Information processing apparatus, information processing method, program, trained model, and learning model generation method

An information processing method executed by one or more processors, the method comprising:

• via the one or more processors,
• acquiring a first image captured by a first imaging protocol for a designated subject using a designated type of modality;
• inputting the first image to an image generation model and receiving a second image, that imitates an image obtained in a case in which imaging is performed by a second imaging protocol different from the first imaging protocol for the designated subject using the designated type of modality, output by the image generation model;
• and
• calculating a clinical parameter from the second image,
• wherein the image generation model is a model trained, by using a plurality of training data,
• wherein the plurality of training data comprises a training image captured by the first imaging protocol and a correct answer clinical parameter calculated from a corresponding image captured by the second imaging protocol for the designated subject using the designated type of modality, such that the clinical parameter calculated from a generation image output by the image generation model in response to input of the training image approaches the correct answer clinical parameter.

A non-transitory, computer-readable tangible recording medium which records thereon a program for causing, when read by a computer, the computer to realize:

• a function of acquiring a first image captured by a first imaging protocol for a designated subject using a designated type of modality;
• a function of inputting the first image to an image generation model and receiving a second image, that imitates an image obtained in a case in which imaging is performed by a second imaging protocol different from the first imaging protocol for the designated subject using the designated type of modality, output by the image generation model;
• and
• a function of calculating a clinical parameter from the second image,
• wherein the image generation model is a model trained, through machine learning using a plurality of training data,
• wherein the plurality of training data comprises a training image captured by the first imaging protocol and a correct answer clinical parameter calculated from a corresponding image captured by the second imaging protocol for the designated subject using the designated type of modality, such that the clinical parameter calculated from a generation image output by the image generation model in response to input of the training image approaches the correct answer clinical parameter.

A learning model generation method of generating a learning model that enables a computer to realize a function of generating, from an image which is input, a pseudo image that imitates an image obtained by an imaging protocol different from an imaging protocol of the image, the method comprising:

• via a system including one or more processors,
• performing machine learning using a plurality of training data comprising a training image captured by a first imaging protocol for a designated subject using a designated type of modality and a correct answer clinical parameter calculated from a corresponding image captured by a second imaging protocol different from the first imaging protocol for the designated subject using the designated type of modality;
• and
• inputting the training image to the learning model, calculating a clinical parameter from a generation image output by the learning model, and training the learning model such that the clinical parameter calculated from the generation image approaches the correct answer clinical parameter.