47 citations · 103 across the 8 of their papers we have counts for
10 papers
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…
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…