JP2018061771A5

JP2018061771A5 -

Info

Publication number: JP2018061771A5
Application number: JP2016202782A
Authority: JP
Filing date: 2016-10-14
Publication date: 2019-03-22

Claims

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.