Method for detecting lesion and computer-readable recording medium storing lesion detection program

Patent metadata

Field	Value
Publication number	US-2025082293-A1
Application number	US-202418816786-A
Country	US
Kind code	A1
Filing date	Aug 27, 2024
Priority date	Sep 13, 2023
Publication date	Mar 13, 2025
Grant date	—

Abstract

A method for detecting lesion for a computer to execute a process includes a training process that includes calculating a first image feature for each of first tomographic image groups obtained from first human bodies, classifying tomographic images included in the first tomographic image groups into second tomographic image groups, and generating, through machine learning, first lesion identification models configured to identify whether or not unit image regions in the tomographic images are regions of a particular lesion, and a lesion detection process that includes acquiring, from the first lesion identification models, probabilities of regions of the particular lesion, for each of the unit image regions, integrating, for each of the unit image regions, the probabilities based on a second image feature of a same type as the first image feature obtained from second human body, detecting the regions of the particular lesion from the first tomographic images.

First claim

What is claimed is:

1 . A method for detecting lesion for a computer to execute a process comprising:
a training process that includes: calculating a first image feature, based on a plurality of first tomographic image groups obtained by imaging an inside of each of a plurality of first human bodies, for each of the first tomographic image groups; classifying tomographic images included in the plurality of first tomographic image groups into a plurality of second tomographic image groups, according to a range of the first image feature; and generating a plurality of first lesion identification models configured to identify whether or not unit image regions included in the tomographic images to be identified are regions of a particular lesion, for each of the unit image regions, each through machine learning that uses different ones of the plurality of second tomographic image groups from each other, as training data; and a lesion detection process that includes: calculating a second image feature of a same type as the first image feature, based on a plurality of first tomographic images obtained by imaging the inside of a second human body; acquiring, from each of the plurality of first lesion identification models, probabilities of being the regions of the particular lesion, for each of the unit image regions included in the plurality of first tomographic images, by inputting the plurality of first tomographic images to each of the plurality of first lesion identification models; integrating, for each of the unit image regions included in the plurality of first tomographic images, the probabilities acquired from each of the plurality of first lesion identification models, based on the second image feature, to calculate an integrated value; and detecting the regions of the particular lesion from each of the plurality of first tomographic images, based on the integrated value.

2 . The method according to claim 1 , wherein
the plurality of second tomographic image groups includes the second tomographic image groups that include the first tomographic image groups in which the first image feature is equal to or greater than a first threshold value, and the second tomographic image groups that include the first tomographic image groups in which the first image feature is equal to or less than a second threshold value lower than the first threshold value.

3 . The method according to claim 1 , wherein
the first image feature is calculated based on pixel information on the regions of a particular organ in image regions of the respective tomographic images included in the first tomographic image groups, and the second image feature is calculated based on the pixel information on the regions of the particular organ in the image regions of the plurality of first tomographic images.

4 . The method according to claim 3 , wherein
the first image feature is a first average value that indicates an average of luminance in the regions of the particular organ in the image regions of the respective tomographic images included in the first tomographic image groups, and the second image feature is a second average value that indicates the average of the luminance in the regions of the particular organ in the image regions of the plurality of first tomographic images.

5 . The method according to claim 4 , wherein
the calculating the integrated value includes calculating the integrated value by performing weighted addition on the probabilities acquired from each of the plurality of first lesion identification models, and a higher weighting factor is set for the probabilities from the first lesion identification models generated by using the second tomographic image groups that have a lower first average value, among the plurality of first lesion identification models, as the second average value is lower.

6 . The method according to claim 1 , wherein
the training process includes: classifying the plurality of first tomographic image groups into a plurality of third tomographic image groups that have different medical findings; and generating a plurality of second lesion identification models configured to identify whether or not the unit image regions included in the tomographic images to be identified are the regions of the particular lesion, for each of the unit image regions, each through the machine learning that uses different ones of the plurality of third tomographic image groups from each other, as the training data, the lesion detection process includes acquiring the probabilities from each of the plurality of second lesion identification models, for each of the unit image regions included in the plurality of first tomographic images, by inputting the plurality of first tomographic images to each of the plurality of second lesion identification models, and the calculating the integrated value includes integrating, for each of the unit image regions included in the plurality of first tomographic images, the probabilities acquired from each of the plurality of first lesion identification models and each of the plurality of second lesion identification models, based on the second image feature, to calculate the integrated value.

7 . The method according to claim 6 , wherein
the classifying the plurality of first tomographic images into the plurality of third tomographic image groups includes classifying the plurality of first tomographic images into the third tomographic image groups that include the tomographic images with normal findings and the third tomographic image groups that include the tomographic images with anomalous findings, and the training process includes: generating a third lesion identification model configured to identify whether or not the unit image regions included in the tomographic images to be identified are the regions of the particular lesion, for each of the unit image regions, through the machine learning that uses, as the training data, the tomographic images of which the first image feature is included in a first range, among the tomographic images with the normal findings; and generating a fourth lesion identification model configured to identify whether or not the unit image regions included in the tomographic images to be identified are the regions of the particular lesion, for each of the unit image regions, through the machine learning that uses, as the training data, the tomographic images of which the first image feature is included in a second range, among the tomographic images with the anomalous findings, the lesion detection process includes acquiring the probabilities from each of the third lesion identification model and the fourth lesion identification model, for each of the unit image regions included in the plurality of first tomographic images, by inputting the plurality of first tomographic images to the third lesion identification model and the fourth lesion identification model, and the calculating the integrated value includes integrating, for each of the unit image regions included in the plurality of first tomographic images, the probabilities acquired from each of the plurality of first lesion identification models, each of the plurality of second lesion identification models, the third lesion identification model, and the fourth lesion identification model, based on the second image feature, to calculate the integrated value.

8 . A non-transitory computer-readable recording medium storing a lesion detection program for causing a computer to execute a process comprising:
a training process that includes: calculating a first image feature, based on a plurality of first tomographic image groups obtained by imaging an inside of each of a plurality of first human bodies, f

Assignees

• Fujitsu Ltd

Inventors

• Baba Kozo
• Takebe Hiroaki
• Miyazaki Nobuhiro

Classifications

• G06T2207/30096

  Tumor; Lesion · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/10081

  Computed x-ray tomography [CT] · CPC title

• G06T7/0014

  using an image reference approach · CPC title

• G06T2207/20084

  Artificial neural networks [ANN] · CPC title