73 citations · 195 across the 23 of their papers we have counts for
24 papers
Occupancy Network-Guided Autonomous Robotic Partial Nephrectomy
Ethan Kilmer, Pit Henrich, Jiawei Ge +13
Autonomous soft-tissue cancer surgery has been limited to interventions on organ surfaces, because current systems cannot perceive and adapt to anatomy once it deforms or is cut. W…
Cross-Modal MRI Ovary Segmentation in Endometriosis Using Unpaired TVUS Prototype Priors
Xingjian Kang, Lina Felsner, Dominik Perrin +5
Transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) provide complementary information for endometriosis image analysis, yet existing studies mainly focus on single-…
A Compact Top-Loading Robot for Endovascular Interventions: Design, Control and Evaluation
Jonas Fischer, Lennart Karstensen, Franziska Mathis-Ullrich
Robot-assisted endovascular intervention can potentially reduce radiation exposure, improve surgeon ergonomics, enable telesurgery, support active assistance and autonomy, and enha…
Comparing Commercial Depth Sensor Accuracy for Medical Applications
Pit Henrich, Maximilian Weiherer, Franziska Hansen +2
Depth estimation has numerous medical and surgical applications. We benchmark four depth sensors on a porcine bone specimen, a porcine belly specimen, and a silicone kidney phantom…
Point Cloud Segmentation for Autonomous Clip Positioning in Laparoscopic Cholecystectomy on a Phantom
Balázs Gyenes, Nikolai Franke, Paul Maria Scheikl +5
High-risk applications in robotics, such as robot-assisted surgery, present unique challenges. These systems must be both highly precise and interpretable in order to be deployed i…
A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on behalf of the Stakeholder Taskforce for AI-assisted Robotic Thrombectomy (START)
Harry Robertshaw, Anna Barnes, Phil Blakelock +20
While we are making progress in overcoming infectious diseases and cancer; one of the major medical challenges of the mid-21st century will be the rising prevalence of stroke. Larg…
Ensemble Learning and 3D Pix2Pix for Comprehensive Brain Tumor Analysis in Multimodal MRI
Ramy A. Zeineldin, Franziska Mathis-Ullrich
Motivated by the need for advanced solutions in the segmentation and inpainting of glioma-affected brain regions in multi-modal magnetic resonance imaging (MRI), this study present…
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors
Ramy A. Zeineldin, Franziska Mathis-Ullrich
Accurate segmentation of brain tumors from 3D multimodal MRI is vital for diagnosis and treatment planning across diverse brain tumors. This paper addresses the challenges posed by…
Tracking Tumors under Deformation from Partial Point Clouds using Occupancy Networks
Pit Henrich, Jiawei Liu, Jiawei Ge +5
To track tumors during surgery, information from preoperative CT scans is used to determine their position. However, as the surgeon operates, the tumor may be deformed which presen…
LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions
Pit Henrich, Franziska Mathis-Ullrich, Paul Maria Scheikl
Accurately determining the shape of deformable objects and the location of their internal structures is crucial for medical tasks that require precise targeting, such as robotic bi…
Learning-Based Autonomous Navigation, Benchmark Environments and Simulation Framework for Endovascular Interventions
Lennart Karstensen, Harry Robertshaw, Johannes Hatzl +8
Endovascular interventions are a life-saving treatment for many diseases, yet suffer from drawbacks such as radiation exposure and potential scarcity of proficient physicians. Robo…
Interactive Surgical Liver Phantom for Cholecystectomy Training
Alexander Schuessler, Rayan Younis, Jamie Paik +3
Training and prototype development in robot-assisted surgery requires appropriate and safe environments for the execution of surgical procedures. Current dry lab laparoscopy phanto…
Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery
Paul Maria Scheikl, Eleonora Tagliabue, Balázs Gyenes +4
Automation holds the potential to assist surgeons in robotic interventions, shifting their mental work load from visuomotor control to high level decision making. Reinforcement lea…
LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images
Pit Henrich, Franziska Mathis-Ullrich
We introduce a novel approach for the precise localization of 67 anatomical structures from single depth images captured from the exterior of the human body. Our method uses a mult…
Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
Paul Maria Scheikl, Nicolas Schreiber, Christoph Haas +4
Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions. To this end,…
Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud
Pit Henrich, Balázs Gyenes, Paul Maria Scheikl +2
In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object. We thus require a syste…
Grounding Graph Network Simulators using Physical Sensor Observations
Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl +2
Physical simulations that accurately model reality are crucial for many engineering disciplines such as mechanical engineering and robotic motion planning. In recent years, learned…
LapGym -- An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery
Paul Maria Scheikl, Balázs Gyenes, Rayan Younis +4
Recent advances in reinforcement learning (RL) have increased the promise of introducing cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS). However,…
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution
Ramy A. Zeineldin, Mohamed E. Karar, Oliver Burgert +1
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their…
Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients
Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich +1
Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes. This pap…
LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes
Benjamin Alt, Christian Kunz, Darko Katic +5
The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wi…
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI
Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich +1
Glioblastomas are the most aggressive fast-growing primary brain cancer which originate in the glial cells of the brain. Accurate identification of the malignant brain tumor and it…
Cooperative Assistance in Robotic Surgery through Multi-Agent Reinforcement Learning
Paul Maria Scheikl, Balázs Gyenes, Tornike Davitashvili +6
Cognitive cooperative assistance in robot-assisted surgery holds the potential to increase quality of care in minimally invasive interventions. Automation of surgical tasks promise…
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
Martin Wagner, Beat-Peter Müller-Stich, Anna Kisilenko +38
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the…