Lalith Sharan, M.Sc.
Scientific Staff
(Clinic for Cardiac Surgery)
Scientific Staff
(AG Artificial Intelligence in Cardiovascular Medicine)
Scientific Staff
(Clinic for Cardiology, Angiology and Pneumology)
Focus
AI and AR assisted computational support for mitral valve repair
Medical/Professional background
- since November 2019
Wissenschaftlicher Mitarbeiter, AG Artificial Intelligence in Cardiovascular Medicine, Universitätsklinik Heidelberg, Germany
- since November 2019
Scientist, Informatics for Life, Heidelberg, Germany
(www.informatics4life.org)- April 2019 – September 2019
Masterand, Forschungsprojekt "Computer-based Quantification of Reconstructive Mitral Valve Surgery", Hochschule Mannheim und Universitätsklinik Heidelberg, Germany
- October 2018 – March 2019
Wissenschaftliche Hilfskraft, Forschungsprojekt "Computer-based Quantification of Reconstructive Mitral Valve Surgery", Hochschule Mannheim und Universitätsklinik Heidelberg, Germany
- January 2018 – September 2018
Wissenschaftliche Hilfskraft, Computer Assisted Surgeries (CAS) group, Otto von Guericke Universitaet, Magdeburg, Germany
- January 2014 – May 2014
Bachelorand, Forschungsprojekt "Cognitive state assessment using EEG signals", Institute of Nuclear Medicine and Allied Sciences, New Delhi, India
- May 2013 – August 2013
Praktikant, Siemens Innovation Think Tank (ITT), Siemens Healthcare Pvt. Ltd. (Now Siemens Healthineers), Goa, India
Scientific background
- since November 2019
Ph.D. student, AG Artificial Intelligence in Cardiovascular Medicine, Heidelberg University Hospital, Heidelberg, Germany
Focus: Quantitative endoscopic image analysis for mitral valve surgery- April 2017 – October 2019
Master of Science in Medical Systems Engineering,
Otto von Guericke Universitaet, Magdeburg, Germany
Focus: Computer assisted surgeries, computer vision, deep learning- July 2010 – May 2014
Bachelor of Engineering in Biomedical Engineering,
Manipal Institute of Technology, Karnataka, India
Focus: Medical image & signal processing, Pattern recognition
Publikationen
Preetha, C.J., Wehrtmann, F.S., Sharan, L., Fan, C., Kloss, J., Müller-Stich, B.P., Nickel, F., Engelhardt, S., Towards augmented reality-based suturing in monocular laparoscopic training. Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113150X (16 March 2020); (https://doi.org/10.1117/12.2550830)
Engelhardt, S., Sharan, L., Karck, M., De Simone, R., Wolf, I., Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11768. Springer, Cham, pp 155-163, (doi.org/10.1007/978-3-030-32254-0_18)