US 12,283,044 B2
Ophthalmic image processing method, ophthalmic image processing device, and ophthalmic image processing program
Reiko Arita, Saitama (JP); Katsumi Yabusaki, Tokyo (JP); and Miyako Suzuki, Tokyo (JP)
Assigned to Kowa Company, Ltd., Nagoya (JP)
Appl. No. 17/788,949
Filed by Kowa Company, Ltd., Nagoya (JP)
PCT Filed Dec. 23, 2020, PCT No. PCT/JP2020/048106
§ 371(c)(1), (2) Date Jun. 24, 2022,
PCT Pub. No. WO2021/132307, PCT Pub. Date Jul. 1, 2021.
Claims priority of application No. 2019-232939 (JP), filed on Dec. 24, 2019; and application No. 2020-031297 (JP), filed on Feb. 27, 2020.
Prior Publication US 2023/0025493 A1, Jan. 26, 2023
Int. Cl. G06T 7/00 (2017.01); G06T 7/13 (2017.01); G06V 10/44 (2022.01)
CPC G06T 7/0012 (2013.01) [G06T 7/13 (2017.01); G06V 10/44 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/30041 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An ophthalmic image processing method for evaluating a state of a subject's eye from an ophthalmic image in which a subject's eye is shot using machine learning, the ophthalmic image processing method comprising:
a learning step of, in order to learn a state of a subject's eye set in advance as a prediction target, obtaining a learned model by performing learning with respect to a neural network in advance regarding extracting a plurality of subsection images from an ophthalmic image for learning, and predicting a state of a subject's eye for each subsection image by machine learning using correct answer data related to the state of the subject's eye of each subsection image;
an image acquisition step of acquiring an ophthalmic image for a test;
an extraction step of extracting a plurality of subsection images from an ophthalmic image for a test; and
a prediction step of predicting a state of a subject's eye of each subsection image using the learned model, wherein
the extraction of the plurality of subsection images is performed from the ophthalmic image after a predetermined image size is set for each state of a subject's eye that is the prediction target such that a detection correct answer rate becomes equal to or greater than a predetermined value in verification in advance regarding a relationship between a size of a subsection image and a detection correction rate of a state of a subject's eye.