most citedDeep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

138 citations · 282 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2017

An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation

Christian F. Baumgartner, Lisa M. Koch, Marc Pollefeys +1

Accurate segmentation of the heart is an important step towards evaluating cardiac function. In this paper, we present a fully automated framework for segmentation of the left (LV)…

cs.CV2017138 cited

Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

Mehmet Turan, Yasin Almalioglu, Helder Araujo +2

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologi…

cs.CV201736 cited

Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots

Mehmet Turan, Yusuf Yigit Pilavci, Ipek Ganiyusufoglu +3

Since the development of capsule endoscopcy technology, substantial progress were made in converting passive capsule endoscopes to robotic active capsule endoscopes which can be co…

cs.CV201720 cited

A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot

Mehmet Turan, Yusuf Yigit Pilavci, Redhwan Jamiruddin +3

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI…

cs.CV201761 cited

A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots

Mehmet Turan, Yasin Almalioglu, Helder Araujo +2

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is considered as a minimally invasive novel diagnostic technology to inspect the entire GI…

cs.CV201727 cited

A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots

Mehmet Turan, Yasin Almalioglu, Ender Konukoglu +1

We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots. Our system mainly focuses on localization of endoscopic capsul…