RSIP Vision Learns

Deep Learning for Cardiac Ultrasound (Echocardiography)

Despite the importance of echocardiography in the diagnosis and treatment of serious cardiac illness, this imaging technology faces two main challenges: Image quality and image assessment. RSIP Vision uses deep learning to enhance both, making it easier for physicians and researchers to interpret findings. As a result, our method resolves user variability, accuracy and efficiency in cardiac ultrasound with advanced, deep learning neural networks. Learn how we do it on our software.

AI in Cardiac MRI Segmentation

Cardiac magnetic resonance (CMR) imaging plays a critical role in the assessment and management of patients with coronary artery disease (CAD), a leading cause of

AI in Cardiac CT angiography

Coronary computed tomography angiography (CCTA) is an efficient and non-invasive imaging modality with widespread clinical implementation in the identification of coronary artery disease (CAD). With

Endoscopic Procedures using 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.

Using AI to Analyse Intravenous Ultrasound Images

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.

Segmentation in Orthopedics with Deep Learning

Segmentation is highly important both for examination and planning of knee replacement, hip replacement, shoulder surgery, lesion detection, osteotomy and many other orthopedic procedures. Deep Learning is repeatedly being proven to be the most powerful framework for various tasks, and segmentation in orthopedics is no exception. RSIP Vision’s CTO Ilya Kovler explains how to improve the segmentation in orthopedics thanks to AI and deep learning.

Surgical Instrument Segmentation

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.

AI-based Surgical Workflow Analysis, another big step towards the future of robotic surgery

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.

Challenges and AI Solutions for Endoscopy

As endoscopic and microscopic image processing, and surgical vision are evolving as necessary tools for computer assisted interventions (CAI), researchers have recognized the need for

Sensors for ADAS Technology: 3. RADARs

This is a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles and increase security for all. Read about RGB cameras, LiDARs and Radars. RSIP Vision and its engineers have a rich experience in sensors for ADAS systems and for autonomous driving.

Autonomous self-driving car showing Lidar

ADAS sensors: 2. LiDARs

ADAS sensors: 1. RGB cameras

Multiplex IF Analysis

The use of deep learning for analysis of multiplex IF has allowed for a much greater accuracy level for the correct phenotypic classification of cells. When combined with RSIP Vision‘s advanced nuclear detection capability, it allows for the simultaneous analysis of multiple florescent markers on a cell by cell basis. This tool is well suited for multiple applications, especially when using multiple markers to characterize distinct cell populations such as in immune-oncology and IBD.