2 citations · 4 across the 20 of their papers we have counts for
7 papers · 1 filter
XiCAD: Camera Activation Detection in the Da Vinci Xi User Interface
Alexander C. Jenke, Gregor Just, Claas de Boer +3
Purpose: Robot-assisted minimally invasive surgery relies on endoscopic video as the sole intraoperative visual feedback. The DaVinci Xi system overlays a graphical user interface…
Three-Dimensional Anatomical Data Generation Based on Artificial Neural Networks
Ann-Sophia Müller, Moonkwang Jeong, Meng Zhang +4
Surgical planning and training based on machine learning requires a large amount of 3D anatomical models reconstructed from medical imaging, which is currently one of the major bot…
Current validation practice undermines surgical AI development
Annika Reinke, Ziying O. Li, Minu D. Tizabi +98
Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation as an important contr…
Towards Application Aligned Synthetic Surgical Image Synthesis
Danush Kumar Venkatesh, Stefanie Speidel
The scarcity of annotated surgical data poses a significant challenge for developing deep learning systems in computer-assisted interventions. While diffusion models can synthesize…
PIVOTS: Aligning unseen Structures using Preoperative to Intraoperative Volume-To-Surface Registration for Liver Navigation
Peng Liu, Bianca Güttner, Yutong Su +16
Non-rigid registration is essential for Augmented Reality guided laparoscopic liver surgery by fusing preoperative information, such as tumor location and vascular structures, into…
Mission Balance: Generating Under-represented Class Samples using Video Diffusion Models
Danush Kumar Venkatesh, Isabel Funke, Micha Pfeiffer +5
Computer-assisted interventions can improve intra-operative guidance, particularly through deep learning methods that harness the spatiotemporal information in surgical videos. How…
Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections
Max Kirchner, Alexander C. Jenke, Sebastian Bodenstedt +5
Purpose: Data privacy regulations hinder the creation of generalizable foundation models (FMs) for surgery by preventing multi-institutional data aggregation. This study investigat…