JP2022155690A5 - - Google Patents

Description

An image processing device according to at least one embodiment of the disclosed technology includes:
The trained model is obtained by training using training data including input data which is a medical image having a first image size, and is equipped with an image processing unit which outputs as output data a second image generated by inputting a first image which is a medical image having a second image size larger than the first image size as input data to the trained model.

Claims (27)

An image processing device comprising: an image processing unit that outputs as output data a second image generated by inputting a first image, which is a medical image having a second image size larger than the first image size, into the trained model as input data, the trained model being obtained by training using training data including input data which is a medical image having a first image size. The image processing device according to claim 1, wherein the image processing unit inputs a plurality of medical images having a third image size larger than the second image size obtained by dividing a medical image having the second image size into a plurality of images as input data to the trained model, and outputs a plurality of images corresponding to the plurality of medical images as output data. The image processing device according to claim 2, wherein the image processing unit generates an image of the third image size by synthesizing the output images. The image processing device according to any one of claims 1 to 3, wherein the trained model includes an Encoder-Decoder type Transformer. The image processing device according to any one of claims 1 to 4, wherein the image processing unit performs image processing on a target region in the first image or the second image so that the difference between pixel values of the target region and pixel values of regions other than the target region increases and the pixel values of the target region become lower. The image processing device according to claim 5, wherein the image processing includes a process of blending the first image and the second image so that the difference between the pixel values of the target region and the pixel values of the region other than the target region increases and the pixel values of the target region become lower. The image processing device according to claim 5 or 6, wherein the image processing unit detects a target area in a medical image using the first image or the second image, and performs the image processing on a target area in the second image that corresponds to the detected target area in the first image. The image processing device according to any one of claims 1 to 7, wherein the image processing unit detects a target region using the trained model. The medical image is a frontal image,
The image processing device according to claim 1 , wherein the image processing unit detects a two-dimensional target region using the trained model obtained by learning using a plurality of front images of a subject corresponding to a plurality of depth ranges as training data. The medical image is a frontal image,
The image processing device according to any one of claims 1 to 7, wherein the image processing unit selects a trained model corresponding to a depth range selected in accordance with an instruction from an examiner from among a plurality of trained models obtained by learning using a plurality of front images of a subject corresponding to a plurality of depth ranges as respective training data, and detects a two-dimensional target region using the selected trained model. The medical image is a three-dimensional medical image.
The image processing device according to claim 1 , wherein the image processing unit detects a three-dimensional target region by using the trained model obtained by training using a three-dimensional medical image of a subject as training data. The image processing device according to any one of claims 1 to 7, wherein the image processing unit detects a target region using the first image by rule-based processing based on the structure of the subject. The image processing device according to any one of claims 1 to 12, wherein the image processing includes a process of correcting at least one of brightness and contrast so as to increase the difference between pixel values of a target region and pixel values of a region other than the target region. the medical image of the subject is a motion contrast image of the subject's eye;
The image processing device according to claim 1 , wherein the target region includes at least one of an aperfusion region, a foveal vascular region, and an optic disc region. The image processing device according to any one of claims 1 to 14, wherein the output unit performs image quality improvement processing on the first image using a first trained model obtained by training using a medical image of the subject as training data, and a second trained model obtained by training using a medical image of the subject that has been subjected to blending processing or at least one of brightness and contrast correction processing in a target region of the medical image of the subject, and outputs a second image of the medical image of the subject. The image processing device according to any one of claims 1 to 15, further comprising a display control unit that causes a live video image of the subject to be displayed on a display unit. The image processing device according to claim 16, wherein the display control unit causes the display unit to display a high-quality image generated using a trained model obtained by training training data including images of a subject, the high-quality image being obtained by inputting a medical image of the subject. The image processing device according to claim 17, wherein the display control unit causes the display unit to display the anterior eye image generated as the high-quality image as the live video image, causes the display unit to display an SLO image generated as the high-quality image, in which a line indicating the position of the tomographic image generated as the high-quality image is superimposed, as the live video image, and causes the display unit to display the tomographic image corresponding to the position of the line on the SLO image as the live video image. The image processing device according to claim 18, wherein the display control unit displays information indicating a vascular region in the cross-sectional image generated as the high-quality image and corresponding to the position of the line, superimposed on the cross-sectional image corresponding to the position of the line. The image processing device according to any one of claims 16 to 19, wherein the display control unit causes the display unit to display analysis results obtained by inputting an image related to the subject, the analysis results being generated using a trained model for generating analysis results obtained by training training data including an image related to the subject. The image processing device according to any one of claims 16 to 20, wherein the display control unit causes the display unit to display a diagnostic result obtained by inputting an image related to the subject, the diagnostic result being generated using a trained model for generating a diagnostic result obtained by training training data including an image related to the subject. The image processing device according to any one of claims 16 to 21, wherein the display control unit causes the display unit to display, as information about an abnormal area, information about a difference between an image obtained by inputting an image about a subject, which is generated using a generative adversarial network or an autoencoder, and the image about the subject input to the generative adversarial network or the autoencoder. The image processing device according to any one of claims 16 to 22, wherein the display control unit causes the display unit to display similar case images obtained by inputting an image related to the subject, the similar case images being searched for using a trained model for similar case image search obtained by training training data including an image related to the subject. The image processing device according to any one of claims 16 to 23, wherein the display control unit causes the display unit to display an object recognition result or a segmentation result generated using a trained model for object recognition or a trained model for segmentation obtained by training training data including an image of a subject, the object recognition result or the segmentation result obtained by inputting an image of the subject. The image processing device according to any one of claims 1 to 24, wherein the operator's instruction to acquire the second image is information obtained using at least one of a trained model for character recognition, a trained model for voice recognition, and a trained model for gesture recognition. An image processing method comprising: a trained model obtained by training using training data including input data which is a medical image having a first image size; and a step of outputting as output data a second image generated by inputting as input data a first image which is a medical image having a second image size larger than the first image size into the trained model. A program that, when executed by a computer, causes the computer to execute each step of the image processing method described in claim 26.