Cardiology

Driving innovation across cardiac imaging modalities

RSIP Vision brings broad and deep experience to the table for developing novel image analysis solutions for cardiology. We've executed successful cardiac image analysis projects in all the relevant imaging modalities-CT, MRI, fluoro, ultrasound, point cloud-and powered product development efforts in a wide variety of computer vision use cases in the field.

AI and Computer Vision Technologies

Much more than AI, we integrate expert medical knowledge from Day One, and leverage all image analysis techniques – from neural networks to 3D computer vision to parametric modeling, integrating multiple data sources and expert clinical knowledge to power the solution.

Case study

3D Reconstruction for Heart Chambers

The challenge

An industry leaders in the electrophysiology space want to develop innovative solutions for improved mapping of electrical activity and anatomy of heart chambers
The goal was to create faster and more informative maps from noisy and sparse data
Developing these unique capabilities would enable the organization to gain a competitive edge

Our approach

The RSIP Vision R&D teams developed production – quality mathematical models, algorithms, and software implementations, providing unique and effective capabilities for the mapping tasks
We developed the initial client IP seeds into fully functional implementation
We created a new IP on behalf of the client by inventing novel models from the ground up

The outcome

Electrical activity mapping:

Is now integrated & in clinical use in industry-leading medical device
Provides unique insights for evaluation of complex pathologies
Has generated excellent physician feedback and is a significant market differentiator for our client

3D anatomical reconstruction:

Proven in >100 clinical trial cases
Producing anatomically accurate maps from sparse and noisy input
Reduces mapping time by up to 15 minutes

Learn more

Learn more solutions and technologies from this field

PR – 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 for diagnosis, measurements, stent modelling, and characterization for procedural planning. TEL AVIV, Israel & SAN JOSE, Calif., December 8, 2021 – RSIP Vision, an experienced leader in driving innovation for medical imaging through advanced AI and computer vision solutions, today announces a new coronary artery modelling technology. This technology enables quick and accurate reconstruction of the coronary vasculature during angiography into a 3D model. This

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 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 rapid 3D visualization of coronary arteries and heart, including visualization of blood flow in arteries and capillaries, CCTA enables accurate monitoring of the coronary tree for blockages and other pathologies, yet, some major challenges remain to be met to maximize its clinical value. Complexity of CCTA Analysis: diagnostic accuracy of CCTA. The diagnostic value of CCTA relies on manual assessment of a 3D model of

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 from CAD, usually have accumulation of deposits such as lipids and inflammatory cells within the coronary arteries, potentially causing abnormal blood flow to the heart muscle due to vessel stenosis, causing ischemia and permanent damage to the heart. Sometimes, a sudden plaque rupture occurs within the coronary artery resulting in acute coronary syndromes (ACS). In these life-threatening cases, also referred to as myocardial infarction (MI)

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. Since the great vessels are an integral part of systemic and pulmonary circulation, vascular pathologies involving said vessels might be deadly. Computed tomography (CT) imaging is a useful tool for assessing the great vessels, and has become an essential tool for diagnosis of vascular pathologies, including aortic arch aneurysm, valvular heart disease and congenital vascular anomalies, such as aortic stenosis, coarctation of aorta or transposition

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 of network blocks in which each layer is densely connected with auxiliary side paths (skip connections) to all the following layers in the block. The main advantage in the structure is the reduced number of parameters, which makes training and prediction more efficient than standard CNNs. Parameter reduction is especially important in volumetric data, where the number of voxels scale cubically with the resolution and

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Cardiology

Driving innovation across cardiac imaging modalities

AI and Computer Vision Technologies

2D to 3D Reconstruction

Multimodal Registration

IVUS

MRI Segmentation

IVOCT

Case study

3D Reconstruction for Heart Chambers

The challenge

Our approach

The outcome

Electrical activity mapping:

3D anatomical reconstruction:

Learn more

PR – Non-Invasive Planning of Coronary Intervention

Deep Learning for Cardiac Ultrasound (Echocardiography)

AI in Cardiac CT angiography

Coronary Arteries Segmentation

Great Vessels Segmentation with Deep Learning

Deep Learning in Cardiology

PR – Non-Invasive Planning of Coronary Intervention

Deep Learning for Cardiac Ultrasound (Echocardiography)

AI in Cardiac CT angiography

Coronary Arteries Segmentation

Great Vessels Segmentation with Deep Learning

Deep Learning in Cardiology

Get in touch

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Cardiology

Driving innovation across cardiac imaging modalities

AI and Computer Vision Technologies

2D to 3D Reconstruction​

Multimodal Registration

IVUS

MRI Segmentation

IVOCT

Case study

3D Reconstruction for Heart Chambers

The challenge

Our approach

The outcome

Electrical activity mapping:

3D anatomical reconstruction:

Learn more

Get in touch

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Upcoming Events

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2D to 3D Reconstruction