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About

Spyridon Souipas is a pioneering researcher at the intersection of computer vision and robotic-assisted surgery, with a primary focus on surgical tool localisation, 3D pose estimation, and markerless orthopaedic robotic assistance. His most impactful work, "SimPS-Net: Simultaneous Pose and Segmentation Network of Surgical Tools" (2023, 9 citations), introduces a novel deep learning framework that simultaneously segments surgical instruments and estimates their 3D pose in real time. This contribution is critical for enhancing motion planning in robotic platforms, enabling safer interactions between tools and registered tissue by avoiding potential collisions. Souipas has also made significant strides in markerless orthopaedic systems, as demonstrated in his 2020 study on automated tissue classification using diffuse laser reflectivity (8 citations), which lays the groundwork for isolating rigid bodies like bone without physical markers. His recent work on real-time active constraint generation (2024) further advances collaborative robotics by dynamically defining and enforcing safety boundaries for surgical tools. Collectively, Souipas’s research—garnering over 20 citations—is shaping the future of intelligent, autonomous surgical assistance, promising greater precision and safety in operating rooms.