Bhushan Borotikar
Also known as: Borotikar BS
Symbiosis International University, LaTIM INSERM U1101, Cleveland State University, The University of Texas at Arlington, National Institutes of Health
About
I am an Associate Professor of research at Symbiosis Centre for Medical Image Analysis, Symbiosis International University in Pune, India. I also hold adjuct Assoc. Prof. position at the University of Cape Town, South Africa and adjunct Sr. Research Scientist position at the French National Institute of Health’s research laboratory (LaTIM, INSERM UMR 1101, Brest, France). My research theme seeks to develop an integrated research and educational framework for evaluation and treatment of muscle, bone and joint diseases and disorders in adult and pediatric population in a functional setting. I develop and implement clinical diagnostic devices and tools based on my expertise in AI, Joint Biomechanics, Machine Learning, Medical Image Analysis, Computational Modeling, Advanced imaging techniques, Mixed Reality, and Clinical Sciences. My global research focus is directed towards developing orthopedic tools through computational imaging, computational modeling, computational anatomy, experimental research, and device designs.
My fundamental research at SCMIA is focused on developing model-based frameworks to perform quantitative medical image analysis and also designing novel medical imaging sequences (on CT, MRI, and X-ray) for the same. Under fundamental research, my group tackle problems related to medical image acquisition (for e.g., image quality and resolution), image analysis (for e.g., image registration, neuro image analysis), and image synthesis (for e.g., MRI to synthetic CT). So far, we have developed multiple novel approaches such as machine learning-based multi-feature Gaussian Process Morphable Modeling (GPMM) tools (DMFC_GPM framework), novel and data-driven deep learning approaches (Multi-task, Multi-domain Deep Segmentation) to name a few. These frameworks are designed keeping the large-scale research applications in mind. Applied research at SCMIA is currently focused in two major group of disorders that affect the human body – neurological disorders and musculoskeletal disorders. Under applied research, we perform large-scale research using imaging and experimental datasets either publicly available or being generated within the collaborative scientific partners of SCMIA that are globally placed. Major clinical focus includes Parkinson’s disease, Bone and Joint disorders, Autism spectrum disorders, brain tumors, Cerebral Palsy, obesity, Duchenne Muscular Dystrophy, and total joint replacement surgeries (hip, knee, shoulder, ankle etc.).
Employment
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Symbiosis International University Associate Professor2020 - Present
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LaTIM INSERM U1101 SENIOR RESEARCH SCIENTIST2014 - 2020
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National Institutes of Health Visiting postdoctoral fellow2010 - 2014
Education
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Cleveland State University Doctor of Engineering2004 - 2009
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The University of Texas at Arlington MS2001 - 2003
Projects & Funding
Projects & funding information is unavailable.
Publications (23)
- Nutritional and lifestyle determinants of Sarcopenia in Adult Asian Patients with Chronic Kidney Disease: A Protocol for Systematic Narrative Review Save
- Impact of obesity on human brain metabolites: a systematic review on magnetic resonance spectroscopy studies Save
- Deep learning methods to analyse knee osteoarthritis using magnetic resonance imaging: a systematic review and synthesis without meta-analysis Save
- Landmark-Constrained Multi-Object Model Fitting. An Application for 3D Reconstruction of X-Ray Images of the Human Foot Save
- Impact of obesity on brain structure: A critical review of the evidence from Magnetic Resonance imaging studies Save
- Learning disentangled representations for unpaired synthesis of high-resolution dynamic MRI Save
- Capturing Complexity of the Foot Arch Bones: Evaluation of a Statistical Modelling Framework for Learning Shape, Pose and Intensity Features in a Continuous Domain Save
- Dynamic multi feature-class Gaussian process models Save
- Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network Save
- Anatomically Parameterized Statistical Shape Model: Explaining Morphometry Through Statistical Learning Save