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2017 Robotic Instrument Segmentation Challenge
The results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments are presented. Expand
Deep Residual Learning for Instrument Segmentation in Robotic Surgery
This work extends the approach to multi-class segmentation, which lets us segment different parts of the tool, in addition to background, on the MICCAI Endoscopic Vision Challenge Robotic Instruments dataset. Expand
Augmented reality and cone beam CT guidance for transoral robotic surgery
The experimental results show the feasibility of the proposed workflow and advantages of cone beam computed tomography image guidance through video augmentation of the primary stereo endoscopy as compared to control and alternative navigation methods. Expand
2018 Robotic Scene Segmentation Challenge
The robotic instrument segmentation dataset was introduced with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs and added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. Expand
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge
The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China and contains 3 additional methods which were submitted after the challenge finished as well as a supplemental section from these teams on issues they found with the dataset. Expand
Autonomous Image-Guided Robot-Assisted Active Catheter Insertion
Interventional cardiologists are at great risk from radiation exposure due to lengthy procedures performed under X-ray radiations. Angioplasty is one such procedure wherein the clinician guides aExpand
Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources
Fusion-KVE is proposed, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events and achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgicalstate estimation models in both JIGSAWS suturing dataset and the authors' RIOUS dataset. Expand
Stereoscopic augmented reality for laparoscopic surgery
This work presents the first in vivo use of a complete system with stereoscopic AR visualization capability, and shows promise to improve the precision and expand the capacity of minimally invasive laparoscopic surgeries. Expand
Cadaveric feasibility study of da Vinci Si-assisted cochlear implant with augmented visual navigation for otologic surgery.
The described system for cochleostomy has the potential to improve the surgeon's confidence, as well as surgical safety, efficiency, and precision by filtering tremor, and the integration of augmented reality may be valuable for surgeons dealing with complex cases of congenital anatomic abnormality. Expand
Visual servoing in medical robotics: a survey. Part I: endoscopic and direct vision imaging – techniques and applications
Intra‐operative imaging is widely used to provide visual feedback to a clinician when he/she performs a procedure. In visual servoing, surgical instruments and parts of tissue/body are tracked byExpand