Benoit Rosa
20 papers 183 citations h-index 6 2011–2026
Semi-supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images
Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During...
FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation
Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art...
Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing
Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video. The...
A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Images
3-D pose estimation of instruments is a crucial step towards automatic scene understanding in robotic minimally invasive surgery. Although robotic systems can potentially directly provide joint...
Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy
Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma (UTUC). During ureteroscopy, the automatic...
Self-Supervised Surgical Tool Segmentation using Kinematic Information
Surgical tool segmentation in endoscopic images is the first step towards pose estimation and (sub-)task automation in challenging minimally invasive surgical operations. While many approaches in...
A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture
Ureteroscopy is becoming the first surgical treatment option for the majority of urinary affections. This procedure is performed using an endoscope which provides the surgeon with the visual...