Diagnosis support device, learning device, diagnosis support method, learning method, and program
AssigneeNikon Corporation
InventorsToshihide Kurihara, Yusaku Katada, Kazuo Tsubota, Kanato Masayoshi
Life of this patent
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Abstract
A device for supporting diagnosis has: a reception unit that is configured to receive a fundus image of a subject eye; an identification unit provided with a trained model that is configured to recognize, in the fundus image of the subject eye, an area of abnormality in blood circulation, wherein the model has been trained based upon an image of a fundus and an area of abnormality in blood circulation specified in a fluorescent angiography image of the fundus; and an output unit that is configured to output information relating to the area of abnormality in blood circulation recognized in the fundus image of the subject eye.
Claims13
A method for supporting diagnosis comprising:
- receiving a fundus image,
- processing the fundus image using a trained model configured to recognize an area of abnormality in blood circulation in the fundus image, wherein the trained model has been trained based upon a training data set comprising plurality sets of a training fundus image, a fluorescent angiography image, and information of blood circulation abnormality associated with the fluorescent angiography image, the information of blood circulation abnormality associated with the fluorescent angiography image comprising an annotation regarding a retinal non-perfusion area and/or an area corresponding to a neovascularization, and
- outputting information relating to the area of abnormality in blood circulation in the fundus image,
- wherein the fluorescent angiography image and the training fundus image are acquired from a same patient.
A device for supporting diagnosis, comprising:
- a memory;
- a processor connected to the memory and that:
- receive a fundus image,
- process the fundus image using a trained model configured to recognize an area of abnormality in blood circulation in the fundus image, wherein the trained model has been trained based upon a training data set comprising plurality sets of a training fundus image, a fluorescent angiography image, and information of blood circulation abnormality associated with the fluorescent angiography image, the information of blood circulation abnormality associated with the fluorescent angiography image comprising an annotation regarding a retinal non-perfusion area and/or an area corresponding to a neovascularization, and
- output information relating to the area of abnormality in blood circulation in the fundus image,
- wherein the fluorescent angiography image and the training fundus image are acquired from a same patient.
A non-transitory computer readable medium, storing a program causing a computer to execute:
- receiving a fundus image,
- processing the fundus image using a trained model configured to recognize an area of abnormality in blood circulation in the fundus image of the subject eye, wherein the trained model has been trained based upon a training data set comprising plurality sets of a training fundus image, a fluorescent angiography image and an information of blood circulation abnormality associated with the fluorescent angiography image, the information of blood circulation abnormality associated with the fluorescent angiography image comprising an annotation regarding a retinal non-perfusion area and/or an area corresponding to a neovascularization, and
- outputting information relating to the area of abnormality in blood circulation in the fundus image,
- wherein the fluorescent angiography image and the training fundus image are acquired from a same patient.
Dependencies are inferred from the claim text.