39 citations · 88 across the 11 of their papers we have counts for
12 papers
AI for Quality Assurance in the Operating Room
Pietro Mascagni, Lalith Sharan, Deepak Alapatt +1
Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself. Yet, for much of mod-ern surger…
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…
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…
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-…
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…
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge
Max Allan, Jonathan Mcleod, Congcong Wang +21
The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China. The task was to perform…
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…
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…
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…