Endoscopic Procedures using Intravenous OCT
While ultrasound imaging cannot be replaced, adding Intravenous OCT to endoscopy procedures significantly improves image resolution and increases the ability to detect plaque and segment it. Intravenous Optical Coherence Tomography (IVOCT) is a catheter-based medical procedure which uses a near i...
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Using AI to Analyse Intravenous Ultrasound Images
While the low-resolution Intravenous Ultrasound (IVUS) present its challenges, by adding AI to the we can easily overcome this issue, and improve the analysis of blood vessels interior parts for a better clinical outcome. Background Most endoscopic procedures rely on conventional 2D image sensors...
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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. RSIP Vision’s CTO Ilya Kovler and Senior Algorithm Developer Arie Rond explain how to improve the segm...
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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...
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AI-based Surgical Workflow Analysis, another big step towards the future of robotic surgery
AI and computer vision technologies can significantly improve the quality of surgical procedures by providing tools that help analyze and monitor certain parts of the surgical workflow. Surgical Workflow Analysis and Surgical Instrument Detection are two good examples of applications that lay the...
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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 common datasets for consistent evaluation and benchmarking of algorithms against each other. The Endoscope Vision...
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Multiplex IF Analysis
Multiplex IF analysis allows for real-time relational data between cells in tissue. This is an advantage over the gold standard brightfield IHC, which looks at a single marker at a time. When using brightfield IHC for multiple markers, sequential sections of the tissue are needed which does not a...
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Circulating Tumor Cells (CTCs)
Circulating tumor cells (CTCs) are rare cancer cells that originate from a tumor and then travel through the patient’s blood or lymphatic system. CTCs have a high risk of metastasizing elsewhere in the body, which can be very dangerous. It is well-known that finding and analyzing these rare cells...
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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 ...
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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...
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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 be achieved via clinical examination. However, in recurrent or complicated cases imaging of the ear is indicated and can help with treatment choice. Th...
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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 the trachea from aspirations. It consists mostly of cartilages- epiglottic, thyroid, cricoid, arytenoid, corniculate and cuneiform....
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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 infection, cancer and other pathologies. Therefore, a biopsy of a suspected lymph node, which provides tissue histology or cytology is...
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Brain Ventricles Segmentation with Deep Learning
The brain ventricular system is in charge of cerebrospinal fluid (CSF) production, which is essential for its normal function. In addition to providing physical protection to the brain tissue, it provides the nervous system with nutrients and removes waste among other functions. The ventricular s...
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Brain Hemorrhage Segmentation with Deep Learning
Intracranial hemorrhage is defined as bleeding within the cranium. Bleeding can occur within the brain parenchyma itself, giving way to what is called intra-axial bleed or intracerebral hemorrhage. On the other hand, bleeding outside the brain tissue is referred to as extra-axial bleed. Subtypes ...
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Detection and Tracking of Tumors
RSIP Vision’s automated detection and tracking of tumors answers one of the most frequent needs in the pharma industry – assessment of patient response to a new drug. This very strong tool saves precious time in the clinical stage when the automated detection and tracking can supply the most accu...
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Automated RECIST Measurement
RSIP Vision has develop an efficient tool for clinical trials in the pharma industry – the automated RECIST measurement. The gold standard in measuring the evolution of solid tumors, along the treatment with a tested drug, is the RECIST score (Response Evaluation Criteria in Solid Tumors), ...
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Intrusion Detection with Deep Learning
Detecting physical and virtual intrusions is a key process in ensuring information and property security. Physical intrusion detection refers to all attempts at break-ins to a building, warehouse, or other perimeters by an unauthorized person, where access is granted to only limited personnel. Ba...
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Chest CT Scan Analysis with Deep Learning
Chest radiography, with modalities such as X-Ray and CT, is now the common practice for the detection and analysis of the progression of lung tumors, tuberculosis and other pulmonary abnormalities. To date, most analysis are done by expert radiographers, who analyze resulting scans and estimate p...
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Object Tracking at High fps
Object tracking in video sequences is a classical challenge in computer vision, which finds applications in nearly all domains of the industry: from assembly line automation, security, traffic control, automatic driving assistance systems and agriculture. Presently state of the art algorithms per...
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