Kutsev Bengisu Ozyoruk

About

Kutsev Bengisu Ozyoruk is a leading researcher at the intersection of computer vision, deep learning, and medical robotics, with a primary focus on advancing endoscopic imaging and surgical navigation. Her most impactful contribution is the creation of the **EndoSLAM dataset**, a comprehensive benchmark that filled a critical gap in the field by enabling quantitative evaluation of simultaneous localization and mapping (SLAM) and depth estimation methods for endoscopic videos. This foundational work, detailed in her highly cited 2021 paper (240 citations), introduced an unsupervised monocular visual odometry and depth estimation approach (Endo-SfMLearner), demonstrating how deep learning can reconstruct dense topography and estimate camera pose without ground-truth labels. Ozyoruk’s research has directly addressed the lack of standardized evaluation tools, making her work essential for developing more reliable, real-time navigation systems for minimally invasive surgery. She has also explored virtual environments for capsule endoscopy (VR-Caps), pushing the boundaries of simulation-based training and algorithm validation. Her contributions have been recognized as pivotal by the medical robotics and computer vision communities, establishing her as a key innovator in data-driven endoscopic analysis.

Research Focus

Key Achievements

Top Papers

1. 1
   EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos

   240 citations · 2021
2. 2
   EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner

   5 citations · 2020
3. 3
   VR-Caps: A Virtual Environment for Capsule Endoscopy

   4 citations · 2021
4. 4
   Quantitative Evaluation of Endoscopic SLAM Methods: EndoSLAM Dataset.

   3 citations · 2020

Key Collaborators

Contact & Links