Blog Archives - RSIP Vision

Automated RECIST Measurement

The golden standard for measuring tumors is the RECIST score. RSIP Vision developed an automated module to accurately measure the RECIST score from CT scans as well as the exact 3D volume of the tumors. Changes in volume are a reliable measure of the progression or remission of the tumor, enabling to evaluate the responsiveness of the treatment in a relatively short time.

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

Agricultural yield prediction using Deep Learning

Technology breakthrough and availability of new datasets are changing forever the world of agriculture. Software solutions using Deep Learning makes it possible for farm managers to produce accurate yield estimates on a simple smartphone or tablet. This precision agriculture solution is made available by pioneering software from RSIP Vision.

Catheter Navigation System with RL

The typical catheter navigation system relies on fluoroscopy, which exposes patients to dangerous irradiation. To limit the dose throughout the operation, endoscopic cameras are used

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

GAN for non-rigid object tracking

Object identification and tracking remains a challenging task in computer vision, despite advances in hardware, computational, and algorithmic developments. Difficulties arise, in part, due to

Visible lung cancer on CT scan of chest and abdomen

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,

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

The algorithm searches for information in each frame

Temporal point process sampling in video

Object identification and tracking in a sequence of frames (video) consists of sampling of the scene, by e.g raster or uniform scatter, to extract features

RGB-D SLAM building 3D models from depth cameras

In the past few years, depth cameras became common and easy to get. Several product are available in the market at a reasonable price, e.g.

Automated Defect Inspection Using Deep Learning

Convention computer vision technique for automated optical inspection of defects have given satisfactory results, until recent years when deep learning and neural network architectures dramatically improved the detection. Deep learning engineers at RSIP Vision use U-Nets and central image monomers (also called Hu moments) to give our clients the quality of control that they request.

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.

Deep Learning in Medical Imaging

Until only a few years ago, traditional computer vision techniques have provided excellent results to detection and segmentation task. More recently, with the advent of deep learning and neural networks also in medical imaging, we obtain surprisingly better results in all task, be it detection, segmentation, classification and the like. In this article we review the state-of-the-art in the newest model in medical image analysis.

Wafer Macro Defects Detection and Classification

Typical wafer (VLSI) defects are numerous and their detection is a key task in every semiconductor production line. High-resolution scanners are expensive and the process of checking for any local defect is long. Cheaper Macro defects scanning allows to check every wafer rather than recur to sampling-base defect detection. Moreover, our automated wafer defect detection and classification uses state-of-the-art deep learning techniques, able to provide faster and more accurate classifications free of human errors.

Echo Cancellation Using Deep Learning

Complete cancellation of returned acoustic echo signal is still an unresolved issue in signal processing. When a signal from a speaker in one end of

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