activity
20182025
most citedIMHOTEP - Virtual Reality Framework for Surgical Applications

43 citations · 46 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models

Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer +2

In computer-assisted surgery, automatically recognizing anatomical organs is crucial for understanding the surgical scene and providing intraoperative assistance. While machine lea…

cs.CV2024

SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models

Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer +1

Computer-assisted surgery (CAS) systems are designed to assist surgeons during procedures, thereby reducing complications and enhancing patient care. Training machine learning mode…

cs.CV2024★ 1 cited

An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion

Sharib Ali, Yamid Espinel, Yueming Jin +17

Augmented reality for laparoscopic liver resection is a visualisation mode that allows a surgeon to localise tumours and vessels embedded within the liver by projecting them on top…

cs.CV2023★ 2 cited

Exploring Semantic Consistency in Unpaired Image Translation to Generate Data for Surgical Applications

Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer +4

In surgical computer vision applications, obtaining labeled training data is challenging due to data-privacy concerns and the need for expert annotation. Unpaired image-to-image tr…