Moment-Based Global Registration of Echo Planar Diffusion-Weighted Images,” Proceedings of 12th Annual ISMRM, pp. 2200, 2004. Conference Paper, Presented, 2004.
  • T. Tasdizen and Ross T. Whitaker, “Cramer-Rao Bounds for Nonparametric Surface Reconstruction from Range Data,” Proceedings of 4th International Conference on 3D Digital Imaging and Modeling, pp. 70-77, October 2003. Conference Paper, Other, 2003.
  • M. Barzohar, L. Preminger. T. Tasdizen and David B. Cooper, “Robust Method for Completely Automatic Aerial Detection of Occluded Roads with New Initialization,” Proceedings of SPIE - Vol. 4820, Infrared Technology and Applications XXVII, pp. 688-698, January 2003. Conference Paper, Other, 2003.
  • Simon Premoze, T. Tasdizen, James Bigler, Aaron Lefohn and Ross T. Whitaker, “Particle-Base Simulation of Fluids,” Proceedings of Eurographics, pp. 401-410, September 2003. Conference Paper, Other, 2003.
  • T. Tasdizen and Ross T. Whitaker, “Feature Preserving Variational Smoothing of Terrain Data,” 2nd International IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision, October 2003. Conference Paper, Other, 2003.
  • T. Tasdizen and Ross T. Whitaker, “Anisotropic Diffusion of Surface Normals for Feature Preserving Surface Reconstruction”, Proceedings of 4th International Conference on 3D Digital Imaging and Modeling, pp. 353-360, October 2003. Conference Paper, Other, 2003.
  • Gordon Kindlmann, Ross T. Whitaker, T. Tasdizen and Torsten Miller, “Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications,” Proceedings of IEEE Visualization, pp. 513-520, October 2003. Conference Paper, Other, 2003.
  • T. Tasdizen, Ross T. Whitaker. Paul Burchard and Stanley Osher, “Geometric Surface Smoothing via Anisotropic Diffusion of Normals” Proceedings of IEEE Visualization, pp. 125-132, October 2002. Conference Paper, Other, 2002.
  • T. Tasdizen and David B. Cooper, “Boundary Estimation from Intensity/Color Images with Algebraic Curve Models,” Proceedings of 15th IEEE Computer Society International Conference on Pattern Recognition (ICPR), Vol. 1, pp. 225-228, September 2000, Best Student Paper Award Honorable Mention. Conference Paper, Other, 2000.
  • T. Tasdizen, Jean-Philippe Tarel and David B. Cooper, “Algebraic Curves that Work Better,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp.35-41, June 1999. Conference Paper, Other, 1999.
  • Zhibin Lei, T. Tasdizen and David B. Cooper, “PIMS and Invariant Parts for Shape Recognition,” Proceedings of 6th IEEE Computer Society International Conference on Computer Vision (ICCV), January 1997. Conference Paper, Other, 1997.

Research Groups

  • Jess Tate, Postdoc. 09/2019 - 12/2020.
  • Abhinav Kumar, Graduate Student. 08/15/2019 - 05/2020.
  • Amir Nazem, Graduate Student. 08/15/2019 - 05/2021.
  • Aishwarya Gupta, Graduate Student. 08/15/2019 - 05/2021.
  • Krithika Iyer, Graduate Student. CS. 01/15/2019 - 05/2020.
  • Jia Wei, Visiting Faculty. 08/2018 - 08/2019.
  • Clement Vachet , Other. 01/01/2018 - present.
  • Joshua Ong, Undergraduate Student. 01/2018 - 08/2019.
  • Fitsum Mesadi, Graduate Student. 09/01/2014 - 05/31/2017.
  • Mehran Javanmardi, Graduate Student. 01/01/2014 - 06/2019.
  • Mehdi Sajjadi, Graduate Student. 01/2013 - 05/15/2017.
  • Ting Liu, Graduate Student. 01/2012 - 05/2016.
  • Cory Jones, Graduate Student. 01/2012 - 01/2016.
  • Nisha Ramesh, Graduate Student. 01/2012 - 05/2018.

Geographical Regions of Interest

  • United Kingdom of Great Britain and Northern Ireland

Software Titles

  • Mixed material microscopy image analysis. https://github.com/nly8292/Mixed_Samples_Synthesis. Release Date: 12/2021.
  • Eye tracking. Code for eye tracking data collection for our NIH R21 project. Release Date: 01/2021. Distribution List: https://github.com/ricbl/eyetracking.
  • DeFI-GAN - deformation field interpretation with generative adversarial networks. Code for our 2020 MICCAI paper. Release Date: 10/2020. Distribution List: https://github.com/ricbl/defigan.
  • Multi-magnification microscopy image classification. https://github.com/nly8292/MISO. Release Date: 08/2020.
  • VR-GAN - visualization for regression with a generative adversarial network. Code for our 2019 MICCAI paper . Release Date: 10/2019. Distribution List: https://github.com/ricbl/vrgan.
  • Semi-supervised deep learning. Semi-supervised deep learning of our NIPS 2016 paper. Release Date: 01/2017.
  • GLIA: GRAPH LEARNING LIBRARY FOR IMAGE ANALYSIS. The code for my student Ting Liu's PhD dissertation. Release Date: 05/2016. Inventors: Ting Liu and Tolga Tasdizen. Distribution List: http://www.sci.utah.edu/~tolga/ResearchWebPages/code.html.
  • Logistic Disjunctive Normal Networks. code for our novel supervised learning methods. Release Date: 12/2013. Distribution List: http://www.sci.utah.edu/~tolga/ResearchWebPages/code.html.
  • Cascaded Hierarchical Model for Segmentation. Semantic image labeling package with applications to boundary detection and object labeling. Release Date: 12/2013. Distribution List: http://www.sci.utah.edu/~tolga/ResearchWebPages/code.html.
  • SLASH Online 3D Image Segmentation Tools. Tools for segmentation of electron microscopy images. Release Date: 01/2013.
  • Neural Reconstruction Toolset (NCRToolset). A set of publically available tools to enable the large scale reconstruction of 2D mosaics and 3D volumes collected via electron and light microscopy. Release Date: 2009. Inventors: T. Tasdizen.
  • Ir-tools and MosaicBuilder. ir-tools are a suite of command line tools for automatically preprocessing and registering serial-section transmission microscopy images. The registration component includes both mosaicking and section-to-section registration. See http://www.sci.utah.edu/stories/2008/CRCNS.html for more details. Mosaicbuilder is a graphical user interface to visualize large microscopy mosaics. Release Date: 2008. Distribution List: Open access. Our collaborators funded on my NIH project are regularly using these tools in their workflow in their lab. External research groups have also used these tools including Clay Reid's research group at Harvard (part of the connectome project). The tools are available online or by email request. There is a story publicizing the tools at: http://www.sci.utah.edu/stories/2008/CRCNS.html.