Manish Sahu
Papers
8
Total Citations
83
H-Index
6
About
Manish Sahu is pioneering the intersection of deep learning, haptics, and robotics to make skull base and otologic surgery safer and more precise. His research centers on developing intelligent, autonomous systems for image-guided surgical interventions, with a major focus on automated segmentation of complex temporal bone anatomy using self-configuring deep learning networks—a contribution that has garnered 29 citations for its potential to streamline preoperative planning. Sahu’s work extends into real-time force sensing and haptic feedback, exemplified by his force-sensing surgical drill for robotic mastoidectomy (12 citations) and a haptic-assisted collaborative robot framework that enhances situational awareness during skull base procedures (9 citations). He has also advanced instrument state recognition and tremor assessment in robot-assisted microlaryngeal surgery, using computer vision for tool tracking (6 citations). With a label-efficient framework for sinonasal CT segmentation (4 citations) and the ENTRI toolkit for robotic interventions (3 citations), Sahu is building the foundational technologies for next-generation, situationally aware surgical robots that augment human skill while minimizing risk.
Research Focus
Key Achievements
Top Papers
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- 8ENTRI: Enhanced Navigational Toolkit for Robotic Interventions3 citations · 2024