Image generation apparatus, image generation method, and recording medium

Patent metadata

Field	Value
Publication number	US-2025273323-A1
Application number	US-202519056901-A
Country	US
Kind code	A1
Filing date	Feb 19, 2025
Priority date	Feb 27, 2024
Publication date	Aug 28, 2025
Grant date	—

Abstract

This image generation apparatus includes: an acquiring section for acquiring a medical image of a patient; a detecting section for detecting a lesion candidate in the medical image; a differentiating section for generating, from information indicating the lesion candidate, a result of differentiation of the lesion candidate regarding a medical condition the patient has; an image generating section for generating, based on the information indicating the lesion candidate and the result of differentiation of the lesion candidate, a prediction image which represents a state in which the lesion candidate will be after a lapse of time; and an outputting section for outputting the prediction image.

First claim

1 . An image generation apparatus, comprising
at least one processor, the at least one processor carrying out: an acquiring process of acquiring a medical image of a patient; a detecting process of detecting a lesion candidate in the medical image; a differentiating process of generating, from information indicating the lesion candidate, a result of differentiation of the lesion candidate regarding a medical condition the patient has; an image generating process of generating, based on the information indicating the lesion candidate and the result of differentiation of the lesion candidate, a prediction image which represents a state in which the lesion candidate will be after a lapse of time; and an outputting process of outputting the prediction image.

2 . The image generation apparatus according to claim 1 , wherein
in the image generating process, the at least one processor generates, based on the information indicating the lesion candidate and the result of differentiation of the lesion candidate, a plurality of prediction images which each represent a state in which the lesion candidate will be after a lapse of time.

3 . The image generation apparatus according to claim 2 , wherein
in the image generating process, the at least one processor generates respective accuracies of the plurality of prediction images; and in the outputting process, the at least one processor outputs the plurality of prediction images and the respective accuracies of the plurality of prediction images.

4 . The image generation apparatus according to claim 2 , wherein
in the differentiating process, the at least one processor generates results of differentiation of a plurality of lesion candidates from the information indicating the lesion candidate; and in the image generating process, the at least one processor generates respective prediction images for the results of differentiation of the plurality of lesion candidates.

5 . The image generation apparatus according to claim 1 , wherein
in the image generating process, the at least one processor generates the prediction image based on the information indicating the lesion candidate and the result of differentiation of the lesion candidate, with use of a generative model trained by machine learning in which a result of a lapse of time on a lesion is used as ground truth data, so as to generate a prediction image that represents a state in which the lesion will be after a lapse of time.

6 . The image generation apparatus according to claim 5 , wherein
in the acquiring process, the at least one processor acquires medical information regarding the patient in addition to the medical image; and in the image generating process, the at least one processor generates the prediction image by inputting the information indicating the lesion candidate, the result of differentiation of the lesion candidate, and the medical information to the generative model.

7 . The image generation apparatus according to claim 6 , wherein
the medical information includes at least one selected from the group consisting of personal information regarding the patient, information on findings shown by a medical examination performed on the patient, and medical history information regarding the patient.

8 . The image generation apparatus according to claim 1 , wherein
the prediction image is used in decision making carried out by a medical service worker who provides medical examination and treatment.

9 . An image generation method, comprising
at least one processor acquiring a medical image of a patient; the at least one processor detecting a lesion candidate in the medical image; the at least one processor generating, from information indicating the lesion candidate, a result of differentiation of the lesion candidate regarding a medical condition the patient has; the at least one processor generating, based on the information indicating the lesion candidate and the result of differentiation of the lesion candidate, a prediction image which represents a state in which the lesion candidate will be after a lapse of time; and the at least one processor outputting the prediction image.

10 . A computer-readable non-transitory recording medium having recorded thereon a program for causing a computer to function as an image generation apparatus, the program causing the computer to carry out:
an acquiring process of acquiring a medical image of a patient; a detecting process of detecting a lesion candidate in the medical image; a differentiating process of generating, from information indicating the lesion candidate, a result of differentiation of the lesion candidate regarding a medical condition the patient has; an image generating process of generating, based on the information indicating the lesion candidate and the result of differentiation of the lesion candidate, a prediction image which represents a state in which the lesion candidate will be after a lapse of time; and an outputting process of outputting the prediction image.

Assignees

• Nec Corp

Inventors

• Sakamoto Hirokazu
• Kiyuna Tomoharu
• Kimura Tatsu

Classifications

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G16H50/50

  for simulation or modelling of medical disorders · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/10068

  Endoscopic image · CPC title