Cardiology Archives - RSIP Vision

Announcement – Non-Invasive Planning of Coronary Intervention

RSIP Vision Presents New Technology for Non-Invasive Planning of Coronary Intervention Innovative technology provides accurate coronary artery 3D reconstruction from 2D angiography to be used

Announcement – Robust Metal Implant and Anatomical Segmentation Tool

RSIP Vision Unveils Robust Metal Implant and Anatomical Segmentation Tool, for Improved Planning of Specialized Orthopedic Procedures including Revision Arthroplasty Groundbreaking Module Joins RSIP Vision’s Existing

Announcement – Coronary Artery Analysis

RSIP Vision Announces Sophisticated AI-Based Tool for Coronary Artery Analysis and Intervention Planning New module utilizes state-of-the-art deep learning algorithms combined with classic computer vision

Announcement – RSIP Vision Announces New Cardiac Diagnostic Tool for Point-of-Care Ultrasound Screening

New algorithmic module provides automated expert-level assessment of heart function for point-of-care medical teams enabling a quick and reliable detection of cardiac illness and heart

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

Coronary Arteries Segmentation

Coronary artery disease (CAD) or ischemic heart disease (IHD) has become one of the most common causes of morbidity and mortality worldwide. Patients who suffer

Great Vessels Segmentation with Deep Learning

The great vessels conduct blood to and from the heart. These vessels include the aorta, superior and inferior vena cava, pulmonary arteries and pulmonary veins.

In a heartbeat: Implementing AI in cardiology

Hosts: Moshe Safran and Miki Haimovich – RSIP Vision We’ll discuss Implementing AI in diagnostics as well as intraoperative cardiology applications. “In cardiology, there is increasing use of noninvasive

Deep Learning in Cardiology

1.1 Segmentation tasks [10] suggest a new fully convolutional network architecture for the task of cardiovascular MRI segmentation. The architecture is based on the idea

3D Cardiac MRI automatic segmentation

3D cardiac segmentation from MRI is a precious tool in the hand of the physician to assess pathologies and treatment. RSIP Vision employ Artificial Intelligence techniques for cardiology in order to perform 3D automatic cardiac segmentation. Deep Learning and Convolutional Neural Networks are called in to achieve state-of-the-art accuracy in the fastest time. This article and the accompanying video explain the challenges presented by this task and the way our algorithms provide a world-class solution.

Three-dimensional reconstruction

Three-dimensional reconstruction of scene can be viewed as is the reproduction of a depth-map. Thus, as an input we have a sequence of images as

Cardiovascular Ultrasound Software

The software created by RSIP Vision uses segmentation algorithms that isolate heart walls in a noisy ultrasound image. The combination of RSIP’s image processing software with cardiovascular ultrasound provides detailed noninvasive heart monitoring system, transforming a noisy image into a clear view of the heart ventricles, enabling the medical professional to witness heart function and activities.

3D reconstruction of the heart

Using minimal 2D images from a routine angiography, RSIP Vision’s algorithm tracks the veins and constructs a route map of the arteries, from two different angles. Our sophisticated algorithms applied to computer vision in cardiology reconstruct 3D models of the heart artery tree from 2D images, allowing for improved diagnostic capabilities, lower risk to both patients and physicians, more effective therapy and reduced costs.

Coronary CT Angiography with Deep Learning

The production of 3D CT images of the heart requires a fast image processing technology, applied simultaneously on multiple scanned layers. To automatically separate the different components of the image, our software locates in the images the muscular layer (myocardium) of the heart needed for the rest of the segmentation in coronary CT angiography. Minimum graph cuts is the technique which provides the clearest tracking and the strongest segmentation results.

Cardiac Left Ventricle Segmentation

Cardiac MRI is used to assess the status of patients suffering from heart diseases like cardiac masses and thrombi, aortic and/or different kinds of congenital cardiac diseases. Cardiac MRI is minimally invasive, does not involve radiation and it generally delivers excellent images for diagnostics. However, automatic segmentation of the left ventricle on MRI images faces challenging difficulties, like locating the left ventricle and overcoming the lack of edge information. We propose a method providing a very robust input for daily clinical application.

Automatic Catheter Orientation Measurement

Catheters are inserted with measurement equipment at their tips, in order to scan their immediate surroundings. While orientation of the catheter’s tip is unknown throughout insertion, RSIP Vision has employed advanced algorithmic techniques to provide an exact measurement of catheter orientation during angiography, enabling the physician to ascertain the orientation of the catheter’s tip from x-ray images.

Quantitative Coronary Analysis

The main contribution of Quantitative Coronary Analysis (QCA) consists in measuring the diameter of arteries. Angiograms provide coronary images of region suspected of lesions using which our advanced algorithms for vessel detection and segmentation measure the segmented artery’s diameter. Abnormal values (as compared to a constructed reference diameter) are suspected as stenosis. Our system extracts and displays relevant values to the view of medical professionals and their patients.

Right Atrium Measurement with Ultrasound

Right Atrium Measurement in Ultrasound Videos Atrial fibrillation is an irregular rhythmic beating of the heart associated with coronary heart disease, high blood pressure

Tag: Cardiology

Announcement – Non-Invasive Planning of Coronary Intervention

Announcement – Robust Metal Implant and Anatomical Segmentation Tool

Announcement – Coronary Artery Analysis

Announcement – RSIP Vision Announces New Cardiac Diagnostic Tool for Point-of-Care Ultrasound Screening

Deep Learning for Cardiac Ultrasound (Echocardiography)

AI in Cardiac MRI Segmentation

AI in Cardiac CT angiography

Coronary Arteries Segmentation

Great Vessels Segmentation with Deep Learning

In a heartbeat: Implementing AI in cardiology

Deep Learning in Cardiology

3D Cardiac MRI automatic segmentation

Three-dimensional reconstruction

Cardiovascular Ultrasound Software

3D reconstruction of the heart

Coronary CT Angiography with Deep Learning

Cardiac Left Ventricle Segmentation

Automatic Catheter Orientation Measurement

Quantitative Coronary Analysis

Right Atrium Measurement with Ultrasound

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Announcement – Non-Invasive Planning of Coronary Intervention

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