Ophthalmology

Improving ophthalmic healthcare through imaging

Treating ophthalmological disorders and diseases requires not only advanced imaging techniques but also innovative and accurate analysis using AI and deep learning tools. RSIP Vision has proven expertise and experience in ophthalmology, including fundus image stitching, eye tracking, Optical Coherence Tomography (OCT), retinal layer segmentation, and many more.

AI and Computer Vision Technologies

Our multidisciplinary team utilizes computer vision and AI algorithms to create fast and efficient segmentation, registration, and classification modules for your medical device.

General Case study

Chest CT Segmentation

The challenge

An industry-leading medical device company required the capability to automatically generate an accurate 3D model of complex patient-specific anatomies
The anatomical model was needed for planning challenging surgical navigation procedures
The pre-existing solution was slow and not robust, requiring extensive and time-consuming manual editing by the interventionist

Our approach

RSIP Vision leveraged its deep-learning based 3d CT auto-segmentation technology to deliver a customized, fast, robust, production-quality solution

The outcome

Core module has been integrated in client platform and is in clinical use
Used in 1000s of cases in hospitals worldwide
>99% segmentation success rate
1mm-level segmentation resolution
30 seconds running time on full-sized chest CT scan

Learn more

Learn more solutions and technologies from this field

Super-Resolution in OCT images

OCT images are frequently used to demonstrate the retina and understand the different pathologies and decide the diagnostics and help with procedure planning. In medical imaging, especially in radiology, deep learning has been used in recent years to gain super-resolution. On one hand, it is possible to scan faster and more efficiently at lower resolutions. On the other hand, quality scans provide added benefits. Super-resolution is a class of techniques in image processing that enhance the resolution of an imaging system, making images sharper and clearer. By improving the quality

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 data is an important technical step for early detection and treatment of common eye disorders, and a central algorithmic challenge for supervised learning approaches in this context if the sparsity of labeled data. We review several new works that suggest new tools and network architectures as deep learning in ophthalmology models to deal with this problem. [4] suggest a semi-supervised learning approach for segmentation of the

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 cells (DCs), which induce T-cell activation, and play a critical role in the pathogenesis of dry eye disease. The density of corneal DC is correlated with both symptoms and clinical signs in dry eye, making it an attractive non-invasive and responsive surrogate biomarker to assess the severity of corneal inflammation. To evaluate the effect of inflammation on the cornea structures and function, coronal images at

Degenerative Diseases Detection in the Eye

Modern imaging knows how to capture an image to see through the eye tissue transparency and inspect retina, vasculature and neural tissue: this phenomenon is unique and it is made possible by the fact that the retina is an extension of the central nervous system (CNS), connected with the optic nerve and therefore accessible for analysis of cerebral pathologies. Alzheimer’s Disease (AD) is a very common kind of dementia, characterized by loss of memory due to damage in nerve cells. Alzheimer’s has no cure, but symptoms can be treated to

Temporary pediatric strabismus in newborn baby

Image processing for pediatric strabismus

Strabismus is a disorder in which the eyes are not properly aligned and point to different directions. When this happens, the “straighter” eye becomes more dominant and the weaker eye does not focus properly. Eventually, the brain ignores input from the weaker eye; this disorder is called amblyopia or “lazy eye”. The vision may be blurred due to the eye failing to achieve normal visual acuity. Strabismus becomes a problem which needs to be properly addressed when the newborn’s eyes fail to straighten and keep wandering by his or her 6th month of life.

ROP - Vessel tortuosity in Retinopathy of Prematurity

ROP: Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a leading cause of blindness in infants. ROP (or Terry syndrome) is a disease of the eye affecting prematurely-born, low birthweight infants having received intensive neonatal care which includes oxygen therapy. Oxygen toxicity causes abnormal growth of retinal blood vessels. These vascular changes are described as plus disease and are defined as abnormal dilation and tortuosity of the blood vessels, which are fragile and can leak and scar the retina, potentially leading to retinal detachment. The longer retinal detachment goes untreated, the greater the risk

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Ophthalmology

Improving ophthalmic healthcare through imaging

AI and Computer Vision Technologies

Retina Image Stitching

Multimodal Registration

Motion Tracking

Optical Coherence Tomography (OCT) Analysis

Confocal Microscopy Analysis of the Cornea

Geographic Atrophy (GA) Segmentation

Pupil Distance Measurement

Image Analysis for OCT

Automated OCTA Analysis

General Case study

Chest CT Segmentation

The challenge

Our approach

The outcome