Maximilian Allan

Papers

Total Citations

198

H-Index

About

Maximilian Allan is a leading researcher in computer vision for computer-assisted interventions, with a primary focus on surgical instrument tracking and pose estimation. His work addresses critical challenges in minimally invasive surgery (MIS) and robotic-assisted procedures. Allan’s most influential contribution, "Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks" (2018, 136 citations), pioneered deep learning methods for detecting and tracking articulated surgical instruments in video, overcoming a major hurdle in articulation detection. His earlier foundational work, "Combined 2D and 3D tracking of surgical instruments for minimally invasive and robotic-assisted surgery" (2016, 62 citations), established vision-based approaches as a viable, hardware-minimal solution for instrument tracking in both conventional and robotic MIS. Together, these contributions have significantly advanced the field, enabling more precise, automated analysis of surgical workflows. Allan’s research is highly cited, reflecting its impact on developing robust, real-time systems that enhance surgical precision and safety. His work continues to shape the future of computer-assisted interventions, making him a key figure in the intersection of deep learning and surgical technology.