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Tag: Reconstruction

XPlan Image

Announcement – XPlan.ai Confirms Premier Precision in Peer-Reviewed Clinical Study of its 2D-to-3D Knee Reconstruction Solution

The study, featured in the prestigious Journal of Clinical Medicine, found sub-millimeter accuracy on real-world patient imaging, enabling widespread access to precise, image-based computer-assisted surgery

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Knee 3D

Announcement – XPlan.ai by RSIP Vision Presents Successful Preliminary Results from Clinical Study of it’s XPlan 2D-to-3D Knee Bones Reconstruction

A tool for reconstruction of a 3D model of the knee from 2D X-ray images is being evaluated on clinical data at a leading medical

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Ureter Reconstruction 2d3d

Announcement – New Urological AI Tool for 3D Reconstruction of the Ureter

RSIP Vision Presents New Urological AI Tool for 3D Reconstruction of the Ureter Improving Urological Procedures Innovative technology utilizes 2D fluoroscopic images and reconstructs an

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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

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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

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Type 4 cholangiocarcinoma

Enhanced ERCP tumor assessment using AI

ERCP involves both endoscopy and fluoroscopy on a region with limited access. Accordingly, it poses several challenges for the gastroenterologist performing the procedure: imaging and artificial intelligence are key technologies to solve these challenges. They are used to reconstruct multiangle 2D X-ray images into a 3D image that will help the gastroenterologists in real-time navigation, reducing any complications involved in the navigation process. AI can be trained to accurately classify each tumor found by ERCP and check whether it is cancerous or not. 

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Robotic Surgery

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

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high resolution with CNN

High Resolution Image Reconstruction

Recovering a high-resolution (HR) image from a low resolution one is a classical problem in computer vision for which many algorithms have been developed to

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Facial recognition with 3D

3D surface reconstruction from single depth view

The advances in the manufacturing of depth sensors and camera technologies, such as LIDAR and RealSense cameras, have brought three-dimensional (3D) applications to the front

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3D-Reconstruction by RSIP Vision

Three-dimensional reconstruction

Three-dimensional reconstruction of scene can be viewed as is the reproduction of a depth-map. Thus, as an input we have a sequence of images as

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CT scan of healthy lungs

Three-dimensional reconstruction of a deformable object

Reconstruction of the three-dimensional surface of an object based on single view 2-D sequence of images is a highly challenging task. Challenges stem in part

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3D Reconstruction of the heart

3D reconstruction of the heart

Using minimal 2D images from a routine angiography, RSIP Vision’s algorithm tracks the veins and constructs a route map of the arteries, from two different angles. Our sophisticated algorithms applied to computer vision in cardiology reconstruct 3D models of the heart artery tree from 2D images, allowing for improved diagnostic capabilities, lower risk to both patients and physicians, more effective therapy and reduced costs.

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Dental 3D scanner

3D object reconstruction by fringe projection

Fringe pattern projection for 3D object reconstruction has been around for 3 decades. The method finds its application in reconstructing static and dynamic objects in

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Knee orthopedics - sparse set of points

Anatomical surface reconstruction with sparse set of points

Advance navigation techniques in orthopedic surgery allow to construct 3D models of patients’ anatomical parts by matching a surface to an acquired set of data points. As an alternative to costly imaging procedures, ultrasound or fluoroscopic scan can be performed with much reduced costs and hazards. A set of surface points marked by the technician or physician forms then an initial sparse set for anatomical surface reconstruction. Here we show how RSIP Vision’s engineers overcome the challenge of using a sparse set of points to accurately reconstruct a complex 3d surface.

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3D Reconstruction for Bone Alignment

Advances in vision-based medical imaging have completely transformed orthopedic surgery planning and operation procedures. A practical example is a high-impact femur fracture, resulting in a noticeable separation into two segments of the femur. Surgery in such cases requires initial alignment of the proximal and distal parts of the broken bone, followed by insertion and securing of a nail. In RSIP Vision’s solution, image features are matched to a pre-designed flexible geometrical model of the bone.

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Hip replacement surgery measurements

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. 

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Recent News

Announcement – XPlan.ai Confirms Premier Precision in Peer-Reviewed Clinical Study of its 2D-to-3D Knee Reconstruction Solution

IBD Scoring – Clario, GI Reviewers and RSIP Vision Team Up

RSIP Neph Announces a Revolutionary Intra-op Solution for Partial Nephrectomy Surgeries

Announcement – XPlan.ai by RSIP Vision Presents Successful Preliminary Results from Clinical Study of it’s XPlan 2D-to-3D Knee Bones Reconstruction

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