BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a machine learning device and a machine learning method, and particularly relates to machine learning device and method for constructing a machine-learned model that performs classification (segmentation) of structures of an image.

2. Description of the Related Art

In the related art, there has been a technique of detecting anatomical feature points of specific organs by machine learning from three-dimensional medical image data (volume data) such as a computed tomography (CT) image to detect organs included in the volume data.

For example, in JP2017-202321A, by using a supervised machine learning algorithm, anatomical feature points included in volume data are extracted, a model indicating a three-dimensional positional relationship of anatomical feature points in the body and the extracted feature points are compared to optimize the extracted feature points, and a part is detected. Further, in a case where a plurality of organs are shown at one absolute position, positional information of a plurality of parts is assigned to one CT image. In this manner, for example, in a case where a CT image is divided into series, it is possible to divide the CT image into series for each part on the basis of the positional information.

SUMMARY OF THE INVENTION

In recent years, a discriminator that simultaneously extracts a plurality of organs from volume data by machine learning or the like is developed. That is, in a case where volume data is input to the discriminator, data in which a label such as “stomach”, “lung”, “bronchi”, “liver”, or “hepatic portal vein” is added to each of voxels constituting the volume data is output. Such a simultaneous labeling function is useful as preprocessing for a wide variety of applications.

The discriminator that simultaneously performs labeling of two or more organs prepares a number of sets of original volume data and voxels of each organ, which are manually labeled by a doctor or the like, included in the volume data, as ground truth data and completes a learned model by performing machine learning of the data.

Here, the volume data for learning has a very large capacity, and thus the volume data for learning is divided to be input to a machine learning process for the convenience of a memory size used in the machine learning, in some cases.

In case of three-dimensional CT image data, the data is divided in an axial direction by a certain number within a memory size limit. The divided data is data for learning. Here, in a case where the divided data includes only a part of the labeled organ, the data may adversely affect machine learning.

The invention is made in view of such problems, and an object of the invention is to provide machine learning device and method which can prepare divided data suitable for machine learning from volume data for learning.

A machine learning device according to a first aspect of the invention comprises a learning data input unit that receives an input of learning data including volume data of a tomographic image and labeling of a region in the volume data; a division unit that divides the learning data of which the input is received by the learning data input unit to create divided learning data; a learning exclusion target region discrimination unit that discriminates a learning exclusion target region which is a region to be excluded from a target of learning, from the divided learning data created by the division unit and the learning data; and a machine learning unit that performs machine learning of labeling of a region other than the learning exclusion target region discriminated by the learning exclusion target region discrimination unit, on the basis of the divided learning data created by the division unit.

In the machine learning device according to a second aspect of the invention, the learning exclusion target region discrimination unit compares a volume of the region labeled in the divided learning data created by the division unit and a volume of the region labeled in the learning data and discriminates the learning exclusion target region according to whether the volume is equal to or less than a threshold value.

The machine learning device according to a third aspect of the invention further comprises a detection accuracy calculation unit that calculates detection accuracy of a region other than the learning exclusion target region discriminated by the learning exclusion target region discrimination unit, in which the machine learning unit performs machine learning of the labeling of the region other than the learning exclusion target region on the basis of the divided learning data created by the division unit and the detection accuracy calculated by the detection accuracy calculation unit.

In the machine learning device according to a fourth aspect of the invention, the detection accuracy calculation unit calculates the detection accuracy on the basis of an average of Intersection over Union (IoU) between a predicted label and a ground truth label of each region.

In the machine learning device according to a fifth aspect of the invention, the division unit re-divides the learning data such that the entire learning exclusion target region is included.

In the machine learning device according to a sixth aspect of the invention, the division unit creates pieces of divided learning data having an overlapping portion.

In the machine learning device according to a seventh aspect of the invention, the tomographic image is a three-dimensional medical tomographic image, and the region includes an organ.

A machine learning method according to an eighth aspect of the invention, which is executed by a computer, comprises a step of receiving an input of learning data including volume data of a tomographic image and labeling of a region in the volume data; a step of dividing the learning data to create divided learning data; a step of discriminating a learning exclusion target region which is a region to be excluded from a target of learning, from the divided learning data and the learning data; and a step of performing machine learning of labeling of a region other than the learning exclusion target region on the basis of the divided learning data.

A machine learning program for causing a computer to execute the machine learning method and a machine-learned model obtained by machine learning by the machine learning program are also included in the invention.

With the invention, since the learning exclusion target region is discriminated from the divided learning data and machine learning of labeling of regions other than the learning exclusion target region is performed, it is possible to perform machine learning with high accuracy even from the divided data in which only a part of an organ is included.

