JP2021100572A5 - - Google Patents
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- JP2021100572A5 JP2021100572A5 JP2021008808A JP2021008808A JP2021100572A5 JP 2021100572 A5 JP2021100572 A5 JP 2021100572A5 JP 2021008808 A JP2021008808 A JP 2021008808A JP 2021008808 A JP2021008808 A JP 2021008808A JP 2021100572 A5 JP2021100572 A5 JP 2021100572A5
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- 210000001519 tissues Anatomy 0.000 claims 11
- 238000010801 machine learning Methods 0.000 claims 6
- 230000001537 neural Effects 0.000 claims 3
- 238000003384 imaging method Methods 0.000 claims 2
- 238000000034 method Methods 0.000 claims 2
- 238000005259 measurement Methods 0.000 claims 1
Claims (3)
前記眼科画像処理装置の制御部は、
眼科画像撮影装置によって撮影された眼科画像、または、前記眼科画像撮影装置によって組織の同一部位を撮影した複数の眼科画像を加算平均した加算平均画像を、基画像として取得し、
機械学習アルゴリズムによって訓練された数学モデルに前記基画像を入力することで、前記基画像よりも高画質の目的画像を取得し、
前記数学モデルは、組織の同一部位を撮影した複数の訓練用眼科画像のうち、Ｌ枚（Ｌ≧１）の前記訓練用眼科画像に基づくデータを入力用訓練データとし、且つ、Ｈ枚（Ｈ＞Ｌ）前記訓練用眼科画像を加算平均した加算平均画像のデータを出力用訓練データとして訓練されており、
前記機械学習アルゴリズムは、競合する２つのニューラルネットワークを利用する敵対的生成ネットワーク（ＧＡＮ）であることを特徴とする眼科画像処理装置。 An ophthalmic image processing device that processes an ophthalmic image, which is an image of the tissue of the eye to be inspected.
The control unit of the ophthalmic image processing device is
An ophthalmic image taken by the ophthalmic imaging apparatus or an added average image obtained by adding and averaging a plurality of ophthalmic images obtained by photographing the same part of the tissue by the ophthalmic imaging apparatus is acquired as a base image.
By inputting the base image into a mathematical model trained by a machine learning algorithm, a target image having a higher image quality than the base image can be obtained.
In the mathematical model, among a plurality of training ophthalmic images obtained by photographing the same part of the tissue, L (L ≧ 1) data based on the training ophthalmic images are used as input training data, and H (H). > L) The data of the added average image obtained by adding and averaging the training ophthalmic images is trained as the output training data .
The machine learning algorithm is an ophthalmic image processing apparatus characterized by being a hostile generation network (GAN) utilizing two competing neural networks.
前記ＯＣＴ装置の制御部は、
撮影した前記組織の眼科画像、または、前記組織の同一部位を撮影した複数の眼科画像を加算平均した加算平均画像を、基画像として取得し、
機械学習アルゴリズムによって訓練された数学モデルに前記基画像を入力することで、前記基画像よりも高画質の目的画像を取得し、
前記数学モデルは、組織の同一部位を撮影した複数の訓練用眼科画像のうち、Ｌ枚（Ｌ≧１）の前記訓練用眼科画像に基づくデータを入力用訓練データとし、且つ、Ｈ枚（Ｈ＞Ｌ）前記訓練用眼科画像を加算平均した加算平均画像のデータを出力用訓練データとして訓練されており、
前記機械学習アルゴリズムは、競合する２つのニューラルネットワークを利用する敵対的生成ネットワーク（ＧＡＮ）であることを特徴とするＯＣＴ装置。 An OCT device that captures an ophthalmic image of the tissue by processing an OCT signal from the reference light and the reflected light of the measurement light applied to the tissue of the eye to be inspected.
The control unit of the OCT device is
An ophthalmic image of the tissue taken or an added average image obtained by adding and averaging a plurality of ophthalmic images of the same part of the tissue was obtained as a base image.
By inputting the base image into a mathematical model trained by a machine learning algorithm, a target image having a higher image quality than the base image can be obtained.
In the mathematical model, among a plurality of training ophthalmic images obtained by photographing the same part of the tissue, L (L ≧ 1) data based on the training ophthalmic images are used as input training data, and H (H). > L) The data of the added average image obtained by adding and averaging the training ophthalmic images is trained as the output training data .
The machine learning algorithm is an OCT apparatus characterized by being a hostile generation network (GAN) utilizing two competing neural networks.
前記眼科画像処理プログラムが前記眼科画像処理装置の制御部によって実行されることで、
眼科画像撮影装置によって撮影された眼科画像、または、前記眼科画像撮影装置によって組織の同一部位を撮影した複数の眼科画像を加算平均した加算平均画像を、基画像として取得する基画像取得ステップと、
機械学習アルゴリズムによって訓練された数学モデルに前記基画像を入力することで、前記基画像よりも高画質の目的画像を取得する目的画像取得ステップと、
を前記眼科画像処理装置に実行させ、
前記数学モデルは、組織の同一部位を撮影した複数の訓練用眼科画像のうち、Ｌ枚（Ｌ≧１）の前記訓練用眼科画像に基づくデータを入力用訓練データとし、且つ、Ｈ枚（Ｈ＞Ｌ）前記訓練用眼科画像を加算平均した加算平均画像のデータを出力用訓練データとして訓練されており、
前記機械学習アルゴリズムは、競合する２つのニューラルネットワークを利用する敵対的生成ネットワーク（ＧＡＮ）であることを特徴とする眼科画像処理プログラム。
An ophthalmologic image processing program executed by an ophthalmologic image processing apparatus that processes an ophthalmologic image that is an image of the tissue of the eye to be inspected.
By executing the ophthalmologic image processing program by the control unit of the ophthalmologic image processing apparatus,
A basic image acquisition step of acquiring as a basic image an ophthalmic image taken by an ophthalmic image taking device or an added average image obtained by adding and averaging a plurality of ophthalmic images obtained by taking the same part of a tissue by the ophthalmic image taking device.
By inputting the base image into a mathematical model trained by a machine learning algorithm, a target image acquisition step of acquiring a target image having a higher image quality than the base image, and
Is executed by the ophthalmic image processing apparatus.
In the mathematical model, among a plurality of training ophthalmic images obtained by photographing the same part of the tissue, L (L ≧ 1) data based on the training ophthalmic images are used as input training data, and H (H). > L) The data of the added average image obtained by adding and averaging the training ophthalmic images is trained as the output training data .
The machine learning algorithm is an ophthalmic image processing program characterized by being a hostile generation network (GAN) using two competing neural networks.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2021008808A JP7147888B2 (en) | 2020-08-05 | 2021-01-22 | Ophthalmic image processing device, OCT device, and ophthalmic image processing program |
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| JP2020133247A JP6866954B2 (en) | 2020-08-05 | 2020-08-05 | Ophthalmic image processing program and OCT device |
| JP2021008808A JP7147888B2 (en) | 2020-08-05 | 2021-01-22 | Ophthalmic image processing device, OCT device, and ophthalmic image processing program |
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| JP2020133247A Division JP6866954B2 (en) | 2020-08-05 | 2020-08-05 | Ophthalmic image processing program and OCT device |
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| JP2021100572A JP2021100572A (en) | 2021-07-08 |
| JP2021100572A5 true JP2021100572A5 (en) | 2021-11-25 |
| JP7147888B2 JP7147888B2 (en) | 2022-10-05 |
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| KR20230111522A (en) * | 2022-01-18 | 2023-07-25 | 가톨릭대학교 산학협력단 | Apparatus and method for detection of pathologic myopia |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| JPH1131214A (en) * | 1997-07-10 | 1999-02-02 | Hitachi Medical Corp | Picture processor |
| US20130051516A1 (en) | 2011-08-31 | 2013-02-28 | Carestream Health, Inc. | Noise suppression for low x-ray dose cone-beam image reconstruction |
| JP5238893B1 (en) * | 2012-02-22 | 2013-07-17 | 株式会社東芝 | X-ray CT apparatus, image display apparatus, and image display method |
| JP6217185B2 (en) * | 2013-07-02 | 2017-10-25 | 株式会社ニデック | Ophthalmic photographing apparatus and ophthalmic image processing program |
| US10573007B2 (en) | 2015-08-19 | 2020-02-25 | Kowa Company, Ltd. | Image processing apparatus, image processing method, and image processing program |
| US10387765B2 (en) | 2016-06-23 | 2019-08-20 | Siemens Healthcare Gmbh | Image correction using a deep generative machine-learning model |
| JP6987495B2 (en) * | 2016-11-18 | 2022-01-05 | キヤノン株式会社 | Image processing device, its operation method, and program |
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