446 citations · 2.3k across the 135 of their papers we have counts for
18 papers · 1 filter
SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
Wadim Kehl, Fabian Manhardt, Federico Tombari +2
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover th…
SUPRA: Open Source Software Defined Ultrasound Processing for Real-Time Applications
Rüdiger Göbl, Nassir Navab, Christoph Hennersperger
Research in ultrasound imaging is limited in reproducibility by two factors: First, many existing ultrasound pipelines are protected by intellectual property, rendering exchange of…
Technical Note: Towards Virtual Monitors for Image Guided Interventions - Real-time Streaming to Optical See-Through Head-Mounted Displays
Long Qian, Mathias Unberath, Kevin Yu +4
Purpose: Image guidance is crucial for the success of many interventions. Images are displayed on designated monitors that cannot be positioned optimally due to sterility and spati…
Multi-layer Visualization for Medical Mixed Reality
Séverine Habert, Ma Meng, Pascal Fallavollita +1
Medical Mixed Reality helps surgeons to contextualize intraoperative data with video of the surgical scene. Nonetheless, the surgical scene and anatomical target are often occluded…
6D Object Pose Estimation with Depth Images: A Seamless Approach for Robotic Interaction and Augmented Reality
David Joseph Tan, Nassir Navab, Federico Tombari
To determine the 3D orientation and 3D location of objects in the surroundings of a camera mounted on a robot or mobile device, we developed two powerful algorithms in object detec…
Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization
Huseyin Coskun, Felix Achilles, Robert DiPietro +2
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used…
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Abhijit Guha Roy, Sailesh Conjeti, Debdoot Sheet +3
Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data ar…
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Gerda Bortsova, Gijs van Tulder, Florian Dubost +5
Intracranial carotid artery calcification (ICAC) is a major risk factor for stroke, and might contribute to dementia and cognitive decline. Reliance on time-consuming manual annota…
Learning-based Surgical Workflow Detection from Intra-Operative Signals
Ralf Stauder, Ergün Kayis, Nassir Navab
A modern operating room (OR) provides a plethora of advanced medical devices. In order to better facilitate the information offered by them, they need to automatically react to the…
Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions
Benjamin Busam, Tolga Birdal, Nassir Navab
Time-varying, smooth trajectory estimation is of great interest to the vision community for accurate and well behaving 3D systems. In this paper, we propose a novel principal compo…
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Abhijit Guha Roy, Sailesh Conjeti, Sri Phani Krishna Karri +4
Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers. In this paper, we propose a new fully convolutional de…
Deep Residual Learning for Instrument Segmentation in Robotic Surgery
Daniil Pakhomov, Vittal Premachandran, Max Allan +2
Detection, tracking, and pose estimation of surgical instruments are crucial tasks for computer assistance during minimally invasive robotic surgery. In the majority of cases, the…
Learning Robust Hash Codes for Multiple Instance Image Retrieval
Sailesh Conjeti, Magdalini Paschali, Amin Katouzian +1
In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for la…
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Iro Laina, Nicola Rieke, Christian Rupprecht +4
Real-time instrument tracking is a crucial requirement for various computer-assisted interventions. In order to overcome problems such as specular reflections and motion blur, we p…
Semi-Supervised Deep Learning for Fully Convolutional Networks
Christoph Baur, Shadi Albarqouni, Nassir Navab
Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to us…
Alignment of the Virtual Scene to the Tracking Space of a Mixed Reality Head-Mounted Display
Ehsan Azimi, Long Qian, Nassir Navab +1
With the mounting global interest for optical see-through head-mounted displays (OST-HMDs) across medical, industrial and entertainment settings, many systems with different capabi…
Quantitative Characterization of Components of Computer Assisted Interventions
Asli Okur, Ralf Stauder, Hubertus Feussner +1
Purpose: We propose a mathematical framework for quantitative analysis weighting the impact of heterogeneous components of a surgery. While multi-level approaches, surgical process…
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies
Robert DiPietro, Christian Rupprecht, Nassir Navab +1
Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to c…