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Segmentation and classification of geographic atrophy patterns in patients with age related macular degeneration in widefield autofluorescence images

A geography and image technology, applied in image analysis, image data processing, image enhancement and other directions, can solve the problem of time-consuming

Pending Publication Date: 2021-10-29
CARL ZEISS MEDITEC INC +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Visual inspection of FAF images of GA lesions and their phenotype using medical staff is effective but time-consuming
Several segmentation algorithms have been developed to aid in the assessment of GA lesions, but many of these algorithms are semi-automatic and require manual input to segment GA lesions

Method used

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  • Segmentation and classification of geographic atrophy patterns in patients with age related macular degeneration in widefield autofluorescence images
  • Segmentation and classification of geographic atrophy patterns in patients with age related macular degeneration in widefield autofluorescence images
  • Segmentation and classification of geographic atrophy patterns in patients with age related macular degeneration in widefield autofluorescence images

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Embodiment Construction

[0034] Early diagnosis is critical to the successful treatment of various eye diseases. Optical imaging is the method of choice for noninvasive examination of the retina. Although age-related macular degeneration (AMD) is known to be a leading cause of vision loss, a diagnosis is often not made until the damage is present. Therefore, the goal of advanced ophthalmic imaging devices is to provide diagnostic tools to help detect and monitor pathological changes in the preclinical stages of diseases.

[0035] A variety of ophthalmic imaging systems are known in the art, such as fundus imaging systems and optical coherence tomography systems, which may be used with the present invention. Examples of ophthalmic imaging modes are provided below, see for example Figure 13 with 14 . Any of these devices can be used to provide an image of the fundus, which is the inner surface of the eye opposite the lens (or lens) of the eye, and which can include the retina, optic disc, macula, f...

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Abstract

An automated segmentation and identification system / method for identifying geographic atrophy (GA) phenotypic patterns in fundus autofluorescence images. A hybrid process combines a supervised pixel classifier with an active contour algorithm. A trained, machine learning model (e.g., SVM or U-Net) provides initial GA segmentation / classification, and this is followed by Chan-Vese active contour algorithm. The junctional zones of the GA segmented area are then analyzed for geometric regularity and light intensity regularity. A determination of GA phenotype is made, at least in part, from these parameters.

Description

technical field [0001] The present invention generally relates to the field of ophthalmic autofluorescence imaging. More specifically, it relates to the classification of regions of geographic atrophy (geographic atrophy) found in fundus autofluorescence images. Background technique [0002] Age-related macular degeneration (AMD) is the most common cause of blindness among older adults in industrialized countries. Geographic atrophy (GA) is an advanced form of AMD characterized by loss of photoreceptors, retinal pigment epithelium (RPE), and choriocapillary layer. An estimated 5 million people worldwide are affected by geographic atrophy. GA can cause irreversible loss of visual function and is responsible for approximately 20% of severe visual impairment with AMD. To date there are no approved treatments to hinder the progression of GA. In recent years, however, advances in understanding the pathogenesis of GA have led to multiple potential therapies in clinical trials....

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/50G06K9/32G06K9/00G06K9/48G06V10/25
CPCG06V40/197G06V40/193G06V10/25G06V10/469G06V10/421G06V2201/03G06V10/82G06F18/2411G06F18/2413G06T7/149A61B3/12G06T2207/20081G06T2207/20084G06T2207/20104G06T2207/30041
Inventor 尼兰查纳·马尼万南玛丽·德宾
Owner CARL ZEISS MEDITEC INC
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