Tag: Medical applications

UPCOMING WEBINAR: Machine Learning in Medical Imaging – Current Challenges

Wednesday June 29, 2022 Host: Moshe Safran – CEO of RSIP Vision USA Guest speaker: Professor Klaus Maier-Hein Klaus Maier-Hein is full professor at Heidelberg

Announcement – New AI Tool for Prostate MRI Analysis to Support PI-RADS Scoring

RSIP Vision Presents New AI Tool for Prostate MRI Analysis to Support PI-RADS Scoring Innovative technology performs automatic segmentation and lesion detection in prostate MRI

Announcement – Total Shoulder Arthroplasty (TSA) Planning Through MRI Scan

RSIP Vision Presents New Tool for Total Shoulder Arthroplasty (TSA) Planning Through MRI Scan Advanced artificial intelligence creates accurate and radiation-free method for TSA planning.

AI and the Future of Radiology

Wednesday January 26, 2022 Host: Moshe Safran – CEO of RSIP Vision USA Guest speaker: Daniel Rueckert, PhD Daniel Rueckert is Professor of Visual Information

Announcement – Intra-op Virtual Measurements in Laparoscopic and Robotic-Assisted Surgeries

RSIP Vision Presents New Technology for Intra-op Virtual Measurements in Laparoscopic and Robotic-Assisted Surgeries Innovative Technology Provides Calibration of Robotic-Assisted Surgeries’ (RAS) Images and a

Success Rating and Dynamic Feedback in RAS

Success Rating in Robotic Assisted Surgeries

Success Rating and Dynamic Feedback Minimally invasive surgeries (MIS), specifically robotic assisted surgeries (RAS), generally have an improved outcome compared with standard surgeries. However, they

Emerging Technologies in AI for Surgical Imaging

Wednesday December 8 at 11am PT Speaker: Moshe Safran (RSIP Vision) Host: Shmulik Shpiro (RSIP Vision) From preoperative planning to intraoperative guidance and post-op analytics,

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 – Bladder Panorama Generator and Sparse Reconstruction Tool

RSIP Vision Introduces Bladder Panorama Generator and Sparse Reconstruction Tool New modules perform stitching of bladder images during cystoscopies, creating a panoramic view and provides

Announcement – Registration Module for Orthopedic Surgery

RSIP Vision Announces Patient-Specific, Intraoperative Registration Module for Orthopedic Surgery The new neural network technology enables accurate, quantitative measurements of bones and implants during the

Announcement – Automated Assessment of Cartilage Damage

RSIP Vision Announces New Tool for Sports Medicine Applications, Enabling Automated Assessment of Cartilage Damage This new algorithmic software provides automated measurement of articular cartilage

Announcement – Real-Time Surgical Workflow Recognition

RSIP Vision Reveals Its Newest Feat of Medical Imaging Innovation: Real-Time Surgical Workflow Recognition and Analysis Technology for Robotic Assisted Surgeries The company’s newest module

Bay Vision Meetup with Lena Maier-Hein June 17

Does machine learning-based biomedical image analysis require domain experts?

Thursday June 17 at 10am PT Hosts: Moshe Safran (RSIP Vision) and Rabeeh Fares (RSIP Vision) Invited speaker: Prof. Dr. Lena Maier-Hein, Head of Department,

Announcement – MRI to Ultrasound Fusion for Prostate

RSIP Vision Launches an Advanced AI-Based Tool for Prostate MRI and Ultrasound Registration Enabling Precise Navigation in Key Procedures New module creates a warped MRI

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

One-click segmentation of medical images

In the medical field, image analysis plays a crucial role in both diagnosis and treatment. Its central tool is segmentation, which involves partitioning an image

Image Analysis and AI in Diagnostic ERCP

Recent developments in the field of medical image analysis and artificial intelligence (AI) are used to improve the procedural outcomes of ERCP (Endoscopic retrograde cholangiopancreatography). Here is how RSIP Vision develops AI for diagnostic ERCP. Read what we do for enabling 3D Image Reconstruction and Image Registration/Fusion. Learn how strictures detection and classification can provide the physicians with classification scoring and, sometimes, help them avoid unnecessary biopsies during ERCP.

AI in ERCP gallstones and strictures treatment

When performing ERCP for stone removal and stricture treatment, the gastroenterologist must overcome several challenges, namely navigation in the region of interest and the choice of the most fitting treatment that must be selected and implemented. AI enables to reconstructing a 3D image from multi-angle X-ray images or ultrasound slices. It is also trained to accurately classify the type of blockage that necessitated the ERCP procedure in the first place, resulting in quicker and more efficient ERCP procedures.

AI and Robotic Surgery for Renal Cancer

Image analysis techniques and artificial intelligence are leading to radical innovations in renal cancer diagnosis and treatment. In particular, renal cancer robotic surgery. Advanced AI algorithms and computer vision assist in detecting and classifying all kinds of renal diseases, using segmentation and contour detection. This results in improved diagnostic accuracy and enhanced personalized treatment for patients. Moreover, robotic assistance in renal surgeries has gained increased traction in both complete and partial nephrectomies. Surgical planning and 3D reconstruction based on CT and MRI images play vital roles in successful robotic-assisted kidney-related procedures

Announcement – RSIP Vision Announces Versatile Medical Image Segmentation Tool

RSIP Vision Announces Versatile Medical Image Segmentation Tool, Delivering Efficient Anatomical Measurements and Better Treatment Options AI-based, domain-agnostic algorithmic module minimizes human errors in clinical

AI and Deep Learning for Prostate Cancer

Recent developments in the field of deep learning and artificial intelligence (AI) are moving the needle in prostate cancer healthcare. More specifically, it is now possible to use state-of-the-art AI and Deep Learning for prostate cancer detection and treatment. Also prostatectomy, a common treatment of prostate cancer, can benefit from the use of these advanced algorithms to increase procedural success. RSIP Vision’s algorithms provide a solution that can be integrated into all steps of prostate cancer care, thus improving patient outcome.

Image Analysis and AI for BPH

Recent developments in the field of deep learning and artificial intelligence can aid in BPH detection, classification and treatment. Analyzing ultrasound and MRI images, and using deep-learning segmentation tools to process them, gives a baseline for severity classification by the physician. Follow-up scans can be accurately compared to baseline scans for optimal treatment decision. Real-time tracking, 3D image reconstruction, and fusion can all provide better guidance during stent placement and urinary tract dilation. Prostatectomy procedure can be kept within boundaries at all times.

Improving Urolithiasis Healthcare Using AI and Image Analysis

Deep learning and artificial intelligence solutions have recently been developed to improve urolithiasis detection and treatment, leading to enhancing the clinical outcome. Utilizing convolutional neural networks provides accurate stone recognition and segmentation. Automatic Neural-Networks or Support Vector Machine (SVM) classifiers on kidney stone CT data classify the stones into their subtypes with notable accuracy, assisting and speeding treatment selection. Throughout the full cycle of detection and treatment of urolithiasis, RSIP Vision’s custom AI image analysis algorithms significantly improve urolithiasis procedures and outcome.

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

Announcement – RSIP Vision introduces an innovative set of AI modules for enhanced medical ultrasound applications

RSIP Vision introduces an innovative set of AI modules for enhanced medical ultrasound applications. These innovative modules empower a wide range of medical applications by

Announcement – RSIP Vision Launches a Pioneering AI Suite Providing Optimal Solutions to Key Tasks in Lung Surgery

RSIP Vision Launches a Pioneering AI Suite Providing Optimal Solutions to Key Tasks in Lung Surgery. New technology offers critical information enabling pulmonary surgeons to

Announcement – RSIP Vision Announces Breakthrough AI technology for 3D Reconstruction of Knees from X-ray Images

RSIP Vision Announces Breakthrough AI technology for 3D Reconstruction of Knees from X-ray Images. New technology creates a rich 3D modelling of each knee bone

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.

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.

Announcement – RSIP Vision Launches a Breakthrough AI-Based Shoulder Replacement Solution

RSIP Vision Launches a Breakthrough AI-Based Shoulder Replacement Solution, Dramatically Improving Clinical Outcome and Shortening Recovery Time. New Technology integrated in medical vendors’ platforms, allows

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.

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

Announcement – RSIP Vision Launches AI-Based, 3D Total Hip Replacement Solution

SILICON VALLEY, Calif., Dec 17, 2019 — RSIP Vision, an Israeli global provider of artificial intelligence (AI), computer vision, and image processing technology, announced today a

Meetup Boston BIV – Computer Vision and Microscopy

Find out how Artificial Intelligence tackles the huge diversity in the most complex challenges that exist in the medical field: the Boston Imaging and Vision

Meetup AI in Medical Imaging – February 5

SCROLL DOWN TO FILL THE FORM AND REGISTER . Find out how Artificial Intelligence tackles the huge diversity in the most complex challenges that exist in

Liver Tumor Segmentation with Deep Learning

Liver tumors, also known as hepatic tumors, are quite common and some poses a grim prognosis. Therefore, early detection and diagnosis has become a main

Brain Ventricles Segmentation with Deep Learning

Early diagnosis and treatment of ventricular system pathologies is crucial. Brain CT has become a leading diagnostic tool due to its high availability and quick image generation, which is useful in emergency room settings such as stroke or traumatic brain injury (TBI). Backed by cutting edge deep neural network and advanced Artificial Intelligence techniques, CT imaging can perform a very accurate brain ventricles segmentation and supply the physicians with crucial information regarding presence of hemorrhage, ischemia, tumors, hydrocephalus, and other pathologies.

Lung Nodules Segmentation

Pulmonary nodules (AKA lung nodules) are small masses (up to 30mm) of tissue surrounded by pulmonary parenchyma. They are quite common finding on computerized tomography

Lung Fissures Segmentation

Lung fissures are double folds of visceral pleura that section the lungs to lobes. Both lungs have an oblique fissure separating the upper and lower

Retina Montage Software

RSIP Vision has developed software which finds common points within the images and reorients them to ‘stitch’ together an accurate panoramic presentation of the retina. Our image stitching technology reconstructs the vascular tree of the retina also when images are acquired at different angles. Its end result enables the ophthalmologist to analyze the patient’s retinal vascular tree through an efficient and non-invasive process.

Automatic Pupil Distance Measurement

Modern eye glasses need high precision measurements to ensure the best fit. Interpupillary distance, the distance between the projections of the pupil on the cornea, is one of the most important measurements. Sophisticated algorithms, developed by RSIP Vision to determine with high precision the center of vision corresponding to the pupil, have been integrated into machinery used in eye clinics and embedded as an application in portable devices, such as cell phones.

Multi-modal registration of retina images

Is it possible to perform combination of fundus images coming from different imaging equipment or technologies and taken from different angles? RSIP Vision apparently can, since we used our expertise in computer vision for ophthalmology to provide software to a client who wanted to combine direct images of the fundus with fluorescein images, which need to be reconstructed before being connected together to form a more detailed image. The end result provides ophthalmologists with detailed images of the retina, ensuring more efficient and accurate patient diagnosis.

Detection and quantification of bone cancer

Metastatic bone malignancies arise following prostate cancer (80% of cases, with 3% five-year survival rate), breast cancer (with no cure) or lung cancers (with 11% two-year survival rate). Bone metastases affect more than 400,000 people annually in the United States with frequent occurrences among patients undergoing irradiation and secondary effect to other treatments. Detection of skeletal metastases has a major impact on devising treatment strategies and prognosis. The solution offered by RSIP Vision produces 3D surfaces of bones and other skeletal-related structures to detect and quantify primary and metastatic bone cancer.

Point and surface registration in orthopedics

Point and surface registration enable computer vision and image processing to improve surgical orthopedy practices and affect surgery outcome recovery. Bringing point and surface registration in the field of orthopedics, computer vision and image processing hold the potential to improve surgical practices and affect surgery outcome to favor the benefit of patients and fast recovery. Measurement accuracy (within less than 1 mm) is a strict constraint to computer-vision-based algorithms.

Chest CT Registration

Lung cancer is the leading cancer killer of men and women in the U.S. and it causes more deaths than colorectal, breast and prostate cancers

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

Finding Cysts, Part Five: Final Detection

The goal is to automatically detect the appearance of Cystoid Macular Edema (CME) in Optical Coherence Tomography (OCT) images. The deep learning technique used, Convolutional Neural Networks, takes as an input patches of pixels from within the retina. These patches were generated from previous segmentation of retinal images. A further segmentation of the retina is performed using an image processing algorithm called SLIC. Every superpixel thus generated, after being labeled as in the OCT scan, is fed into the neural network to detect the cyst.

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