National Centre for Cardiovascular Research (CNIC) Melchor Fernandez Almagro 3, Madrid-28029, Spain
Date of birth: 24th Dec, 1982 WebpageLinkedIn: SPECIALTIES Medical Imaging, Computer Aided Detection, Bioimage Informatics, Machine Learning, Image Texture Analysis, Light Microscopy Image Analysis, Volume Visualization, Imaging Biomarkers, Effect Specific Drug Quantification, , High Content Image Analysis, Optical Pathology, Data Analysis, and Statistics. EDUCATION QUALIFICATION
• (BE) Bachelor of Electronics Engineering from Pune University, India in 2005
• (MS) Master of Science in Medical Software from Manipal University, India in 2007
• (PhD) PhD at Dept. of Computer Science, University of Copenhagen, Denmark in 2011
• Languages: Matlab (Advance), Python (Average).
• Application Softwares: Amira, Imaris, Definiens, Knime, MevisLab, Volocity, ITK, VTK. INDUSTRY EXPOSURE
• Current Job (since Dec 2011): Working as an Image Analysis Scientist at National Centre for Cardiovascular Research (CNIC), Madrid. Highlights of some projects are mentioned below Introducing/Training biologist to various Image analysis softwares and answering their daily
Machine learning for modeling and interpretation of High Content Screening (HCS) images
of siRNAs for rapid drug discovery using Matlab, Definiens, ImageJ and Imaris. I have developed a technique based on shape and texture of local cell structure and co-ordinate system which will discriminate different siRNAs and controls, there by robust and rapid screening. We also developed a supervised machine learning technique for GFP+/- phenotype classification for High Content Screening. Corresponding work has been currently accepted in Journal of Biomolecular Screening.
I'm leading a project on 3D visualization of mice embryonic heart morphogenesis in order to
quantify the mitotic spindle orientation using computational geometry and shape modeling in OPT/Confocal Imaging using Amira, Imaris, MevisLab and Matlab. It mainly involves a) Volume registration b) Tubulin Segmentation c) Vector quantification and visualization.
Texture Based Biomarker for Quantifying Fibrillogenesis using Structure Tensor, Fractal
Dimension, Lacunarity, and Homogeneity for both Confocal and SHG Images using Matlab and ImageJ.
Quantify the pattern (orientation) of GFP in Z-fish embryonic heart development and study
the behavior of Epicardium formation. It also involves the intelligent scanning of Z-fish on Opera machine by Perkin Elmer.
• (2011) Invited PhD scholar at University of Pennsylvania, Radiology Dept., USA, worked on
developing imaging biomarker (e.g. structure & diffusion tensor etc.) for Estrogen receptor subtype specific populations in breast cancer imaging.
• (2010) PhD scholar Siemens Corporate Research, Princeton, USA worked on fast automatic
landmark detection in CT-topographs using voting based machine learning techniques.
• (2008-2011) worked as a PhD scholar (industrial) with an interest in “Computer Aided Breast
Cancer Risk Assessment using Shape and Texture of Breast Parenchyma in Mammography” at University of Copenhagen in collaboration with Synarc Labs, USA & Nordic Bioscience A/S, Denmark.
• (2009) Short term visit at Dept. of Epidemiology & Biomedical Engg. at Mayo clinic and Dept.
of Mathematics at UC-Berkeley, USA.
• (2007) Invited Scholar at the Dept. of Bioinformatics, The Fraunhofer Institute for Algorithms and Scientific Computing, Germany. Worked on shape based image segmentation in application to Chemical Structure Reconstruction (CSR) project.
• (2007) Research Consultant in Imaging Labs, GE Global Research, John F Welch Technology
Center, Bangalore, India, worked on CT Perfusion imaging for detection of Ischemic Stroke (Segmentation of WT, GT, and CSF to evaluate Penubra and Infarct region), vessel partitioning, brain tissue labeling, and non rigid registration for atlas based segmentation.
• (2006-07) Master Thesis on Early Detection of Breast Cancer using Ultrasound at Philips Medical System.
• (2005-2007) Worked as a Junior Research Scientist (part-time) on Female cancer project at Manipal University worked on Improved Techniques for the Classification of Cancerous Data collected by optical pathology using HPLC-LIF.
• (2003-04) Bachelor thesis on Intelligent Unmanned Battle Tank using Real Time Pattern Recognition and Feature Extraction at DRDO, India.
• (2005-06) Application development on SIMPUTER using GTK library with Linux Kernel for PHILIPS BOP at Oral Cancer awareness program in 2005-06. PATENT Inventors: Jakob Raundahl, Marco Loog, Mads Nielsen, Sami S. Brandt, Gopal R. Karemore USPTO: 20110013819, BREAST TISSUE DENSITY MEASURE - method of processing mammogram image to derive a value for a parameter useful in detecting differences in breast. SCIENTIFIC PUBLICATIONS
o S. Brandt, Gopal Karemore, Karssemeijer N, Mads, Anatomically oriented breast coordinate system for mammogram analysis, IEEE Trans in Medical Imaging (TMI), 30(10):1841-51.
o Gopal Karemore, Brad Keller, Huen Oh, Julia Tchou, Emily Conant, Despina Kontos, Computer-Aided Parenchymal Texture Analysis in Digital Mammograms: The Potential for Estrogen-Receptor Specific Breast Cancer Risk Estimation, RSNA Chicago, USA, 2011, Trainee prize nominee.
o Gopal Karemore,S Brandt, N Karssemeijer,M Nielsen,Discovery of Mammogram Regions That Show Early Changes Due to the Development of Breast Cancer: A Preliminary Work, RSNA Chicago, USA, 2011.
o Gopal Karemore, M. Nielsen, An Automatic Framework for Assessing Breast Cancer Risk Due to Various Hormone Replacement Therapies (HRT): A novel CAD application in Digital Mammography. RSNA 2010, Chicago, USA.
o Gopal Karemore, S. Brandt, M. Nielsen, Automatic breast cancer risk assessment from digital Mammograms, European Congress of radiology-2011, Vienna, Austria.
o Nielsen, Mads, Gopal Karemore, Loog M, Raundahl J, Karssemeijer N, Otten JD, Karsdal MA,
Vachon CM, Christiansen C., A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer, Cancer Epidemiology. 2010 Dec 9, 35(4):381-7.
o Nielsen, Mads; Pettersen, Paola; Alexandersen, P;Gopal Karemore; Raundahl, Jakob; Loog,
Marco; Christiansen, Claus. Breast density changes associated with postmenopausal hormone replacement therapy: post hoc radiologist- and computer-based analyses.Menopause Journal 17(4) 772-77, (2010)
o Karemore G, B. Keller, H. Oh, J. Tchou, M. Nielsen, E. Conant, D. Kontos, Mammographic Parenchymal Texture Analysis for Estrogen-Receptor Subtype Specific Breast Cancer Risk Estimation. Lecture Notes in Computer Science (LNCS), 596-603.
o Azegrouz H., Karemore G et al. Cell-based fuzzy metrics enhance High Content Screening (HCS) assay robustness, 2013 Special Issue on Phenotypic Drug Discovery initiated by Journal of Biomolecular Screening (Accepted).
o Despina Kontos, Gopal Karemore, Dania Daye et al., Mammographic parenchymal texture as an imaging marker of endogenous hormonal activity: The potential for sub-type specific breast cancer risk prediction, DoD Era of Hope, Orlando, FL, USA, August 2, 2011
o Gopal Karemore et al, Fractal Dimension and Lacunarity analysis of mammographic patterns in assessing breast cancer risk related to HRT treated population: A Longitudinal and Cross- sectional study, Progress in biomedical optics and imaging, SPIE Proceedings, Vol 10 (2).
o Nielsen, Mads; Raundahl, Jakob; Pettersen, Paola; Loog, Marco;Gopal Karemore; Karsdal,
Morten; Christiansen, Claus , Low dose transdermal estradiol induces breast density and heterogeneity changes comparable to those of raloxifene, Menopause Journal, August 2009 Vol 16(4)- 2009, USA
o Chen, Chen ; Chernoff, Konstantin ; Gopal Karemore ; Lo, Pechin Chien Pau ; Nielsen, Mads ;
Lauze, Francois Bernard. Classification in Medical Image Analysis Using Adaptive Metric KNN. SPIE Medical Imaging 2010, No. 2010, February 18, 2010,USA
o Gopal Karemore et al., Classification of Laser Induced Fluorescence Spectra from Normal and Malignant bladder tissues using Learning Vector Quantization Neural Network in Bladder Cancer Diagnosis, 8th IEEE International Conference on Bioinformatics and Bioengineering, BIBE, Athens, Greece.
o Gopal Karemore et al., Automatic Consistent Registration Framework for temporal pairs of mamogram: In application to breast cancer risk assessment due to HRT (Hormone Replacement Therapy), In proceedings of International Journal of Computer Assisted Radiology and Surgery, Volume 4, Supplement 1, June, 2009, Berlin, Germany.
o Gopal Karemore et al., Anisotropic diffusion tensor applied to temporal mammograms: an application to breast cancer risk assessment, Proc IEEE Eng in Medicine and Biology: 3178- 81.
o Karemore G, Azegrouz H., Del Pozo MA et al., Quantification of collagen ﬁbre orientations in Second Harmonic Generation (SHG) imaging using scale space texture techniques (Manuscript under process).
o Gopal Karemore et al., Classification of protein profiles using fuzzy clustering techniques: An application in early diagnosis of oral, cervical and ovarian cancer, IEEE Proc in Engineering in Medicine and Biology: 6361-4.
o Gopal Karemore et al., Protein profile study of clinical samples using Laser Induced Fluorescence as the detection method: Case of malignant and normal cervical tissues, Advanced Biomedical and Clinical Diagnostic Systems VII, SPIE proceedings, Volume 7169, pp. 71691-8.
o Gopal Karemore et al., Serum Protein Profile Study of Clinical Samples Using High Performance Liquid Chromatography-Laser Induced Fluorescence: Case of Cervical and Oral Cancers, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues VII , SPIE proceedings, Vol. 7182, 71820J.
o Gopal Karemore, S. Brandt, Karssemeijer, N & Nielsen, ‘Anatomic Breast Coordinate System for Mammogram Analysis’ 5th International Workshop on Breast Densitometry and Breast Cancer Risk Assessment-2011 ,San Francisco, United States.
o Gopal Karemore et al., Yet Another Mammography Measure to Evaluate Breast Cancer Risk,
4th International workshop on Breast Densitometry and the 1st International Workshop on Mammography-based Assessment of Breast Cancer Risk, 2009, USA.
o Gopal Karemore et al., An Automatic Framework for Assessing Breast Cancer Risk Due to Various Hormone Replacement Therapies (HRT), 4th International workshop on Breast Densitometry and the 1st International Workshop on Mammography-based Assessment of Breast Cancer Risk, 2009, USA.
o Gopal Karemore et al., Fractal Analysis: In Application to Breast Cancer Risk assessment,
Summer school on manifold learning in image and signal analysis, August 17 - 21, 2009,Ven, Sweden.
o Gopal Karemore, et al., Markov Random Field in Image Segmentation, International Conference on Stochastic Process and Application, July 16, 2007, Indian Institute of Science (IISC), Bangalore.
o Gopal Karemore et al., Analysis of protein profiles using fuzzy clustering methods: Case of malignant and normal cervical tissues, International Symposium on Frontiers in Functional Genomics, IACR Conference.
o Gopal Karemore and Mads Nielsen. Parenchymal texture techniques in application to breast cancer risk assessment. To be submitted:Biomedical Imaging and Intervention Journal, 2013.
o Gopal Karemore, Sami Brandt, Nico Karssemeijer, and Mads Nielsen. A framework to determine mammographic regions that show early changes due to development of breast cancer. To be Submitted: Physics in Medicine and Biology.
o Gopal Karemore, B. Keller, Kontos et al, Computer Aided Detection in Breast Cancer Screening for Estrogen Receptor Subtype Specific Populations, To be Submitted: Medical Physics.
o Mads Nielsen, Celine M Vachon, Christopher G Scott, Konstantin Chernoff, Gopal Karemore,
Nico Karssemeijer, Martin Lillholm and Morten A Karsdal, Mammographic Texture Resemblance generalizes as an independent risk factor for breast cancer (Under Review: Breast Cancer Research) Teaching
Master Level: “Medical Image Analysis-2009 and 2010” at DIKU, Copenhagen Univ., Denmark. Master Level: “Signal and Image Processing- 2010” at DIKU, Copenhagen Univ., Denmark.
I hereby declare that the information furnished above is true to the best of my knowledge. Place: Madrid, Spain. Date: July 10, 2013 (Gopal Karemore)
Experimental and Clinical Evidence for Brimonidine as an Optic Nerve and Retinal Neuroprotective Agent An Evidence-Based Review Meredith Saylor, BA; Linda K. McLoon, PhD; Andrew R. Harrison, MD; Michael S. Lee, MD Objective: To review the available evidence for the neu- for neuroprotection: receptors on its target tissues, ad-roprotective qualities of brimonidine tartrate in optic ne
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