System and method for analyzing corneal lesion using anterior ocular segment image, and computer-readable recording medium

Patent metadata

Field	Value
Publication number	US-12268528-B2
Application number	US-202017784140-A
Country	US
Kind code	B2
Filing date	Dec 10, 2020
Priority date	Dec 10, 2019
Publication date	Apr 8, 2025
Grant date	Apr 8, 2025

Abstract

A system for and a method of analyzing a corneal lesion using an anterior segment image according to the present invention. The system includes: an image acquisition unit configured to acquire an anterior segment image from the eyeball of a subject, a feature extractor configured to extract feature information on a position and a cause of a lesion in the cornea from the anterior segment image by applying a convolution layer to the anterior segment image through machine learning on the basis of a database in which clinical information pre-acquired by analyzing positions and causes of lesions in the corneas of subjects is stored; and a result determination unit configured to identify a position of the cornea from the anterior segment image using the feature information and to analyze and determine the position and the cause of the lesion in the cornea from the position of the cornea.

First claim

The invention claimed is:

1. A system for analyzing a corneal lesion using an anterior segment image, the system comprising:
an image acquisition unit configured to acquire the anterior segment image from an eyeball of a subject;
a feature extractor configured to extract feature information on a position and a cause of a lesion in a cornea of the eyeball from the anterior segment image by applying a convolution layer to the anterior segment image through machine learning on the basis of a database in which clinical information pre-acquired by analyzing positions and causes of lesions in the corneas of subjects is stored; and
a result determination unit configured to identify a position of the cornea from the anterior segment image using the feature information and to analyze and determine the position and the cause of the lesion in the cornea from the position of the cornea;
wherein the feature extractor comprises a slit lamp mask adjustment module adjusting a slit beam image present in a region of the anterior segment image,
wherein the slit lamp mask adjustment module applies the convolution layer to the anterior segment image and an already-collected slit lamp region mask and then adjusts a masking ratio by applying a weighting factor for a slit beam portion.

2. The system of claim 1 , wherein the feature extractor comprises:
a residual network (ResNet) obtained by stacking at least one network of a plurality of networks each including a convolution layer, a pooling layer, and an activation function or a rectified linear unit (ReLU) function and the ResNet extracts the feature information, including a multiple-channel feature map for extracting a suspicious region, from the anterior segment image.

3. The system of claim 2 , wherein the feature extractor comprises:
a lesion guiding module configured to extract a lesion region more precise than the suspicious region through convolution of the feature information and positional data of a lesion in the clinical information, the positional data representing a position of the lesion.

4. The system of claim 1 , wherein the slit lamp mask adjustment module excludes a slit lamp portion from the anterior segment image and causes the position of the cornea or the position of the lesion in the cornea, which is included in the anterior segment image, to be learned.

5. The system of claim 1 , wherein the result determination unit re-inputs a 3 rd label feature vector output from the feature extractor and a prediction vector corresponding to a 2 nd label feature vector computed by applying a fully connected layer to the 3 rd label feature vector, into the fully connected layer and thus identifies the cause of the lesion in the cornea.

6. A method of analyzing a corneal lesion using an anterior segment image, the method comprising:
a step of acquiring, by an image acquisition unit, the anterior segment image from an eyeball of a subject;
a step of extracting, by a feature extractor, feature information on a position and a cause of a lesion in a cornea of the eyeball from the anterior segment image by applying a convolution layer to the anterior segment image and a database in which clinical information pre-acquired by analyzing positions and causes of lesions in the corneas of subjects is stored; and
a step of analyzing and determining, by a result determination unit, the position and the cause of the lesion in the cornea after identifying a position of the cornea from the anterior segment image using the feature information;
wherein the step of extracting, by the feature extractor, the feature information comprises, a step of adjusting, by a slit lamp mask adjustment module of the feature extractor, a slit beam image present in a region of the anterior segment image; and
wherein the step of adjusting, by the slit lamp mask adjustment module, the slit beam image comprises a step of applying, the slit lamp mask adjustment module, the convolution layer to the anterior segment image and an already-collected slit lamp region mask and a step of adjusting a masking ratio by applying a weighting factor for a slit beam portion.

7. The method of claim 6 , wherein the step of extracting, by the feature extractor, the feature information comprises:
a step of extracting the feature information including a multiple-channel feature map for extracting a suspicious region, from the anterior segment image, the feature information being destined for a residual network (ResNet) obtained by stacking at least one network of a plurality of networks each including a convolution layer, a pooling layer, and an activation function or a rectified linear unit (ReLU) function.

8. The method of claim 7 , wherein the step of extracting the feature information comprises:
a step of extracting a lesion region more precise than the suspicious region through convolution of the feature information and positional data of a lesion in the clinical information, the positional data representing a position of the lesion, by a lesion attention module.

9. The method of claim 6 , wherein the step of determining, by the result determination unit, the position and the cause of the lesion in the cornea comprises:
a step of outputting, by the feature extractor, a 3 rd label feature vector;
a step of outputting a prediction vector corresponding to a 2 nd label feature vector computed by applying a fully connected layer to the 3 rd label feature vector; and
a step of re-inputting, by the result determination unit, the feature vector and the prediction vector to the fully connected layer.

10. A non-transitory computer-readable storage device on which a program that performs a method of analyzing a corneal lesion using an anterior segment image is recorded, the method comprising:
a step of acquiring, by an image acquisition unit, the anterior segment image from an eyeball of a subject;
a step of extracting, by a feature extractor, feature information on a position and a cause of a lesion in a cornea of the eyeball from the anterior segment image by applying a convolution layer to the anterior segment image and a database in which clinical information pre-acquired by analyzing positions and causes of lesions in the corneas of subjects is stored; and
a step of analyzing and determining, by a result determination unit, the position and the cause of the lesion in the cornea after identifying a position of the cornea from the anterior segment image using the feature information;
wherein the step of extracting, by the feature extractor, the feature information comprises, a step of adjusting, by a slit lamp mask adjustment module of the feature extractor, a slit beam image present in a region of the anterior segment image; and
wherein the step of adjusting, by the slit lamp mask adjustment module, the slit beam image comprises a step of applying, the slit lamp mask adjustment module, the convolution layer to the anterior segment image and an already-collected slit lamp region mask and a step of adjusting a masking ratio by applying a weighting factor for a slit beam portion.

Assignees

• Samsung Life Public Welfare Foundation

Inventors

• Lim Dong Hui
• Han Gyu Le
• Kim Young Jun
• Ro Yong Man
• Lee Hye Bin

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06T2207/30096

  Tumor; Lesion · CPC title

• G06T2207/30041

  Eye; Retina; Ophthalmic · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title