RSIP Vision Introduces AI-Based Segmentation and Measurement Platform
RSIP Vision recently announced a general-purpose, AI-based segmentation and measurement platform for quickly and automatically detecting objects of interest and their limits.
FREMONT, CA: The top innovator in medical imaging through advanced AI and computer vision applications, RSIP Vision, recently announced a general-purpose, AI-based segmentation and measurement platform for quickly and automatically detecting objects of interest and their limits, making surgical and diagnostic measurements more straightforward and more precise for better treatment choices. The tool needs limited user work to provide accurate 3D visualization and analysis of patient anatomy and is relevant across verticals & modalities for medical imaging. The solution works automatically and is stable and clinically precise, eliminating human factors like fatigue and misreading, leading to measurement errors. It is open to manufacturers of medical devices for use in leading facilities around the world.
"Distinguishing and measuring organs, lesions, and other areas of interest in biopsy and pre-surgical planning can be tedious work, which is generally assigned to a specific employee or technician, or even a physician, said Ron Soferman, Founder & CEO at RSIP Vision. "Our new segmentation tool makes it easier to pinpoint specific points and boundaries in images, which in turn leads to greater accuracy during surgeries without being dependent on the capability and experience of a specific individual. In 2021, RSIP Vision will continue to drive innovation in image analysis across the medical verticals through custom software, advanced algorithm development, and custom technologies, which will be found in medical devices in leading facilities worldwide. RSIP Vision ensures customers can leverage the latest advances in AI and computer vision, in order to save time and cost during medical procedures."
In a scan through a series of algorithms, RSIP Vision provides this AI-based program to identify and localize the area of interest and use certain complex features. For better viewing, the segmentation establishes boundaries around the image and conducts automated measurements. Using artificial intelligence technology accessible across every modality, including X-ray, CT scans, MR, surgical robotics, and pathology, physicians and researchers may obtain consistent, repeatable measurements of a particular area's dimensions and characteristics. For example, the tool can be utilized across patient populations and cohorts for one-click segmentation of lesions in multiple organs, such as the lungs and liver. Without the necessity of collecting and training machine learning models on comprehensive domain-specific training data, the latest AI module can be easily incorporated into medical device apps for multiple applications. It will improve the time to market for medical device companies seeking to remain ahead of the AI adoption curve.
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