PCNL – Planning and real-time navigation
Urolithiasis, or kidney stones, is a common pathology affecting nearly 10% of the population in the USA. Percutaneous Nephrolithotomy (PCNL) is a minimally invasive urology
Tag: Segmentation
PR – 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
PR – 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
PR – 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
PR – 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
PR – 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
PR – 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
Image Analysis and Artificial Intelligence in Urology
Artificial intelligence (AI) and deep learning play an increasingly crucial role in medical imaging in general, and in the field of urology particularly. The applications of AI in urology are numerous, starting with accurate diagnosis (using image segmentation and abnormality detection), continuing with biopsy and operative procedures (using tools for assisted navigation and robotic guidance), and ending in treatment assessment (using tools similar to those used in diagnosis in order to assess the response to treatment).
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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
PR – 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
PRESS RELEASE – RSIP Vision Launches a New Knee Segmentation and Landmark Detection from X-ray Module
Breakthrough AI technology leads to precise surgery and optimal implant positioning, resulting in improved quality of life for the patients. SILICON VALLEY, CA, September 15,
PRESS RELEASE – 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
PRESS RELEASE – 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
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
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.
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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.
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PRESS RELEASE – Lung Segmentation with AI
RSIP Vision’s Advanced AI Technology Provides Segmentation with Unmatched Precision for Interventional Lung Procedures. New Solution Enables Surgeons to Biopsy Exact Location of Suspicious Lesions
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.
Middle and Inner Ear Segmentation with Deep Learning
Ear pathologies are common in all age groups, and are one of the leading causes for visiting a doctor. In most cases, proper diagnosis can
Larynx Segmentation with Deep Learning
The larynx, also known as the voice box, is a triangular structure in charge of important functions including breathing, voice production and supplying protection to
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
PRESS RELEASE – Knee Replacement by RSIP Vision
Knee Replacement Patients Enjoy Life-Changing Surgical Outcomes with RSIP Vision’s Revolutionary AI Solution Jerusalem, May 29, 2019 – RSIP Vision, a global leader in artificial
Lymph Node Segmentation Module
Lymph nodes are routinely examined and assessed during physical examination of patients in a clinic or hospital setting. Enlarged lymph nodes can be indicators of
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.
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Sinus Segmentation with Deep Learning
The paranasal sinuses are air-filled spaces surrounding the nasal cavity. The sinuses include the maxillary, frontal, ethmoidal and sphenoidal sinuses. Due to being air filled, the sinuses make
Brain Hemorrhage Segmentation with Deep Learning
Prompt diagnosis, monitoring and treatment of intracranial hemorrhage are essential to avoid brain structure damage. This task is made possible by recent AI-based advancements. Image analysis algorithms based on deep learning can rapidly estimate the hemorrhage volume and measure the edematous area around it. Automated image processing algorithms produce a 3D model of the ventricular system, which can ultimately be useful in guidance of the neurosurgeon during brain procedures.
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Dental Segmentation with Deep Learning
Dental problems affect people of all ages and ethnic groups, and are common worldwide. With many patients suffering from tooth decay, orthodontic issues and even
Lung vasculature segmentation
Lungs vasculature has a major part in blood oxygenation. The complicated branches of arteries and veins, accompanied by the intricate bronchial tree are in charge
Lung Tumor Segmentation
Lung cancer is the most common cancer related mortality cause among men, and second in women worldwide. Primary lung cancer is usually divided into two
Tissue Analysis with AI
New AI technologies by RSIP Vision are very powerful in analysis of tissues and histopathology. This complex task, which has been haunting for years the medical community, has now a very practical solution: deep learning gives very fruitful results to several challenges, like the segmentation of cells and nucleus and the classification of the cells according to the detected pathologies.
Detection and Segmentation of Dendritic Cells
Dendritic cells are a type of antigen-presenting cells and have an integral part in the normal functioning immune system, in that they help to initiate
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
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
Deep Learning in Pulmonology
Deep learning has been successfully applied in various applications in pulmonary imaging, including CT registration, airway mapping, real time catheter navigation, and pulmonary nodule detection.
Deep Learning in Ophthalmology
Recent works suggest novel deep learning tools for detection, segmentation and characterization of eye disorders. Accurate segmentation of retinal fundus lesions and anomalies in imaging
Deep Learning in Brain Imaging
Recent years’ AI-based advancements in brain imaging have been outstanding. Many of them are precious for the physician to avoid or reduce structural damage and save lives. This article resumes some of those breakthrough innovations in brain imaging brought by Artificial intelligence, computer vision, deep learning and image analysis in performing crucial tasks of automated segmentation, registration, classification, image enhancement and more.
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Classification and Segmentation of Dendritic Cells
Dry eye disease (DED) is one of the most common ophthalmic disorders. Inflammation of the ocular surface is controlled by corneal antigen-presenting cells called dendritic
Fingerprint Segmentation Using Deep Learning
Automatic fingerprint recognition systems are based on the extraction of features from scanned fingerprint image. A successful preprocessing of the scan is an important first
Intracranial Hemorrhage and Edema Segmentation
An intracranial hemorrhage (ICH) is a condition in which a blood vessel erupts inside the brain, causing internal bleeding. If not treated correctly and immediately,
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.
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Computer-Assisted Joint Replacement (hip and knee)
Pathologies in the joint regions are common especially among elderly patients. They are caused, in many cases, by wear and tear of the cartilage layer
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.
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ROP: Retinopathy of Prematurity
Retinopathy of prematurity (ROP) is a leading cause of blindness in infants. ROP (or Terry syndrome) is a disease of the eye affecting prematurely-born, low
CT Segmentation in orthopedic surgery
CT image segmentation is a typical phase of orthopedic surgeries in which a visualization system is called to visually support the surgeon’s task. This system
Meibomian gland dysfunction detection
Meibomian gland dysfunction is often seen as an early stage of dry eye syndrome. Indeed, Meibomian glands play a significant role in tears production by
Lung Segmentation Software
RSIP Vision has built a lower respiratory tract segmentation software using advanced image processing algorithms. This region includes both lungs along with their pulmonary vasculature. This lung segmentation software takes advantage of the structure of vascular and capillary tree in the area as an exploratory tool for lungs segmentation, enabling both diagnosis and planning of invasive interventions.
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Retinal inner layers segmentation
OCT is the only method that can perform noninvasive imaging with non-ionizing radiation and offering relatively good resolution. That is why it has become a
Geographic Atrophy Segmentation Using SD-OCT
In a previous article, we talked about Geographic Atrophy segmentation in 2D images. This article focuses on how OCT images shed light on the development
Geographic Atrophy (GA)
Geographic Atrophy (generally called GA) is a case of advanced Dry AMD (Age-related Macular Degeneration) which might lead to vision loss. As a consequence of
Segmentation and tracking of kidney stones
An affected anatomical region can be treated in a selective and non-invasive manner by localized and contact-free methods such as high-intensity focused ultrasound. Treatment by
Lesion segmentation by random-forest classifiers
Segmentation of lesions in images, such as those obtained from MRI, ultrasound, CT etc, can be viewed as classifying pixels (or voxels, in the 3-D
Gastrointestinal lesion detection with machine learning
Endoscopic examination is performed to disclose the lesion’s biophysical properties and assess its severity according to its physical appearance. Computer vision and machine learning put a large arsenal of techniques at our disposal. Clever utilization of these techniques enables to do just that, that is, translating expert knowledge into a fine-tuned algorithm, specifically designed for the task of GI lesion detection. The highest level of robustness, accuracy, and reproducibility is required, hence automatic methods can perform as a proper alert system for lesion detection.
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Bone segmentation in orthopedic surgery
Incorporation of new visualization technologies and planning methodologies shortens examination, planning and operation procedures in orthopedic surgery, while retaining the high standard of accuracy that are required in these common practices. Innovative algorithmic techniques, relying on image processing, computer vision and machine learning are increasingly utilized and have gained approval by regulatory bodies such as the FDA, leading to what is better known as Computer Assisted Orthopedic Surgery (CAOS) procedures. This requires accurate bone segmentation, better performed using the method which we recommend.
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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.
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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.
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3D inspection and crack detection
Industrial production is prone to surface defects and it often needs to be inspected prior to shipment, when still in a semi-finished status. Cracks being very frequent in many types of material, vision-based crack inspection and detection is cost effective and offers high reproducibility and reliability. Here is a contact-free procedure using laser scanning, which can be placed in-line for continuous inspection during production.
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Live cells tracking
Manual cell inspection is limited to tracking a relatively small number of cells in short periods of time and it is prone to human errors. On the other hand, computer vision algorithms can be used to perform fast scanning, segmentation and tracking of large cell populations over long periods of time. Taking advantage of our experience in segmentation, microscopy and machine learning, this procedure helps saving both labor and time, hence enabling more timely diagnostic and therapy.
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Automatic segmentation of tumor cells
Molecular analysis of in histology enables quantification of abnormality in a given tissue, assess patient condition, and devise treatment. Tissue samples taken in biopsy allow researchers to screen for therapeutic agents but might not accurately capture the bulk tumor, due to its irregular non-cylindrical shape. This calls for an automated segmentation of tumor cells: RSIP Vision does that in several phases, concluded by machine learning methods which study the cell texture and classify the image accordingly. The end result is a fast and life-saving biopsy scanning and analysis system.
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Bones and Skeleton segmentation
RSIP Vision suggests an automatic segmentation procedure based on iterative binarization of bone tissues density, as observed in Computed Tomography (CT), the most common 3D process used for bone imaging. This method is particularly fast, regardless of whether contrast was used in the CT scans. In fact, images taken with contrast generally display blood with an intensity which is similar to bone; our technique is able to overcome this challenge and to deliver a fast and satisfying bones segmentation and skeleton segmentation solution to our client.
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Lungs tumors and nodules segmentation with Deep Learning
It is visually more difficult to identify lung tumors than nodules, since the latter are supposed to have an elliptical shape, while the chromatic aspect of the former is quite hard to distinguish from healthy tissues on a CT image. We use Deep Learning neural networks to overcome this difficulty in a way that is quick to perform, reliable and memory efficient. Our software of computer vision in pulmonology detects and classifies tumors and nodules in the fastest time, to provide our clients a quick and reliable 3D segmentation of lung tumors with Deep Learning.
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Brain tumor segmentation
In addition to primary tumors, the human brain can also suffer from secondary tumors or brain metastases. The most common cancers that spread from remote areas to the brain are lung, breast, melanoma, kidney, nasal cavity and colon cancers. By the way of segmenting the tumor in the image, brain tumor image processing overcomes anatomical structure challenges. AI-based techniques enable to estimate the volume and spread of the tumor and provide objective and variation-free expected tumor boundaries.
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Prostate segmentation in MR images
Prostate cancer is the second most common cancer among American men, with more than 200,000 new cases diagnosed every year and about 1 man in 7 diagnosed during his lifetime. Volume is a key indicator of the health of the prostate, revealing key information about the stage of the cancer, the probable prognosis and viable treatment. The rich experience of RSIP Vision enables us to recommend an approach based on a semi-automatic prostate segmentation to give a precise estimate of the prostate volume.
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Airways segmentation with Deep Learning
Image processing is a fundamental technique in the quest to identify lung cancer, one of the main causes of death among both men and women, and many other lung pathologies. For the worst diseases, survival rate depends on the stage in which the disease is diagnosed and correct segmentation of airway vessels offers the most effective solution to determine the lesion’s size and location, significantly improving diagnosis and treatment. Our solution is built upon Deep Learning and neural networks.
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Kidney Segmentation
The most dramatically common kidney diseases are: kidney cancer, hitting 50,000 new patients every year only in the U.S.; and kidney failures, which leave the organ unable to remove wastes. Laparoscopic partial nephrectomy operations remove or reduce kidney tumors and some renal malfunctions. We at RSIP Vision help by providing a semi-automatic and very accurate kidney segmentation technique, built on deep learning and neural networks to create a kidney model which would be specific for each patient.
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Pulmonary lobes segmentation
Emphysema quantification and lung nodule detection are among the clinical applications which benefit the most from lobes segmentation in CT scans. Proper lung segmentation is key to determine the boundaries of lobes and prevent pleural damage during examination and treatment. When correctly located, diseases are treated faster and better, hence the call for RSIP Vision to find a faster alternative to time-consuming manual segmentation.
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Lung vessel segmentation
Blood vessel segmentation of the lungs can help to identify important pulmonary diseases, characterizing nodules in the lungs, detecting pulmonary emboli and evaluating the lungs vasculature. Our technique of automatic pulmonary vessel segmentation completes very effectively the vessel tree structure provided by the CT scans of the lung, in such a way that the resulting image is more precise and matchlessly faster than any manual segmentation could be.
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Vessel Segmentation Using Deep Learning
Various segmentation methods, whether based on Convolution Neural Networks or traditional image processing techniques, can be used to delineate the vascular tree in clinical imaging. Given the few features distinguishing veins from arteries (usually brighter and thinner than veins), the challenge consists of training a binary classifier assigning each pixel to the category of vein or artery. This article covers the advantages of using CNNs and deep neural networks for the classification and segmentation of vessels in fundus images.
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Indoor Scene Structure Analysis
Summary: Indoor Scene Structure Analysis for Single Image Depth Estimation This is the first of our series of summaries of interesting texts on computer
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.
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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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Finding Cysts Part Three: Layer Segmentation
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
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Finding Cysts, Part Two: The Denoising Process
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
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Automatic Detection of Macular Cysts
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
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