no code implementations • 17 Jan 2023 • Shervin Dehghani, Michael Sommersperger, Peiyao Zhang, Alejandro Martin-Gomez, Benjamin Busam, Peter Gehlbach, Nassir Navab, M. Ali Nasseri, Iulian Iordachita
In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes.
1 code implementation • 15 Apr 2022 • Azade Farshad, Yousef Yeganeh, Peter Gehlbach, Nassir Navab
Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications.
Ranked #1 on Retinal OCT Layer Segmentation on Duke SD-OCT (using extra training data)
no code implementations • 30 Nov 2021 • Shervin Dehghani, Michael Sommersperger, Junjie Yang, Benjamin Busam, Kai Huang, Peter Gehlbach, Iulian Iordachita, Nassir Navab, M. Ali Nasseri
For this purpose, we present a platform for autonomous trocar docking that combines computer vision and a robotic setup.
no code implementations • 16 Nov 2020 • Ji Woong Kim, Changyan He, Muller Urias, Peter Gehlbach, Gregory D. Hager, Iulian Iordachita, Marin Kobilarov
We show that the network can reliably navigate a needle surgical tool to various desired locations within 137 microns accuracy in physical experiments and 94 microns in simulation on average, and generalizes well to unseen situations such as in the presence of auxiliary surgical tools, variable eye backgrounds, and brightness conditions.