Tammy Riklin Raviv, PhD

Associate Professor
The School of Electrical and Computer Engineering
Ben Gurion University of the Negev

Postdoc and Research Fellow
Medical Vision Group, CSAIL, MIT
with Prof. Polina Golland
Departments of Psychiatry and Radiology, Harvard Medical School
Imaging Platform , The Broad Institute of MIT and Harvard

PhD
Prior based Image Segmentation
The School of Electrical Engineering, Tel-Aviv University
with Prof. Nahum Kiryati

M.Sc.
The Quotient Image:Class based Recognition and Synthesis Under Varying Illumination Conditions
Computer Science, The Hebrew University of Jerusalem
with Prof. Amnon Shashua

Contact

Location: BGU campus,Building 33, Office 212
Phone:+972-8-6428812
Fax: +972-8-6472949
Address: P.O.Box. 653, Beer-Sheva, 84105, ISRAEL

Curriculum Vitae


Community Activities

Associate Editor IEEE Transactions on Medical Imaging (TMI)

Technical Committee member IEEE Bio Imaging and Signal Processing (BISP) Committee

Co-organizer CVPR Medical Computer Vision (MCV) workshop 2023

Area Chair Computer Vision and Pattern recognition (CVPR) 2023

Program Committee Medical Image Computing and Computer Aided Intervention (MICCAI) 2023

Steering Committee IEEE International Symposium on Biomedical Imaging (ISBI) 2023 I

Steering Committee Israel Machine Vision Conference (IMVC) 2023

Organizing Committee Medical Image Computing and Computer Aided Intervention (MICCAI) 2024

Teaching
Introduction to Digital Image Processing
Deep Learning and Its Applications to Signal and Image Processing and Analysis
Introduction to Biomedical Imaging
Magnetic Resonance Imaging
Introduction to Computational Methods

Publications

Journal Papers

M. Avi-Aharon, A. Arbelle and T. Riklin Raviv,
Differentiable Histogram Loss Functions for Intensity-based Image-to-Image Translation,
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Accepted, 2023.

M. Maška V. Ulman, P. Delgado-Rodriguez, E. Gómez-de-Mariscal and others (including A. Arbelle, T. Ben-Haim and T. Riklin Raviv),
The Cell Tracking Challenge: 10 Years of Objective Benchmarking,
Nature Methods , Accepted, 2023.

A. Arbelle, S. Cohen and T. Riklin Raviv,
Dual-Task ConvLSTM-UNet for Instance Segmentation of Weakly Annotated Microscopy Videos ,
IEEE Transactions on Medical Imaging (TMI) , 41(8), 1948-1960, 2022.

Y. Ben-Guigui, J. Goldberger and T. Riklin Raviv,
Stochastic Weight Pruning and the Role of Regularization in Shaping Network Structure,
Neurocomputing,Vol. 462, pages 555-567, 2021.

S. Gordon, B. Veselov, T. Goldfryd, M. Sidorov, J. Goldberger and T. Riklin Raviv,
An Atlas of Classifiers - A Machine Learning Paradigm for Brain MRI Segmentation,
Medical & Biological Engineering & Computing, Volume 59, Issue 9, pages 1833-1849, 2021.

R. Shaul, I. David, O. Shitrit and T. Riklin Raviv,
Subsampled Brain MRI Reconstruction by Generative Adversarial Neural Networks,
Medical Image Analysis, Volume 65, 2020.

G. Levakov, G. Rosenthal, I. Shelef, T. Riklin Raviv and G. Avidan,
From a deep learning model back to the brain - identifying regional predictors and their relation to aging,
Human Brain Mapping, Volume 41, Issue 12, pages 3235-3252, 2020.

R. Veksler, U. Vazana, Y. Serlin, O. Prager, J. Ofer, N. Shemen, A. M. Fisher, O. Minaeva, N. Hua, R. Saar-Ashkenazy, I. Benou, T. Riklin-Raviv, E. Parker, G. Mumby, L. Kamintsky, S. Beyea, C. V. Bowen, I. Shelef, E. O'Keefe, M. Campbell, D. Kaufer, L. E. Goldstein and A. Friedman,
Slow blood-to-brain transport underlies enduring barrier dysfunction in American football players,
Brain, Volume 143, Issue 6, pages 1826-1842, 2020.

J.J. Levitt, P. J. Nestor, M. Kubicki, A.E. Lyall, F. Zhang, T. Riklin-Raviv, L.J. O'Donnell, R.W. McCarley, M.E. Shenton and Y. Rathi,
Miswiring of Frontostriatal Projections in Schizophrenia,
Schizophrenia Bulletin, Volume 46, Issue 4, pages 990-998, 2020.

I. Benou, R. Veksler, A. Freidman and T. Riklin Raviv, Combining White Matter Diffusion and Geometry for Tract-Specific Alignment and Variability Analysis,
Neuroimage, Volume 200, pages 674--689, October 2019.

T. Gilad, J. Reyes, J.-Y. Chen, G. Lahav and T. Riklin Raviv, Fully Unsupervised Symmetry-Based Mitosis Detection in Time-Lapse Cell Microscopy,
Bioinformatics, Volume 35, Issue 15, 2644-2653, August 2019.

S. Gordon, I. Dolgopyat, I. Kahn and T. Riklin Raviv, Multidimensional Co-segmentation of Longitudinal Brain MRI Ensembles in the Presence of a Neurodegenerative Process,
Neuroimage, Vol. 178, pp. 346-369, Sep. 2018.

A. Arbelle, J. Reyes, J.-Y. Chen, G. Lahav and T. Riklin Raviv, A Bayesian Approach for Joint Cell Tracking and Segmentation in High-Throughput Microscopy Videos,
Medical Image Analysis, Vol. 47, 140-152, July 2018.

A. Benou, R. Veksler, A. Freidman and T. Riklin Raviv, Ensemble of Expert Deep Neural Networks for Spatio-Temporal Denoising of Contrast-Enhanced MRI Sequences,
Medical Image Analysis, Vol. 42, pp. 145-159, 2017.

T. Hershkovich, T. Shalmon, O. Shitrit, N. Halay, B. Menze, I. Dolgopyat, I. Kahn, I. Shelef and T. Riklin Raviv, ,
A probabilistic model for 3D interactive segmentation,
Computer Vision and Image Understanding (CVIU), Special Issue on Probabilistic Models for Biomedical Image Analysis, Vol. 151, pp. 47-60, 2016.
Demo<->

B. Menze, K. Van Leemput, D. Lashkari, T. Riklin-Raviv, E. Geremia, E. Albert, et al. A generative probabilistic model and discriminative extensions for brain lesion segmentation with application to tumor and stroke,
IEEE Transaction on Medical Imaging, Vol. 35 (4), pp. 933-946, 2016.

B. Menze, A. Jakaby, S. Bauery, J. Kalpathy-Cramery, K. Farahaniy, J. Kirbyy, et al, (including T. Riklin Raviv), The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),
IEEE Transaction on Medical Imaging, Vol. 34(10), pp. 1993-- 2024, Oct. 2015.

T. Riklin-Raviv, Yi Gao, James J. Levitt and S. Bouix Statistical Shape Analysis of Neuroanatomical Structures via Level-set based Shape Morphing.
SIAM Journal on Imaging Sciences, Vol. 7(3), pp. 1645--1668, November 2014.

Y. Gao, T. Riklin-Raviv, and S. Bouix, Shape analysis, a field in need of careful validation,
Human Brain Mapping, Vol. 35 (10), pp. 4965-4978, October 2014.

E. Dittrich, T. Riklin-Raviv, G. Kasprian, P. Brugger, D. Prayer and G. Langs A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation.
Medical Image Analysis, Vol. 18(1), pp 9-21, Jan. 2014.

C. Wählby, L. Kamentsky , Z. H. Liu , T. Riklin-Raviv, A. L. Conery , E. J. ORourke, K. L. Sokolnicki , O. Visvikis , V. Ljosa , J. E. Irazoqui , P. Golland, G. Ruvkun, F. M. Ausubel and A. E. Carpenter An Image Analysis Toolbox for High-throughput C. Elegans Assays.
NATURE METHODS vol. 9 pp 627-763, July 2012.

T. Riklin Raviv, K. Van-Leemput, B. Menze, W.M. Wells III and P. Golland Segmentation of Image Ensambles via Latent Atlases.
Special Issue on the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2009. Medical Image Analysis (MedIA), Vol. 14(5) pp 654-665, October 2010.

T. Riklin Raviv, N. Sochen and N. Kiryati On Symmetry, Perspectivity and Level-set based Segmentation.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 31(8) pp 1458--1471, August 2009

T. Riklin Raviv, N. Sochen and N. Kiryati Shape-based Mutual Segmentation.
International Journal of Computer Vision (IJCV), 79(3) pp 231--245, September 2008

T. Riklin Raviv, N. Kiryati and N. Sochen Prior-based Segmentation and Shape Registration in the Presence of Projective Distortion.
International Journal of Computer Vision (IJCV), 72(3), pp 309--328, May 2007

A. Shashua and T. Riklin Raviv The Quotient Image: Class Based Re-rendering and Recognition with Varying Illuminations.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 23(2), pp. 129--139, February 2001

Peer Reviewed Conference Papers

T. Ben-Haim and T. Riklin Raviv
Graph Neural Network for Cell Tracking in Microscopy Videos
European Conference on Computer Vision (ECCV) , pages 610-626, October 2022

T. Ben-Haim, R.M. Sofer, I. Shelef, G. Ben-Arie and T. Riklin Raviv
A Deep Ensemble Learning Approach to Lung CT Segmentation for COVID-19 Severity Assessment
IEEE International Conference on Image Processing (ICIP), pages 151-155 , October 2022

T. Goldfryd, S. Gordon and T. Riklin-Raviv
Deep Semi-Supervised Bias Field Correction of MR Images
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) , 1836-1840, April 2021

I. Benou and T. Riklin Raviv
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
International Conference of Medical Image computing and Computer Assisted Intervention (MICCAI), pages 626--635, October 2019

A. Arbelle and T. Riklin Raviv,
Microscopy Cell Segmentation via Convolutional LSTM Networks,
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pages 1008--1012, April 2019.

H. Ben-Hamu Goldberg, J. Mushkin, N. Sochen and T. Riklin Raviv, Sampling Technique for Defining Segmentation Error Margins with Application to Structural Brain MRI,
IEEE International Conference on Image Processing (ICIP), pp. 734-738, October 2018.

O. Gorodissky, A. Sharon, A. Danov, A. Friedman, and T. Riklin Raviv, Symmetry-based Analysis of Diffusion MRI for the Detection of Brain impairments,
IEEE International Conference on Image Processing (ICIP), pp. 376-379, October 2018.

A. Arbelle and T. Riklin Raviv, Microscopy Cell Segmentation via Adversarial Neural Networks,
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp. 645-648, April 2018.

T. Hershkovich and T. Riklin Raviv, Model-dependent Uncertainty Estimation of Medical Image Segmentation,
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp. 1373-1376, April 2018.

I. Benou, A. Friedman and T. Riklin Raviv, Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players,
MICCAI Workshop on Computational Diffusion MRI, 191--202, September 2018.

O. Shitrit and T. Riklin Raviv, Accelerated Magnetic Resonance Imaging by Adversarial Neural Network,
MICCAI workshop on Deep Learning in Medical Image Analysis (DLMIA), pp 30-38, September 2017.

B. Kodner, S.H. Gordon, J. Goldberger and T. Riklin Raviv, Atlas of Classifiers for Brain MRI Segmentation,
MICCAI workshop on Machine Learning in Medical Imaging (MLMI), pp 36-44, September 2017.

T. Riklin Raviv, Multinomial Level-Set Framework for Multi-Region Image Segmentation,
6th Conference on Scale Space and Variational Methods in Computer Vision (SSVM), pages 386-395, Springer, Cham, June 2017.

A. Benou, R. Veksler, A. Freidman and T. Riklin Raviv, De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks,
MICCAI workshop on Deep Learning in Medical Image Analysis (DLMIA),pages 95-110, October 2016.

S. Gordon, I. Dolgopyat, I. Kahn and T. Riklin Raviv, Co-segmentation of Multiple Images into Multiple Regions: Application to Mouse Brain MRI,
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pages 399-402, April 2016.

A. Arbelle, N. Drayman, M. Bray, U. Alon, A. Carpenter and T. Riklin Raviv
Analysis of High-throughput Microscopy Videos: Catching Up with Cell Dynamics.
International Conference of Medical Image computing and Computer Assisted Intervention (MICCAI), pages 218--225, October 2015

T. Gilad, M.A. Bray, A.E. Carpenter and T. Riklin Raviv Symmetry based mitosis detection in time-lapse microscopy.
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pages 164--167, April 2015

O. Shitrit, T. Hershkovitch, T. Shalmon, I. Shelef and T. Riklin Raviv Probabilistic Model for 3D Interactive Segmentation.
MICCAI workshop on Interactive Medical Image Computing (IMIC), September, 2014

T. Riklin-Raviv, Y. Gao, J. Levitt and S. Bouix Statistical Shape Analysis for Population Studies via Level-set based Shape Morphing.
ECCV workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA), October, 2012

T. Riklin Raviv, K. Van-Leemput and B.M. Menze Multi-modal brain tumor segmentation via latent atlases.
MICCAI challenge on Multimodal Brain Tumor Segmentation, pp. 64--73, October 2012

E. Dittrich, T. Riklin-Raviv, G. Kasprian, P. Brugger, D. Prayer and G. Langs Learning a Spatio-temporal Latent Atlas for Fetal Brain Segmentation.
MICCAI workshop: Image Analysis of Human Brain Development, September 2011

T. Riklin-Raviv, V. Ljosa, A. L. Conery, F. M. Ausubel, A.E. Carpenter, P. Golland and C. Wählby Morphology-Guided Graph Search for Untangling Objects: C. Elegans Analysis
International Conference of Medical Image computing and Computer Assisted Intervention (MICCAI), pp. 634-641, September 2010

C. Wählby, T. Riklin-Raviv, V. Ljosa, A. L. Conery, P. Golland, F. M. Ausubel and A. E. Carpenter Resolving Clustered Worms via Probabilistic Shape Models
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), April 2010

T. Riklin Raviv, K. Van-Leemput, W.M. Wells III and P. Golland Joint Segmentation of Image Ensambles via Latent Atlases.
International Conference of Medical Image computing and Computer Assisted Intervention (MICCAI), Part I, LNCS 5761, pp 272--280, September 2009. Received the MICCAI-09 Young Scientist Award.

T. Riklin Raviv, B.M. Menze, K. Van-Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W.M. Wells III and P. Golland Joint Segmentation via Patient-Specific Latent Anatomy Model.
MICCAI workshop: Probabilistic Models for Medical Imaging Analysis (PMMIA), September 2009

N. Ben-Zadok, T. Riklin Raviv and N. Kiryati Interactive Level Set Segmentation for Image-guided Therapy
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp 1079--1082, June 2009

N. Kiryati, T. Riklin Raviv, Y. Ivanchenko, S. Rochel Real-time Abnormal Motion Detection in Surveillance Video.
International Conference on Pattern Recognition (ICPR), December 2008

T. Riklin Raviv, N. Kiryati, N. Sochen, N. Ben-Zadok, S. Gefen, L. Bertand and J. Nissanov Propagating Distributions for Segmentation of Brain Atlas.
IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp 1304--1307, April 2007

T. Riklin Raviv, N. Kiryati and N. Sochen Segmentation by Level sets and Symmetry.
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp 1015--1022, New York, June 2006

T. Riklin Raviv, N. Sochen and N. Kiryati Mutual Segmentation with Level Sets.
The 5th IEEE Workshop on Perceptual Organization in Computer Vision, in conjunction with the CVPR, New York, June 2006

T. Riklin Raviv, N. Kiryati and N. Sochen Prior-based Segmentation by Projective Registration and Level sets.
IEEE International Conf. on Computer Vision (ICCV), pp 204--211, October 2005.

T. Riklin Raviv, N. Kiryati and N. Sochen Unlevel-Set: Geometry and Prior-based Segmentation.
Proc. of the European Conference on Computer Vision(ECCV), pp.50--61, May 2004

T. Riklin Raviv and A. Shashua The Quotient Image: Class Based Recognition and Synthesis with Varying Illumination Conditions.
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp.566-571, June 1999

Short papers and abstracts

J. Levitt, Y. Rathi, T. Riklin Raviv, P. G. Nestor, L. Levin, R.W. McCarley and M.E. Shenton, Frontrostriatal dysconnectivity in Schizophrenia
In Schizophrenia bulletin. Vol. 41, pp. 263-263, 2015.

J. Levitt, Y. Rathi, T. Riklin Raviv, R.W. McCarley and M.E. Shenton, DTI Connectivity-Based Parcellation of the Striatum in Schizophrenia.
Biological Psychiatry. Vol. 75, No. 9, pp. 375-375, 2014.

J. Levitt, Y. Rathi, T. Riklin Raviv, R. McCarley, M.E. Shenton, Connectivity-based Parcellation of the Striatum in Schizophrenia Using Diffusion Weighted Imaging (DWI). Neuropsychopharmacology. 39, 221-222, 2014.

T. Riklin Raviv Y. Gao, and S. Bouix Statistical shape analysis with modified Hausdorff distance.
IEEE Engineering in Medicine and Biology Society (EMBS), 2012.

Theses
T. Riklin Raviv Prior based Image Segmentation.

T. Riklin Raviv The Quotient Image: Class Based Re-rendering and Recognition With Varying Illuminations.

Patent
N. Kiryati, T. Riklin Raviv, Y. Ivanchenko, S. Rochel, Y. Dvir and D. Harari
Apparatus and Methods for the Detection of Abnormal Motion in a Video Stream.
European Patent EP1631073B1

Demos
Expert DNN Ensemble for DCE-MRI Denoising
Joint Cell Segmentation and Tracking in High-Throuput Microscopy Videos
A Probabilistic Approach to User-interactive Segmentation
Segmentation of Image Ensembles Via Latent Atlases
Interactive Level-set Segmentation for Image-guided Therapy
Propageting Distributions for Segmentation of Mouse Brain Atlas
Geometry and Prior Based Segmentation with Level-sets
Real-time Abnormal Motion Detection in Surveillance Video
The Quotient Image: Class based Recognition and Synthesis under Varying Illumination Conditions

Impact/Links/Collaborations
C. elegans Analysis
Image analysis for high-throughput C. elegans infection and metabolism assays, NIH, RO1, Sample Application
Segmentation of Image Ensembles Via Latent Atlases
Young Scientist Award, Miccai 2009
A glimpse to google scholar

Presentations
Statistical Shape Analysis for Population Studies via Level-set Based Shape Morphing
Segmentation of Image Ensembles Via Latent Atlases
Shape-based Segmentation with Level-sets
Real-time Abnormal Motion Detection in Surveillance Video
The Quotient Image:Class based Recognition and Synthesis under Varying Illumination Conditions

Best Ever Projects