Pulmonology Archives - RSIP Vision

Announcement – RSIP Vision introduces an innovative set of AI modules for enhanced medical ultrasound applications

RSIP Vision introduces an innovative set of AI modules for enhanced medical ultrasound applications. These innovative modules empower a wide range of medical applications by

Announcement – RSIP Vision Launches a Pioneering AI Suite Providing Optimal Solutions to Key Tasks in Lung Surgery

RSIP Vision Launches a Pioneering AI Suite Providing Optimal Solutions to Key Tasks in Lung Surgery. New technology offers critical information enabling pulmonary surgeons to

Detecting Pulmonary Embolism from CT Scan

Pulmonary embolism is a very dangerous condition, which happens when a clot of blood moves from somewhere (generally the legs) to the heart and then

ZDNet: How an algorithm [by RSIP Vision] is taking the guesswork out of lung biopsies

Our work on pulmonary imaging was featured on ZDNet. Please watch their video.

Announcement – Lung Segmentation with AI

RSIP Vision’s Advanced AI Technology Provides Segmentation with Unmatched Precision for Interventional Lung Procedures. New Solution Enables Surgeons to Biopsy Exact Location of Suspicious Lesions

Lung vasculature segmentation

Lungs vasculature has a major part in blood oxygenation. The complicated branches of arteries and veins, accompanied by the intricate bronchial tree are in charge

Lung Tumor Segmentation

Lung cancer is the most common cancer related mortality cause among men, and second in women worldwide. Primary lung cancer is usually divided into two

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

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,

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.

Automated lung segmentation - airways and blood vessels

RSIP Vision has built a lower respiratory tract segmentation software using advanced image processing algorithms. This region includes both lungs along with their pulmonary vasculature. This lung segmentation software takes advantage of the structure of vascular and capillary tree in the area as an exploratory tool for lungs segmentation, enabling both diagnosis and planning of invasive interventions.

Lungs tumors and nodules segmentation with Deep Learning

It is visually more difficult to identify lung tumors than nodules, since the latter are supposed to have an elliptical shape, while the chromatic aspect of the former is quite hard to distinguish from healthy tissues on a CT image. We use Deep Learning neural networks to overcome this difficulty in a way that is quick to perform, reliable and memory efficient. Our software of computer vision in pulmonology detects and classifies tumors and nodules in the fastest time, to provide our clients a quick and reliable 3D segmentation of lung tumors with Deep Learning.

Lung Nodule Classification

Lung cancer early detection is a vital task which is made difficult by the small size of pulmonary nodules, the detection of which on thousands of CT scans every day is excessively time-consuming. Computer-aided lung nodule classification can dramatically boost the speed of diagnosis. Recommended solution starts from bidimensional images obtained from CT scan and displaying suspicious nodules areas: these are inserted into an autoencoder, from which two hundred dimensional features are extracted. These learned features are then confronted with a trained classifier to produce the final lung nodules classification.

Chest CT Registration

Lung cancer is the leading cancer killer of men and women in the U.S. and it causes more deaths than colorectal, breast and prostate cancers

Airways segmentation with Deep Learning

Image processing is a fundamental technique in the quest to identify lung cancer, one of the main causes of death among both men and women, and many other lung pathologies. For the worst diseases, survival rate depends on the stage in which the disease is diagnosed and correct segmentation of airway vessels offers the most effective solution to determine the lesion’s size and location, significantly improving diagnosis and treatment. Our solution is built upon Deep Learning and neural networks.

Pulmonary lobes segmentation

Emphysema quantification and lung nodule detection are among the clinical applications which benefit the most from lobes segmentation in CT scans. Proper lung segmentation is key to determine the boundaries of lobes and prevent pleural damage during examination and treatment. When correctly located, diseases are treated faster and better, hence the call for RSIP Vision to find a faster alternative to time-consuming manual segmentation.

Pulmonary embolism detection

Timely detection of pulmonary embolism via CT angiography is key to reduce mortality risks. Human detection of pulmonary embolism (being often too slow, RSIP Vision recommends a combination of bidimensional and tridimensional image-processing techniques to achieve computer-aided pulmonary embolism detection which enables prompter diagnosis and treatment.

Lung Lymph Nodes Detection

Analyzing pulmonary lymph nodes can give us valuable information for lung cancer diagnosis and treatment. This solution too uses advanced algorithm of computer vision for pulmonology; it also allows to overcome technical difficulties like low image contrast and high nodes variation, offering a drastic improvement over techniques currently used to detect lung lymph nodes.

Lung vessel segmentation

Blood vessel segmentation of the lungs can help to identify important pulmonary diseases, characterizing nodules in the lungs, detecting pulmonary emboli and evaluating the lungs vasculature. Our technique of automatic pulmonary vessel segmentation completes very effectively the vessel tree structure provided by the CT scans of the lung, in such a way that the resulting image is more precise and matchlessly faster than any manual segmentation could be.

Tag: Pulmonology

Announcement – RSIP Vision introduces an innovative set of AI modules for enhanced medical ultrasound applications

Announcement – RSIP Vision Launches a Pioneering AI Suite Providing Optimal Solutions to Key Tasks in Lung Surgery

Detecting Pulmonary Embolism from CT Scan

ZDNet: How an algorithm [by RSIP Vision] is taking the guesswork out of lung biopsies

Announcement – Lung Segmentation with AI

Lung vasculature segmentation

Lung Tumor Segmentation

Lung Nodules Segmentation

Lung Fissures Segmentation

Chest CT Scan Analysis with Deep Learning

Deep Learning in Pulmonology

Lung Segmentation Software

Lungs tumors and nodules segmentation with Deep Learning

Lung Nodule Classification

Chest CT Registration

Airways segmentation with Deep Learning

Pulmonary lobes segmentation

Pulmonary embolism detection

Lung Lymph Nodes Detection

Lung vessel segmentation

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