![AI in Diagnostic ERCP](https://dev.rsipvision.com/wp-content/uploads/2021/02/AI-in-Diagnostic-ERCP.jpg)
Image Analysis and AI in Diagnostic ERCP
![Type 4 cholangiocarcinoma](https://dev.rsipvision.com/wp-content/uploads/2022/01/Type-4-cholangiocarcinoma.jpg)
Enhanced ERCP tumor assessment using AI
![Robotic Surgery](https://dev.rsipvision.com/wp-content/uploads/2016/07/RSIP-Vision-Robotic-Surgery.jpg)
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
![Segmented prostate gland](https://dev.rsipvision.com/wp-content/uploads/2021/01/Prostate-Segmentation.jpg)
Image Analysis and AI for BPH
![Cardiac MRI Heart Chambers Segmentation](https://dev.rsipvision.com/wp-content/uploads/2020/02/Cardiac-MRI-Heart-Chambers-Segmentation.jpg)
AI in Cardiac MRI Segmentation
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
![lymph nodes](https://dev.rsipvision.com/wp-content/uploads/2021/03/lymph-nodes_s.gif)
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
![Pharma - Tissue Analysis](https://dev.rsipvision.com/wp-content/uploads/2019/01/Pharma-Tissue-Analysis.jpg)
Tissue Analysis with AI
New AI technologies by RSIP Vision are very powerful in analysis of tissues and histopathology. This complex task, which has been haunting for years the medical community, has now a very practical solution: deep learning gives very fruitful results to several challenges, like the segmentation of cells and nucleus and the classification of the cells according to the detected pathologies.
![Cardiac Motion Correction](https://dev.rsipvision.com/wp-content/uploads/2018/08/Cardiac-Motion-Correction-768x303.jpg)
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
![RegNet](https://dev.rsipvision.com/wp-content/uploads/2018/08/RegNet-768x271.jpg)
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.
![Zoom-in-Net](https://dev.rsipvision.com/wp-content/uploads/2018/08/Zoom-in-Net-1-768x466.png)
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
![Joint reconstruction and segmentation](https://dev.rsipvision.com/wp-content/uploads/2018/08/Joint_reconstruction_and_segmentation_2-1-768x307.png)
Deep Learning in Brain Imaging
![Macro Defects Detection](https://dev.rsipvision.com/wp-content/uploads/2018/08/Macro-Defects-Detection-2-768x669.jpg)
Wafer Macro Defects Detection and Classification
![Classification and Segmentation of Dendritic cells](https://dev.rsipvision.com/wp-content/uploads/2018/07/Classification-and-Segmentation-of-Dendritic-cells-768x392.jpg)
Classification and Segmentation of Dendritic Cells
Dry eye disease (DED) is one of the most common ophthalmic disorders. Inflammation of the ocular surface is controlled by corneal antigen-presenting cells called dendritic
![Robot reading a text on digital tablet](https://dev.rsipvision.com/wp-content/uploads/2018/01/Robot-reading-a-text-on-digital-tablet.jpg)
OCR for robots
![Robot examining camera in factory](https://dev.rsipvision.com/wp-content/uploads/2018/01/Robot-examining-camera-in-factory.jpg)
Object Detection Methods for Robots
![Robot camera on the board of chips](https://dev.rsipvision.com/wp-content/uploads/2018/01/Robot-camera-on-the-board-of-chips.jpg)
Machine Vision Robots for Semiconductors
Machine vision algorithms are also used to operate robots in the high-precision semiconductor industry. Robots perform these intelligent tasks supported by machine vision software: several methods are currently used to detect defects and classify them, with important economies in both time and money. Robots in the semiconductor industry too can take advantage of deep learning techniques: their main benefit is the dramatic improvement in the defect classification abilities of the robotic devices.
![Robots using Machine Vision](https://dev.rsipvision.com/wp-content/uploads/2018/01/Robots-for-agriculture.jpg)
Robots using Machine Vision in Agriculture
![Microscopy view of Monocytes](https://dev.rsipvision.com/wp-content/uploads/2013/12/Monocytes_a_type_of_white_blood_cell_Giemsa_stained-768x560.jpg)
Cell Classification software
Whenever the task of classification of single cells is required, RSIP Vision offers pioneering technologies in both segmentation and classification of cells and nuclei. This module includes also the initial task of locating the best area in the slide that might give the best candidate for the classification.
![Defects detection in ceramics](https://dev.rsipvision.com/wp-content/uploads/2017/02/Ceramics.jpg)
Defect Detection in Ceramics
![](https://dev.rsipvision.com/wp-content/uploads/2016/11/CNC-Laser-plasma-cutting-of-metal-1.jpg)
Machine fault detection and classification
Automatic detection and diagnosis of various types of machine failure is a very interesting precess in industrial applications. With the advancement of sensors and machine intelligence,
![](https://dev.rsipvision.com/wp-content/uploads/2016/11/Multiethnic-group-of-smiling-people-1.jpg)
Deformable pattern matching and classification
Three sources of apparent object deformation can occur: a change in the shape of the object itself, partial or full occlusion by dynamically changing background
![](https://dev.rsipvision.com/wp-content/uploads/2021/02/ICCV-Daily-2019-Tuesday-S.jpg)
Breakthroughs in biomedical imaging
During the last few decades, the field of biomedical imaging was shaken by major breakthroughs, which have completely changed the way physicians can observe imaging data. For
![](https://dev.rsipvision.com/wp-content/uploads/2021/02/Daily-CVPR-Wednesday-Cover-220.jpg)
Type 2 interval fuzzy sets in pattern classification
In search for a pattern in an image, a video or a signal, one has to consider several sources of bias, noise and uncertainties. Such
![](https://dev.rsipvision.com/wp-content/uploads/2021/02/Computer-Vision-News-March-2018-M.jpg)
Image Features for Classification
Classification problems in image and signal analysis require, on the algorithmic side, to take into account complex information embedded in the data. Images might contain
![Lesion Detection in CT scan (Hemorrhagic stroke)](https://dev.rsipvision.com/wp-content/uploads/2016/07/Lesion-Detection-Hemorrhagic-stroke.jpg)
Lesion segmentation by random-forest classifiers
Segmentation of lesions in images, such as those obtained from MRI, ultrasound, CT etc, can be viewed as classifying pixels (or voxels, in the 3-D
![Tree detection - green](https://dev.rsipvision.com/wp-content/uploads/2016/07/tree2.jpg)
Tree Detection and Related Applications in Forestry
Using aerial images taken by drone, plane or satellite, RSIP Vision can create forestry image processing and analysis software to efficiently determine: Trees detection Automatic
![Weeds detection (yellow polygons)](https://dev.rsipvision.com/wp-content/uploads/2016/07/area11.jpg)
Bounded Objects Detection and Related Applications in Forestry
Using aerial images taken by drone, plane or satellite, RSIP Vision develops software for image processing and analysis in forestry to efficiently determine: Forest border
![Ron Maron and Ron Soferman](https://dev.rsipvision.com/wp-content/uploads/2016/03/Ron-Maron-and-Ron-Soferman.jpg)
BIRD Foundation and a successful project
Our readers already know about the video classification software which we developed for a client wishing to classify untagged online videos in order to match relevant
![Detected crack](https://dev.rsipvision.com/wp-content/uploads/2016/02/Detected-crack.jpg)
3D inspection and crack detection
![Karyotype](https://dev.rsipvision.com/wp-content/uploads/2016/02/Karyotype-300x207-1.jpg)
Chromosome classification
![Lung CT scans](https://dev.rsipvision.com/wp-content/uploads/2016/01/Lung-CT-scans.jpg)
Lung Nodule Classification
![](https://dev.rsipvision.com/wp-content/uploads/2016/01/Date-sorting-768x512.jpg)
Date sorting
![Lymph Nodes of Lungs and Mediastinum](https://dev.rsipvision.com/wp-content/uploads/2016/01/Lymph-Nodes-of-Lungs-and-Mediastinum.jpg)