Max Robotics — Global Service Robot Directory + US Certification

About

Imanol Luengo is a leading researcher in computer vision for surgical robotics, specializing in semantic segmentation, instrument tracking, and scene understanding in minimally invasive surgery. His most impactful contribution is the organization of the 2018 Robotic Scene Segmentation Challenge (119 citations), which pioneered the use of ex-vivo tissue with automatically generated annotations from robot kinematics and CAD models—a foundational dataset for the field. Luengo’s work on real-time multiple surgical tool tracking (18 citations) addresses critical challenges like fast instrument motion and occlusion, enabling applications in video summarization and surgical navigation. He also developed a spatio-temporal network for video semantic segmentation (19 citations), advancing the analysis of dynamic surgical scenes. As a key figure in the Endoscopic Vision Challenge at MICCAI 2020, Luengo has shaped benchmarking standards for surgical AI. His recent leadership in the PhaKIR 2024 challenge (2 citations) demonstrates ongoing commitment to validating surgical phase recognition and instrument instance segmentation. With over 170 total citations, Luengo’s contributions bridge computer vision and clinical practice, providing essential tools for computer-assisted interventions and robotic surgery.