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
Tag: Retina
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
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
Retinal images enhancement
Image enhancement of retinal structures has the potential to facilitate diagnosis of several eye diseases. Retinal disease diagnosis and monitoring often requires very delicate analysis
Glaucoma Detection
Glaucoma, a high intraocular pressure (IOP) pathology, leading to damage of the optic nerve, can be better detected using deep learning techniques. When it detects the optical disc (the visible section of the optic nerve), the deep learning algorithm helps assess glaucoma in an automated way, starting from the region of interest and providing a reliable probability for the disease, which the physician will use to support both diagnosis and treatment decisions.
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Retinal Detachment Detection
Retinal detachment occurs when part of the retina detaches itself from the pigmented cell layer of the RPE, depriving itself of blood and nutrition: this
Retinal inner layers segmentation
OCT is the only method that can perform noninvasive imaging with non-ionizing radiation and offering relatively good resolution. That is why it has become a
Diabetic retinopathy screening and microaneurysm detection
Diabetic Retinopathy (DR) is a leading cause of blindness, especially among adults and even more among the elderly segments of the population. It is associated
Retinal images taken with mobile cameras
Portable cameras able to help ophthalmologists have been a desired solution for a long time. Among the reasons for the need of an additional device
Geographic Atrophy Segmentation Using SD-OCT
In a previous article, we talked about Geographic Atrophy segmentation in 2D images. This article focuses on how OCT images shed light on the development
Geographic Atrophy (GA)
Geographic Atrophy (generally called GA) is a case of advanced Dry AMD (Age-related Macular Degeneration) which might lead to vision loss. As a consequence of
Retina Montage Software
RSIP Vision has developed software which finds common points within the images and reorients them to ‘stitch’ together an accurate panoramic presentation of the retina. Our image stitching technology reconstructs the vascular tree of the retina also when images are acquired at different angles. Its end result enables the ophthalmologist to analyze the patient’s retinal vascular tree through an efficient and non-invasive process.
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Optical Coherence Tomography Angiography
Optical coherence tomography angiography uses OCT to produce high-resolution images of the vascular tree in the eye. A fast and non-invasive procedure, it generates high resolution tridimensional images. Stitching multiple images together and increasing image information and clarity, RSIP Vision’s solution allows physicians to quickly and accurately detect pathologies related to vascularization without side effects or clinical hazards.
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Multi-modal registration of retina images
Is it possible to perform combination of fundus images coming from different imaging equipment or technologies and taken from different angles? RSIP Vision apparently can, since we used our expertise in computer vision for ophthalmology to provide software to a client who wanted to combine direct images of the fundus with fluorescein images, which need to be reconstructed before being connected together to form a more detailed image. The end result provides ophthalmologists with detailed images of the retina, ensuring more efficient and accurate patient diagnosis.
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Measurement of Retinal Thickness
Retinal thickness is a key measurement used to assess the health of the retina and whether it needs any treatment. Thickness measures can be compared to optimal ranges or to data from the same patient over time, helping ophthalmologists to identify retinal disorders. Advanced optimization methods, borrowed from graph theory, enable us to solve the complex challenge of measuring retinal thickness within reasonable processing time.
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Finding Cysts Part Three: Layer Segmentation
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
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Automatic Detection of Macular Cysts
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
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