19Jan

AI and Robotic Surgery for Renal Cancer

By R. A. / 19/01/2021 / RSIP Vision Learns / No Comments

According to the CDC, over 41,000 men and 24,000 women are diagnosed with renal cancer in the US each year, and about one fourth of those diagnosed die from the disease. While a wide variety of renal cancer types exists, renal cell carcinoma (RCC) covers about 90% of renal cases. RCC includes sev...

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14Jan

AI and Deep Learning for Prostate Cancer

By R. A. / 14/01/2021 / RSIP Vision Learns / No Comments

Prostate cancer is the 2nd most prevalent cancer in men, requiring sophisticated tools for tumor detection and grading, as well as for treatment execution. Recent developments in the field of deep learning and artificial intelligence (AI) are moving the needle in prostate cancer healthcare. More ...

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Image Analysis and AI for BPH

By R. A. / 13/01/2021 / RSIP Vision Learns / No Comments

AI for BPH BPH (Benign Prostatic Hyperplasia) is a common condition in aging men and can lead to lower urinary tract symptoms (LUTS). Recent developments in the field of deep learning (DL) and artificial intelligence (AI) can aid in BPH detection, classification and treatment. Deep Learning and A...

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Improving Urolithiasis Healthcare Using AI and Image Analysis

By R. A. / 12/01/2021 / RSIP Vision Learns / No Comments

Urolithiasis Healthcare Using AI Urolithiasis, the formation of “stones” in the urinary pathway, is a prevalent pathology, found in 4-20% of the population. Recent developments in the field of image analysis, specifically deep learning (DL) and artificial intelligence (AI) are improving urolithia...

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23Feb

AI for Gastric Cancer Detection

By R. A. / 23/02/2020 / RSIP Vision Learns / No Comments

Upper gastrointestinal cancers, including esophageal cancer and gastric cancer, are among the most common cancers worldwide. However, a lack of endoscopists with colonoscopy skills has been identified and solutions are critically needed. The development of a real-time robust detection system for ...

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20Feb

Real time SLAM in Endoscopy Applications

By R. A. / 20/02/2020 / RSIP Vision Learns / No Comments

Robotic surgery requires extreme precision due to its small scale and the vulnerable organs such procedures usually target. To be able to get this precision many devices use magnetic or radio based external sensors in order to calculate the endoscope location in the patient’s body. However,...

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5Feb

Deep Learning for Cardiac Ultrasound (Echocardiography)

By R. A. / 05/02/2020 / RSIP Vision Learns / No Comments

Cardiovascular disease at a glance Cardiovascular disease is a leading cause of death, disability and health care expenditure in the United States. Cardiovascular disease (CVD) accounted for 840,678 – or 1 out of 3 – deaths in the US in 2016 Nearly 30 million American adults are curre...

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4Feb

AI in Cardiac MRI Segmentation

By R. A. / 04/02/2020 / RSIP Vision Learns / No Comments

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 death worldwide. However, postprocessing is time-consuming and prone to inter and intraobserver variability. Deep learning neural netw...

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4Feb

AI in cardiac CT angiography

By R. A. / 04/02/2020 / RSIP Vision Learns / No Comments

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 ar...

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30Jan

Endoscopic Procedures using Intravenous OCT

By R. A. / 30/01/2020 / RSIP Vision Learns / No Comments

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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30Jan

Using AI to Analyse Intravenous Ultrasound Images

By R. A. / 30/01/2020 / RSIP Vision Learns / No Comments

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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28Jan

Segmentation in Orthopedics with Deep Learning

By R. A. / 28/01/2020 / RSIP Vision Learns / No Comments

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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20Jan

Surgical Instrument Segmentation

By R. A. / 20/01/2020 / RSIP Vision Learns / No Comments

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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12Jan

AI-based Surgical Workflow Analysis, another big step towards the future of robotic surgery

By R. A. / 12/01/2020 / RSIP Vision Learns / No Comments

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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1Jan

Challenges and AI Solutions for Endoscopy

By R. A. / 01/01/2020 / RSIP Vision Learns / No Comments

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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25Sep

Sensors for ADAS Technology: 3. RADARs

By R. A. / 25/09/2019 / RSIP Vision Learns / No Comments

This article is the third part in a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles. RGB cameras, LiDARs and Radars are the three main sensors used by the automotive industry to maintain the perception for autonomous vehicles at va...

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25Sep

ADAS sensors: 2. LiDARs

By R. A. / 25/09/2019 / RSIP Vision Learns / No Comments

This article is the second part in a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles. RGB cameras, LiDARs and Radars are the three main sensors used by the automotive industry to maintain the perception for autonomous vehicles at v...

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25Sep

ADAS sensors: 1. RGB cameras

By R. A. / 25/09/2019 / RSIP Vision Learns / No Comments

This article is the first part in a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles. RGB cameras, LiDARs and Radars are the three main sensors used by the automotive industry to maintain the perception for autonomous vehicles at va...

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24Sep

Multiplex IF Analysis

By R. A. / 24/09/2019 / RSIP Vision Learns / No Comments

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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12Sep

Circulating Tumor Cells (CTCs)

By R. A. / 12/09/2019 / RSIP Vision Learns / No Comments

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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