JP2018061771A5 - - Google Patents
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- JP2018061771A5 JP2018061771A5 JP2016202782A JP2016202782A JP2018061771A5 JP 2018061771 A5 JP2018061771 A5 JP 2018061771A5 JP 2016202782 A JP2016202782 A JP 2016202782A JP 2016202782 A JP2016202782 A JP 2016202782A JP 2018061771 A5 JP2018061771 A5 JP 2018061771A5
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- 230000003902 lesions Effects 0.000 claims 37
- 230000036210 malignancy Effects 0.000 claims 13
- 238000003672 processing method Methods 0.000 claims 8
- 230000000875 corresponding Effects 0.000 claims 5
- 238000000605 extraction Methods 0.000 claims 4
- 230000004044 response Effects 0.000 claims 3
- 239000000284 extract Substances 0.000 claims 2
- 230000001537 neural Effects 0.000 claims 1
Claims (15)
前記病変疑い領域画像に関する画像特徴ラベルを分類するための学習を行う画像特徴ラベル学習部と、
前記画像特徴ラベル学習部の学習により得られる前記画像特徴ラベルの学習パラメータを用いて、前記病変疑い領域画像の画像特徴量を抽出する画像特徴量抽出部と、
前記病変疑い領域画像を表示する表示部と、
ユーザ入力部と、
前記ユーザ入力部からの入力に応じて、前記学習パラメータを更新する画像特徴ラベル学習更新部と、を備える
ことを特徴とする画像処理装置。 An image processing apparatus that presents a suspected lesion image detected from image data,
An image feature label learning unit that performs learning to classify image feature labels related to the suspected lesion image;
An image feature amount extraction unit that extracts an image feature amount of the suspected lesion region image using a learning parameter of the image feature label obtained by learning of the image feature label learning unit;
A display unit for displaying the suspected lesion area image;
A user input section;
An image processing apparatus comprising: an image feature label learning update unit that updates the learning parameter in response to an input from the user input unit.
前記画像特徴量抽出部は、
前記画像特徴ラベル学習更新部により更新された前記学習パラメータを用いて、前記病変疑い領域画像の画像特徴量を改めて抽出する、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 1,
The image feature amount extraction unit includes:
Using the learning parameters updated by the image feature label learning update unit, the image feature amount of the suspected lesion area image is extracted again.
An image processing apparatus.
前記画像特徴量抽出部で抽出される前記画像特徴量を用いて、病変疑い領域の悪性度を推定するための病変疑い領域悪性度学習部と、
前記病変疑い領域悪性度学習部で得られる病変疑い領域悪性度推定パラメータを用いて、病変疑い領域の悪性度を算出する病変疑い領域悪性度推定部と、
前記病変疑い領域画像のそれぞれに対する前記ユーザ入力部からの入力に応じて、前記病変疑い領域悪性度推定パラメータを更新する病変疑い領域悪性度学習更新部を備える、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 1,
Using the image feature amount extracted by the image feature amount extraction unit, a lesion suspected region malignancy learning unit for estimating the malignancy of a suspected lesion region,
Using the lesion suspected area malignancy estimation parameter obtained in the suspected lesion area malignancy learning unit, the lesion suspected area malignancy estimation unit for calculating the malignancy of the suspected lesion area,
A suspected lesion area malignancy learning update unit that updates the suspected lesion area malignancy estimation parameter in response to an input from the user input unit for each of the suspected lesion area images;
An image processing apparatus.
前記病変疑い領域悪性度推定部は、
前記病変疑い領域悪性度学習更新部より更新された前記病変疑い領域悪性度推定パラメータを用いて、前記病変疑い領域画像に関する病変疑い領域の悪性度を改めて推定する、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 3,
The suspected lesion area malignancy estimation unit is:
Using the lesion suspicious area malignancy estimation parameter updated from the lesion suspicious area malignancy learning update unit, reestimating the malignancy of the lesion suspicious area related to the lesion suspicious area image,
An image processing apparatus.
前記画像特徴量抽出部は、前記学習パラメータを用いて、前記病変疑い領域画像に関する前記画像特徴ラベルの種類を識別する、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 1,
The image feature amount extraction unit identifies the type of the image feature label related to the lesion suspicious area image using the learning parameter.
An image processing apparatus.
前記画像特徴ラベル学習部は、
前記病変疑い領域画像に関する、前記ユーザ入力部から新たに追加された画像特徴ラベルに応じて、前記画像特徴ラベルの種類を追加し、前記学習パラメータを更新する、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 1,
The image feature label learning unit
According to the image feature label newly added from the user input unit related to the suspected lesion region image, the type of the image feature label is added, and the learning parameter is updated.
An image processing apparatus.
前記表示部は、
前記画像データと前記病変疑い領域画像と、前記病変疑い領域画像に対応する前記画像特徴ラベルの識別結果とを表示し、前記病変疑い領域悪性度の推定スコアに対し、順位付けを行い、悪性度の高い順で前記病変疑い領域画像を並べ替える、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 1,
The display unit
Displaying the image data, the suspected lesion area image, and the identification result of the image feature label corresponding to the suspected lesion area image, ranking the estimated score of the suspected lesion area malignancy, Rearrange the suspected lesion image in descending order of
An image processing apparatus.
前記表示部は、
前記画像特徴ラベルの画像と、前記病変疑い領域画像に対応する画像特徴ラベルの識別結果を表示し、前記病変疑い領域画像に対応する画像特徴ラベルの正解をユーザに選択させる、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 7,
The display unit
Displaying the image of the image feature label and the identification result of the image feature label corresponding to the suspected lesion region image, and allowing the user to select the correct image feature label corresponding to the suspected lesion region image;
An image processing apparatus.
前記表示部は、
前記病変疑い領域画像に対応する正誤情報をユーザに選択させる、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 7,
The display unit
Allowing the user to select correct / incorrect information corresponding to the suspected lesion image,
An image processing apparatus.
前記表示部は、
前記病変疑い領域画像の他、ユーザに新たに病変疑い領域画像を追加させ、それに対応する画像特徴ラベルおよび正誤情報を選択させる、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 7,
The display unit
In addition to the suspected lesion area image, the user is allowed to add a new suspected lesion area image and select the corresponding image feature label and correct / incorrect information.
An image processing apparatus.
前記画像特徴ラベル学習部は、
画像特徴ラベルクラスのそれぞれに対し、CNN(Convolutional Neural Network)ネットワークをそれぞれ設定し、
前記CNNネットワーク各々を学習させるための学習データの中、正のサンプルデータはそれぞれの前記画像特徴ラベルクラスに所属する画像であり、負のサンプルデータはそれ以外の画像特徴ラベルクラスに所属する画像である、
ことを特徴とする画像処理装置。 The image processing apparatus according to claim 1,
The image feature label learning unit
For each image feature label class , set up a CNN (Convolutional Neural Network) network,
Among the learning data for learning each of the CNN networks, positive sample data is an image belonging to each of the image feature label classes, and negative sample data is an image belonging to other image feature label classes. is there,
An image processing apparatus.
前記画像処理装置は、
前記病変疑い領域画像に関する画像特徴ラベルを分類するための学習を行い、
前記学習により得られる前記画像特徴ラベルの学習パラメータを用いて、前記病変疑い領域画像の画像特徴量を抽出し、
前記病変疑い領域画像を前記表示部に表示し、
前記ユーザ入力部からの入力に応じて、前記学習パラメータを更新する、
ことを特徴とする画像処理方法。 An image processing method of an image processing apparatus that includes a display unit and a user input unit and presents a suspected lesion area image detected from image data,
The image processing apparatus includes:
Learning to classify image feature labels related to the suspected lesion image,
Using the learning parameter of the image feature label obtained by the learning, extract the image feature amount of the suspected lesion image,
Displaying the suspected lesion image on the display unit;
Updating the learning parameter in response to an input from the user input unit;
An image processing method.
前記画像処理装置は、
更新された前記学習パラメータを用いて、前記病変疑い領域画像の画像特徴量を改めて抽出する、
ことを特徴とする画像処理方法。 The image processing method according to claim 12,
The image processing apparatus includes:
Using the updated learning parameters, the image feature amount of the lesion suspicious area image is extracted again.
An image processing method.
前記画像処理装置は、
前記学習パラメータを用いて、前記病変疑い領域画像に関する前記画像特徴ラベルの種類を識別する、
ことを特徴とする画像処理方法。 The image processing method according to claim 12,
The image processing apparatus includes:
Identifying the type of image feature label for the suspected lesion image using the learning parameter;
An image processing method.
前記画像処理装置は、
前記病変疑い領域画像に関する、前記ユーザ入力部から新たに追加された画像特徴ラベルに応じて、前記画像特徴ラベルの種類を追加し、前記学習パラメータを更新する、
ことを特徴とする画像処理方法。 The image processing method according to claim 12,
The image processing apparatus includes:
According to the image feature label newly added from the user input unit related to the suspected lesion region image, the type of the image feature label is added, and the learning parameter is updated.
An image processing method.
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JP7321671B2 (en) | 2018-04-20 | 2023-08-07 | キヤノン株式会社 | Information processing device, information processing method, information processing system and program |
EP3564961A1 (en) | 2018-05-03 | 2019-11-06 | Koninklijke Philips N.V. | Interactive coronary labeling using interventional x-ray images and deep learning |
HUE058687T2 (en) * | 2018-06-14 | 2022-09-28 | Kheiron Medical Tech Ltd | Immediate workup |
EP3815610A4 (en) * | 2018-06-28 | 2021-09-15 | FUJIFILM Corporation | Medical-image processing device and method, machine learning system, program, and storage medium |
JP7210175B2 (en) * | 2018-07-18 | 2023-01-23 | キヤノンメディカルシステムズ株式会社 | Medical information processing device, medical information processing system and medical information processing program |
JP7370694B2 (en) * | 2018-08-14 | 2023-10-30 | キヤノン株式会社 | Medical information processing device, medical information processing method, and program |
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JP7325942B2 (en) * | 2018-10-19 | 2023-08-15 | キヤノンメディカルシステムズ株式会社 | Image processing device and program |
JPWO2020099986A1 (en) * | 2018-11-15 | 2021-11-04 | 株式会社半導体エネルギー研究所 | Content classification method |
WO2020110774A1 (en) | 2018-11-30 | 2020-06-04 | 富士フイルム株式会社 | Image processing device, image processing method, and program |
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