Eva Curto

Institute for Systems Engineering and Computers

Papers

Total Citations

248

H-Index

About

Dr. Eva Curto is a leading researcher in the intersection of computer vision and medical robotics, with a primary focus on advancing endoscopic surgery through Simultaneous Localization and Mapping (SLAM) and deep learning. Her most significant contribution is the creation of the **EndoSLAM dataset**, a comprehensive benchmark that addresses a critical gap in the field—the lack of standardized, quantitative evaluation tools for endoscopic SLAM methods. This work, detailed in her highly cited 2021 paper (240 citations), provides the first large-scale dataset with ground truth for depth and pose, enabling rigorous comparison of algorithms. Dr. Curto’s research also pioneers unsupervised monocular visual odometry and depth estimation techniques, such as Endo-SfMLearner, which allow for dense 3D topography reconstruction without requiring costly labeled data. By providing both the data and the learning framework, she has laid the essential groundwork for developing more robust, real-time navigation systems in minimally invasive surgery. Her contributions are pivotal for moving computer-assisted endoscopy from theoretical promise toward practical, clinical deployment.