# US 12,602,778 B2 · Information processing apparatus, information processing method, and information processing program

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- Number: US 12,602,778 B2
- Document ID: US12602778B2
- Status: Granted
- Kind: B2
- Application number: 18/186,220
- Filed: 2023-03-20
- Published: 2023-07-20
- Granted: 2026-04-14
- Earliest claimed: 2020-09-29
- Assignees: FUJIFILM Corporation
- Applicants: FUJIFILM Corporation
- Inventors: Saeko Sasuga
- Web page: https://patentgrep.com/patent/US12602778B2

## CPC

- G06T 7/0012: {Biomedical image inspection}
- G06V 10/82: using neural networks
- A61B 6/03: Computed tomography [CT]
- G06T 7/11: Region-based segmentation
- G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI]
- G06V 10/758: {Involving statistics of pixels or of feature values, e.g. histogram matching}
- G06V 10/778: Active pattern-learning, e.g. online learning of image or video features
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/20081: Training; Learning
- G06T 2207/30096: Tumor; Lesion
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)

## Abstract

A plurality of training data in which range information indicating a range in which a first region and a second region including at least a part of the first region are present is added to each of a plurality of training images each including the first region and the second region are acquired. For each pixel of the training image, a probability that the pixel is a portion of the first region that is not included in the second region is calculated by using an estimation model. A probability sum, which is a sum of the probabilities, is calculated for each of the plurality of training images. The estimation model is trained such that the probability sum calculated for each of training images in which the first region has the portion that is not included in the second region is increased and the probability sum calculated for each of training images in which the first region does not have the portion that is not included in the second region is zero.

## Claims

### Claim 1

An information processing apparatus comprising:
at least one processor,
wherein the processor
acquires a plurality of training data in which range information indicating a range in which a first region and a second region including at least a part of the first region are present is added to each of a plurality of training images each including the first region and the second region,
wherein the training data includes both a training image including the first region and the second region having a complete inclusion relationship and a training image including the first region and the second region having a partial inclusion relationship,
wherein in a case of the partial inclusion relationship, a part of the range in which the first region is present is not included in the range in which the second region is present and, the partial inclusion relationship is different from the complete inclusion relationship where an entire range in which the first region is present is included in the range in which the second region is present,
calculates, for each pixel of the training image, a probability that the pixel is a portion of the first region that is not included in the second region by using an estimation model,
calculates a probability sum, which is a sum of the probabilities, for each of the plurality of training images, and
trains the estimation model such that for each of training images having the partial inclusion relationship in which the first region has the portion that is not included in the second region, the probability sum is increased and for each of training images having the complete inclusion relationship in which the first region does not have the portion that is not included in the second region, the probability sum is zero.

### Claim 2 (depends on 1)

The information processing apparatus according to claim 1,
wherein the processor trains the estimation model such that a loss is reduced, the loss including, as one of elements, an evaluation value in which a first average value, which is an average value of the probability sum calculated for each of the training images in which the first region has the portion that is not included in the second region, is included in a denominator and a second average value, which is an average value of the probability sum calculated for each of the training images in which the first region does not have the portion that is not included in the second region, is included in a numerator.

### Claim 3 (depends on 2)

The information processing apparatus according to claim 2,
wherein, in a case in which the evaluation value is E1, the first average value is A1, the second average value is A2, and predetermined constants are α and β, Expression (I) is satisfied
E1=(A2+α)/(A1+A2+β) (I).

### Claim 4 (depends on 1)

The information processing apparatus according to claim 1,
wherein the estimation model calculates, for each pixel of the training image, a probability Pa that the pixel is the first region and a probability Pb that the pixel is the second region to calculate Pa×(1−Pb) as the probability that the pixel is the portion of the first region that is not included in the second region.

### Claim 5 (depends on 1)

The information processing apparatus according to claim 1,
wherein the training image is a medical image, and
the first region is a lesion region and the second region is a biological tissue region including at least a part of the lesion region.

### Claim 6 (depends on 1)

The information processing apparatus according to claim 1,
wherein the processor
acquires a target image including the first region and the second region,
specifies at least one of the first region, the second region, or the portion of the first region that is not included in the second region by using the estimation model, and
performs control of displaying the specified region or portion in a discriminable manner.

### Claim 7 (depends on 1)

The information processing apparatus according to claim 1,
wherein, instead of the probability sum, the number of pixels in which the probability that the pixel is the portion of the first region that is not included in the second region is equal to or larger than a threshold value is used.

### Claim 8

An information processing method executed by at least one processor provided in an information processing apparatus, the method comprising:
acquiring a plurality of training data in which range information indicating a range in which a first region and a second region including at least a part of the first region are present is added to each of a plurality of training images each including the first region and the second region,
wherein the training data includes both a training image including the first region and the second region having a complete inclusion relationship and a training image including the first region and the second region having a partial inclusion relationship,
wherein in a case of the partial inclusion relationship, a part of the range in which the first region is present is not included in the range in which the second region is present and, the partial inclusion relationship is different from the complete inclusion relationship where an entire range in which the first region is present is included in the range in which the second region is present;
calculating, for each pixel of the training image, a probability that the pixel is a portion of the first region that is not included in the second region by using an estimation model;
calculating a probability sum, which is a sum of the probabilities, for each of the plurality of training images; and
training the estimation model such that for each of training images having the partial inclusion relationship in which the first region has the portion that is not included in the second region, the probability sum is increased and for each of training images having the complete inclusion relationship in which the first region does not have the portion that is not included in the second region, the probability sum is zero.

### Claim 9

A non-transitory computer-readable storage medium storing an information processing program causing at least one processor provided in an information processing apparatus to execute a process comprising:
acquiring a plurality of training data in which range information indicating a range in which a first region and a second region including at least a part of the first region are present is added to each of a plurality of training images each including the first region and the second region,
wherein the training data includes both a training image including the first region and the second region having a complete inclusion relationship and a training image including the first region and the second region having a partial inclusion relationship,
wherein in a case of the partial inclusion relationship, a part of the range in which the first region is present is not included in the range in which the second region is present and, the partial inclusion relationship is different from the complete inclusion relationship where an entire range in which the first region is present is included in the range in which the second region is present;
calculating, for each pixel of the training image, a probability that the pixel is a portion of the first region that is not included in the second region by using an estimation model;
calculating a probability sum, which is a sum of the probabilities, for each of the plurality of training images; and
training the estimation model such that for each of training images having the partial inclusion relationship in which the first region has the portion that is not included in the second region, the probability sum is increased and for each of training images having the complete inclusion relationship in which the first region does not have the portion that is not included in the second region, the probability sum is zero.