AI for Endoscopy

AI in Endoscopy is a new field, utilizing innovative AI technologies to enhance the accuracy of Endoscopy imaging and provide better detection and clinical outcomes. RSIP Vision is a pioneer in the field of artificial intelligence for endoscopy imaging. Thanks to our deep learning solutions, radiologists and physicians obtain much more information than before from endoscopy images for both detection of diseases and measurement of therapy progress effectiveness. We have successfully completed a large number of important R&D projects for all kind of minimally invasive procedures. Contact us and discuss your project with our experts!

Surgical Tool Segmentation in Endoscopy

Using computer vision to identify tools being employed at different stages of a procedure is not only another step toward robotic surgery, it’s a simple, yet very useful tool to streamline and safeguard the surgical process. Surgical instrument (tool) segmentation and classification is a computer vision algorithm that complements workflow analysis. It automatically detects and identifies tools used during the procedure, and assess whether they are used by the surgeon correctly. Read more...

Surgical Workflow Analysis

Surgical workflow analysis is an important safety guard for the surgeon: with it, a computer is able to scan a video of a surgery, either offline after it has already been performed or online during the surgery itself, and automatically identify at what stage the surgery is at. Read about RSIP Vision's approach, built on many years of experience in the development of practical applications.   Read more...

Intravenous Ultrasound

Intravenous ultrasound (IVUS) has been used for many years in the diagnosis of cardiovascular diseases. The recent use of deep learning based on convolutional neural networks has shown improved accuracy, and has also enabled additional applications such as plaque detection. Read more...

Intravenous OCT

Recently, OCT has emerged as an alternative modality that provides high resolution images. While ultrasound imaging cannot be replaced, adding Intravenous Optical Coherence Tomography (IVOCT) to endoscopy procedures significantly improves image resolution and increases the ability to detect plaque and segment it. Read more...

Endoscopic Vision Challenge

EndoVis (the MICCAI workshop group researching AI in endoscopy) has posted a vision CAI (Computer-Assisted Interventions) challenge to evaluate the current state of the art, gather researchers in the field and provide high quality data for validating endoscopic vision algorithms. We have asked Daniel Tomer, AI team leader at RSIP Vision, to comment the EndoVis challenge and its three AI sub-challenges. Read More...

Real-time SLAM in Endoscopy Applications

A family of algorithms called simultaneous localization and mapping (SLAM) are able, in real time, to create a 3D map of a scene captured by a camera and calculate with very high accuracy the location of the camera in the scene. As a result, RSIP Vision's engineers are able to create a precise 3D model of the endoscope environment and calculate its exact location in that model. Read more...

AI for Gastric Cancer Detection

Upper gastrointestinal cancers, including esophageal cancer and gastric cancer, are among the most common cancers worldwide. However, a lack of endoscopists with colonoscopy skills has been identified and solutions are critically needed. The development of a real-time robust detection system for colorectal neoplasms is needed to significantly reduce the risk of missed lesions during colonoscopy. Read More...