Lalith Sharan, M.Sc.

Wissenschaftlicher Mitarbeiter, AG Artificial Intelligence in Cardiovascular Medicine, Universitätsklinik Heidelberg, Germany

Scientist, Informatics for Life, Heidelberg, Germany
(www.informatics4life.org)

Masterand, Forschungsprojekt "Computer-based Quantification of Reconstructive Mitral Valve Surgery", Hochschule Mannheim und Universitätsklinik Heidelberg, Germany

Wissenschaftliche Hilfskraft, Forschungsprojekt "Computer-based Quantification of Reconstructive Mitral Valve Surgery", Hochschule Mannheim und Universitätsklinik Heidelberg, Germany

Wissenschaftliche Hilfskraft, Computer Assisted Surgeries (CAS) group, Otto von Guericke Universitaet, Magdeburg, Germany

Bachelorand, Forschungsprojekt "Cognitive state assessment using EEG signals", Institute of Nuclear Medicine and Allied Sciences, New Delhi, India

Praktikant, Siemens Innovation Think Tank (ITT), Siemens Healthcare Pvt. Ltd. (Now Siemens Healthineers), Goa, India

Ph.D. student, AG Artificial Intelligence in Cardiovascular Medicine, Heidelberg University Hospital, Heidelberg, Germany
Focus: Quantitative endoscopic image analysis for mitral valve surgery

Master of Science in Medical Systems Engineering,
Otto von Guericke Universitaet, Magdeburg, Germany
Focus: Computer assisted surgeries, computer vision, deep learning

Bachelor of Engineering in Biomedical Engineering,
Manipal Institute of Technology, Karnataka, India
Focus: Medical image & signal processing, Pattern recognition

Kostiuchik, G., Sharan, L., Mayer, B., Wolf, I., Preim, B., Engelhardt, S.
Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
In: arXiv (https://doi.org/10.48550/arXiv.2306.16879)

Burger, L., Sharan, L., Karl, R., Wang, C., Karck, M., De Simone, R., Wolf, I., Romano, G., Engelhardt, S.
Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation
In: International Journal of Computer Assisted Radiology and Surgery 2023 (https://doi.org/10.1007/s11548-023-02887-1)

Fischer, S., Romano, G., Sharan, L., Warnecke, G., Mereles, D., Karck, M., De Simone, R., Engelhardt, S.
Surgical Rehearsal for Mitral Valve Repair: Personalizing Surgical Simulation by 3D-Printing
In: The Annals of Thoracic Surgery 2023 (https://doi.org/10.1016/j.athoracsur.2022.12.039)

Sharan, L., Romano G., Kelm, H., Karck, M., De Simone, R., Engelhardt, S.
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection
In: AE-CAI | CARE | OR 2.0 JOINT MICCAI WORKSHOP 2022 at MICCAI 2022 (https://arxiv.org/abs/2206.09372)

Koehler, S., Sharan, L., Kuhm, J., Ghanaat, A., Gordejeva, J., Simon, N. K., Grell, N. M., André, F., Engelhardt, S.
Comparison of Evaluation Metrics for Landmark Detection in CMR Images
In: Bildverarbeitung in der Medizin (BVM), Informatik aktuell. Springer Vieweg, Wiesbaden 2022 (https://arxiv.org/abs/2201.10410)

Burger, L., Sharan, L., Fischer, S., Brand, J., Hehl, M., Romano, G., Karck, M., De Simone, R., Wolf, I., Engelhardt, S.
Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements
In: Bildverarbeitung in der Medizin (BVM), Informatik aktuell. Springer Vieweg, Wiesbaden 2022 (https://arxiv.org/abs/2201.10848)

Sharan, L., Romano, G., Brand, J., Kelm, H., Karck, M., De Simone, R., Engelhardt, S.
Point detection through multi-instance deep heatmap regression for sutures in endoscopy
In: International Journal of Computer Assisted Radiology and Surgery 2021 (https://doi.org/10.1007/s11548-021-02523-w)

Sharan, L., Romano, G., Koehler, S., Kelm, H., Karck, M., De Simone, R., Engelhardt, S.
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation
In: IEEE JBHI 2021 (https://doi.org/10.1109/JBHI.2021.3099858)

Stern, A., Sharan, L., Romano, G., Koehler, S., Karck, M., De Simone, R., Wolf, I., Engelhardt, S.
Heatmap-based 2D Landmark Detection with a Varying Number of Landmarks
In: Bildverarbeitung für die Medizin (BVM), Informatik aktuell. Springer Vieweg, Wiesbaden 2021 (https://doi.org/10.1007/978-3-658-33198-6_7)

Engelhardt, S., Sharan, L., Karck, M., De Simone, R., Wolf, I.
Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training
In: Bildverarbeitung für die Medizin (BVM) 2020 (https://doi.org/10.1007/978-3-658-29267-6_75)

Sharan, L., Burger, L., Kostiuchik, G., Wolf, I., Karck, M., De Simone, R., Engelhardt, S.
Domain gap in adapting self-supervised depth estimation methods for stereo-endoscopy
In Current Directions in Biomedical Engineering (CDBME) 2020 - (3rd Place CURAC Best Poster Award) (https://doi.org/10.1515/cdbme-2020-0004)

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)