47 citations · 219 across the 23 of their papers we have counts for
24 papers
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis
Salman Ul Hassan Dar, Arman Ghanaat, Jannik Kahmann +4
Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data…
Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits
Georgii Kostiuchik, Lalith Sharan, Benedikt Mayer +3
Purpose: Machine learning models can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes of inter…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Firas Khader, Gustav Mueller-Franzes, Soroosh Tayebi Arasteh +12
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input…
Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations
Sven Koehler, Tarique Hussain, Hamza Hussain +5
Cardiac magnetic resonance (CMR) sequences visualise the cardiac function voxel-wise over time. Simultaneously, deep learning-based deformable image registration is able to estimat…
Content-Aware Differential Privacy with Conditional Invertible Neural Networks
Malte Tölle, Ullrich Köthe, Florian André +2
Differential privacy (DP) has arisen as the gold standard in protecting an individual's privacy in datasets by adding calibrated noise to each data sample. While the application to…
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection
Lalith Sharan, Halvar Kelm, Gabriele Romano +3
Multi-point tracking is a challenging task that involves detecting points in the scene and tracking them across a sequence of frames. Computing detection-based measures like the F-…
Posterior temperature optimized Bayesian models for inverse problems in medical imaging
Max-Heinrich Laves, Malte Tölle, Alexander Schlaefer +1
We present Posterior Temperature Optimized Bayesian Inverse Models (POTOBIM), an unsupervised Bayesian approach to inverse problems in medical imaging using mean-field variational…
Comparison of Evaluation Metrics for Landmark Detection in CMR Images
Sven Koehler, Lalith Sharan, Julian Kuhm +6
Cardiac Magnetic Resonance (CMR) images are widely used for cardiac diagnosis and ventricular assessment. Extracting specific landmarks like the right ventricular insertion points…
Comparison of Depth Estimation Setups from Stereo Endoscopy and Optical Tracking for Point Measurements
Lukas Burger, Lalith Sharan, Samantha Fischer +7
To support minimally-invasive intraoperative mitral valve repair, quantitative measurements from the valve can be obtained using an infra-red tracked stylus. It is desirable to vie…
Point detection through multi-instance deep heatmap regression for sutures in endoscopy
Lalith Sharan, Gabriele Romano, Julian Brand +4
Purpose: Mitral valve repair is a complex minimally invasive surgery of the heart valve. In this context, suture detection from endoscopic images is a highly relevant task that pro…
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation
Lalith Sharan, Gabriele Romano, Sven Koehler +4
The CycleGAN framework allows for unsupervised image-to-image translation of unpaired data. In a scenario of surgical training on a physical surgical simulator, this method can be…
Posterior Temperature Optimization in Variational Inference for Inverse Problems
Max-Heinrich Laves, Malte Tölle, Alexander Schlaefer +1
Bayesian methods feature useful properties for solving inverse problems, such as tomographic reconstruction. The prior distribution introduces regularization, which helps solving t…
Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks
Sven Koehler, Tarique Hussain, Zach Blair +9
Anisotropic multi-slice Cardiac Magnetic Resonance (CMR) Images are conventionally acquired in patient-specific short-axis (SAX) orientation. In specific cardiovascular diseases th…
Heatmap-based 2D Landmark Detection with a Varying Number of Landmarks
Antonia Stern, Lalith Sharan, Gabriele Romano +5
Mitral valve repair is a surgery to restore the function of the mitral valve. To achieve this, a prosthetic ring is sewed onto the mitral annulus. Analyzing the sutures, which are…
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Zhaohan Xiong, Qing Xia, Zhiqiang Hu +41
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial fir…
How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?
Sven Koehler, Animesh Tandon, Tarique Hussain +7
Planning the optimal time of intervention for pulmonary valve replacement surgery in patients with the congenital heart disease Tetralogy of Fallot (TOF) is mainly based on ventric…
Towards Augmented Reality-based Suturing in Monocular Laparoscopic Training
Chandrakanth Jayachandran Preetha, Jonathan Kloss, Fabian Siegfried Wehrtmann +5
Minimally Invasive Surgery (MIS) techniques have gained rapid popularity among surgeons since they offer significant clinical benefits including reduced recovery time and diminishe…
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
Micha Pfeiffer, Isabel Funke, Maria R. Robu +12
In the medical domain, the lack of large training data sets and benchmarks is often a limiting factor for training deep neural networks. In contrast to expensive manual labeling, c…
Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training
Sandy Engelhardt, Lalith Sharan, Matthias Karck +2
Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the he…
Flexible and Comprehensive Patient-Specific Mitral Valve Silicone Models with Chordae Tendinae Made From 3D-Printable Molds
Sandy Engelhardt, Simon Sauerzapf, Bernhard Preim +3
Given the multitude of challenges surgeons face during mitral valve repair surgery, they should have a high confidence in handling of instruments and in the application of surgical…
Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries
Sandy Engelhardt, Raffaele De Simone, Peter M. Full +2
Current `dry lab' surgical phantom simulators are a valuable tool for surgeons which allows them to improve their dexterity and skill with surgical instruments. These phantoms mimi…
What does it all mean? Capturing Semantics of Surgical Data and Algorithms with Ontologies
Darko Katić, Maria Maleshkova, Sandy Engelhardt +9
Every year approximately 234 million major surgeries are performed, leading to plentiful, highly diverse data. This is accompanied by a matching number of novel algorithms for the…