Anirban Mukhopadhyay

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About

Anirban Mukhopadhyay’s research lies at the intersection of surgical robotics, medical image analysis, and intraoperative planning, with a focus on enabling safer, more precise minimally invasive procedures. His major contributions include developing novel trajectory planning methods for flexible surgical robots, such as using Bézier spline translation to allow nonlinear navigation in confined anatomical spaces like the temporal bone and cochlea—critical for reducing invasiveness and improving access. His work on preoperative optimization for robot-assisted temporal bone surgery has been validated through retrospective in silico evaluations, demonstrating potential to enhance clinical safety. Mukhopadhyay also advanced instrument pose estimation with i3PosNet, a deep learning framework that estimates surgical tool position and orientation from X-ray images, addressing limitations of conventional optical and electromagnetic tracking systems. Additionally, his research on shape-regularized segmentation for guidewire planning integrates image processing with trajectory optimization. With over 30 citations across his most-cited papers, his contributions are shaping the future of autonomous surgical navigation and real-time intraoperative guidance. His achievements highlight a commitment to translating computational methods into practical tools that improve surgical outcomes and patient safety.