activity
20222026
most citedAirway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge

11 citations · 22 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2026

A New Angle on Bones: Robust Pose Estimation in X-Ray and Ultrasound

Ron Keuth, Christoph Großbröhmer, Franziska Halm +5

Measuring the angle between bone structures is a routine task in medical image analysis and provides a key quantitative parameter for diagnosis and treatment planning. Automated me…

cs.CV2025

Fracture Morphology Classification: Local Multiclass Modeling for Multilabel Complexity

Cassandra Krause, Mattias P. Heinrich, Ron Keuth

Between and of children experience a fracture during their growth years, making accurate diagnosis essential. Fracture morphology, alongside location and fragment…

cs.CV2025

Shaken or Stirred? An Analysis of MetaFormer's Token Mixing for Medical Imaging

Ron Keuth, Paul Kaftan, Mattias P. Heinrich

The generalization of the Transformer architecture via MetaFormer has reshaped our understanding of its success in computer vision. By replacing self-attention with simpler token m…

eess.IV2025★ 3 cited

BronchoGAN: Anatomically consistent and domain-agnostic image-to-image translation for video bronchoscopy

Ahmad Soliman, Ron Keuth, Marian Himstedt

The limited availability of bronchoscopy images makes image synthesis particularly interesting for training deep learning models. Robust image translation across different domains…

cs.CV2024

A Systematic Analysis of Input Modalities for Fracture Classification of the Paediatric Wrist

Ron Keuth, Maren Balks, Sebastian Tschauner +2

Fractures, particularly in the distal forearm, are among the most common injuries in children and adolescents, with approximately 800 000 cases treated annually in Germany. The AO/…

cs.CV2024

SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation

Ron Keuth, Lasse Hansen, Maren Balks +4

Semantic segmentation is a crucial task in medical imaging. Although supervised learning techniques have proven to be effective in performing this task, they heavily depend on larg…

cs.CV2024

DenseSeg: Joint Learning for Semantic Segmentation and Landmark Detection Using Dense Image-to-Shape Representation

Ron Keuth, Lasse Hansen, Maren Balks +4

Purpose: Semantic segmentation and landmark detection are fundamental tasks of medical image processing, facilitating further analysis of anatomical objects. Although deep learning…

cs.CV2024

Combining Image- and Geometric-based Deep Learning for Shape Regression: A Comparison to Pixel-level Methods for Segmentation in Chest X-Ray

Ron Keuth, Mattias Heinrich

When solving a segmentation task, shaped-base methods can be beneficial compared to pixelwise classification due to geometric understanding of the target object as shape, preventin…

cs.CV2023★ 11 cited

Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge

Ron Keuth, Mattias Heinrich, Martin Eichenlaub +1

Purpose: Navigation guidance is a key requirement for a multitude of lung interventions using video bronchoscopy. State-of-the-art solutions focus on lung biopsies using electromag…

cs.CV2022★ 8 cited

Weakly Supervised Airway Orifice Segmentation in Video Bronchoscopy

Ron Keuth, Mattias Heinrich, Martin Eichenlaub +1

Video bronchoscopy is routinely conducted for biopsies of lung tissue suspected for cancer, monitoring of COPD patients and clarification of acute respiratory problems at intensive…