Identifying artifacts in synthetic medical images

Abstract

The present invention relates to the technical field of producing synthetic medical images. The subject matter of the present disclosure relates to a method, a computer system and a computer-readable storage medium comprising a computer program for identifying artifacts in synthetic medical images.

Description

[0001] Detecting artifacts in synthetic medical images

[0002] COPYRIGHT NOTICE

[0003] A portion of the disclosure of this patent contains material subject to copyright protection. The copyright owner has no objection to the facsimile reproduction of this patent as it appears in a patent file or in the files of the Patent Office, but otherwise reserves all copyright and other rights of any kind. © 2023 Bayer AG

[0004] TECHNICAL FIELD

[0005] The present disclosure relates to the technical field of generating synthetic medical images. Subjects of the present disclosure are a method, a computer system, and a computer-readable storage medium comprising a computer program for detecting artifacts in synthetic medical images.

[0006] INTRODUCTION

[0007] Artificial intelligence is increasingly finding its way into medicine. Machine learning models are not only used to identify signs of disease in medical images of the human or animal body (see, for example, WO2018202541A1, WO2020229152A1), they are also increasingly being used to generate synthetic (artificial) medical images.

[0008] WO2021052896A1 and WO2021069338A1, for example, describe methods for generating an artificial medical image showing an examination area of ​​an examination subject in a first time period, based on medical images showing the examination area in a second time period, using a trained machine learning model. This method can be used, for example, to accelerate radiological examinations; instead of measuring radiological images over a longer period of time, measurements are taken only within a portion of the time period, and one or more radiological images for the remaining portion of the time period are predicted using the trained model.

[0009] For example, WO2019 / 074938A1 and WO2022184297A1 describe methods for generating an artificial radiological image that shows an examination region of an examination subject after the application of a standard amount of contrast agent, even though only a smaller amount of contrast agent than the standard amount has been applied. The methods described in WO2019 / 074938A1 and WO2022184297A can therefore be used to reduce the amount of contrast agent.

[0010] The medical images generated by trained machine learning models may contain errors (see, for example, K. Schwarz et al.: On the Frequency Bias of Generative Models, https: / / doi.org / 10.48550 / arXiv.2111.02447).

[0011] Such errors can be problematic because a physician could make a diagnosis and / or initiate therapy based on the artificial medical images. When a physician examines artificial medical images, the physician needs to know whether features in the artificial medical images can be attributed to real features of the subject under examination or whether they are artifacts resulting from prediction errors by the trained machine learning model. These and other problems are addressed by the subject matter of the present disclosure.

[0012] A first subject of the present disclosure is a computer-implemented method for generating at least one confidence value for a synthetic image, comprising the steps:

[0013] Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area,

[0014] Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model,

[0015] Determine at least one trust value,

[0016] Output at least one trust value.

[0017] A first embodiment of the first subject matter of the present disclosure is a computer-implemented method for generating at least one confidence value for a synthetic image, comprising the steps:

[0018] Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area,

[0019] Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model,

[0020] Determining a measure of dispersion of corresponding image elements of the generated synthetic images, whereby corresponding image elements represent the same sub-area of ​​the examination area,

[0021] Determine at least one confidence value based on the determined dispersion measure,

[0022] Output at least one trust value.

[0023] A further embodiment of the first subject matter of the present disclosure is a computer-implemented method for generating at least one confidence value for a synthetic image, comprising the steps:

[0024] Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area,

[0025] Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model,

[0026] Combining the plurality of synthetic images of the examination area of ​​the examination object into an aggregated synthetic image of the examination area of ​​the examination object,

[0027] Determining a measure of dispersion of corresponding image elements of the received image and the aggregated synthetic image, wherein corresponding image elements represent the same sub-area of ​​the examination area,

[0028] Determine at least one confidence value based on the determined dispersion measure,

[0029] Output at least one trust value.

[0030] A further embodiment of the first subject matter of the present disclosure is a computer-implemented method for generating at least one confidence value for a synthetic image, comprising the steps:

[0031] Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area,

[0032] Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model,

[0033] Determining at least one confidence value based on the color values ​​of corresponding image elements of the generated synthetic images, wherein corresponding image elements represent the same sub-area of ​​the examination area,

[0034] Output at least one trust value.

[0035] A further embodiment of the first subject matter of the present disclosure is a computer-implemented method for generating at least one confidence value for a synthetic image, comprising the steps:

[0036] Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area,

[0037] Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model,

[0038] Determining a scatter measure based on the color values ​​of mutually corresponding image elements of the generated synthetic images, wherein mutually corresponding image elements represent the same sub-area of ​​the examination area, determining at least one confidence value based on the determined scatter measure, outputting the at least one confidence value.

[0039] Another embodiment of the first subject matter of the present disclosure is a computer-implemented method for generating at least one confidence value for a synthetic image, comprising the steps of:

[0040] Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area,

[0041] Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image using a generative model, wherein each synthetic image is created with a different variant of the generative model,

[0042] Combining the plurality of synthetic images of the examination area of ​​the examination object into an aggregated synthetic image of the examination area of ​​the examination object,

[0043] Determining a scatter measure based on the color values ​​of corresponding image elements of the received image and the aggregated synthetic image, wherein corresponding image elements represent the same sub-area of ​​the examination area,

[0044] Determine at least one confidence value based on the determined dispersion measure,

[0045] Output at least one trust value.

[0046] Another subject of the present disclosure is a computer system comprising

[0047] - a receiving unit,

[0048] - a control and computing unit and

[0049] - an output unit, wherein the control and computing unit is configured,

[0050] To cause the receiving unit to receive at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a sub-area of ​​the examination area; to generate a plurality of synthetic images of the examination area of ​​the examination object based on the received image using a generative model, wherein each synthetic image is created using a different variant of the generative model; to determine at least one confidence value; to cause the output unit to output the at least one confidence value. A further subject matter of the present disclosure is a computer-readable storage medium comprising a computer program which, when loaded into a working memory of a computer system, causes the computer system to perform the following steps:

[0051] Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area,

[0052] Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model,

[0053] Determine at least one trust value,

[0054] Output at least one trust value.

[0055] DETAILED DESCRIPTION

[0056] The invention is explained in more detail below, without distinguishing between the subject matters of the present disclosure (method, computer system, computer-readable storage medium). Rather, the following statements apply mutatis mutandis to all subject matters of the invention, regardless of the context in which they are described (method, computer system, computer-readable storage medium).

[0057] If steps are specified in a particular order in this description or in the claims, this does not necessarily mean that the invention is limited to the specified order. Rather, it is conceivable that the steps could also be performed in a different order or even in parallel, unless one step builds on another, which necessarily requires that the subsequent step be performed (although this will become clear in the individual case). The specified orders are therefore preferred embodiments of the present disclosure.

[0058] The invention is explained in more detail at some points with reference to drawings. The drawings depict specific embodiments with specific features and combinations of features, which primarily serve for illustrative purposes; the invention should not be understood as being limited to the features and combinations of features shown in the drawings. Furthermore, statements made in the description of the drawings regarding features and combinations of features are intended to apply generally, meaning they are also transferable to other embodiments and are not limited to the embodiments shown.

[0059] The present disclosure describes means for assessing the trustworthiness of a synthetic image of an examination region of an examination object.

[0060] The term “trustworthiness” means that a person examining the synthetic image can trust that structures and / or morphologies and / or textures depicted in the synthetic image can be traced back to real structures and / or real morphologies and / or real textures of the examination area of ​​the object under investigation and are not artifacts.

[0061] The term "synthetic" means that the synthetic image is not the direct result of a measurement on a real examination object, but has been artificially generated (calculated). However, a synthetic image can be based on measured image recordings of a real examination object, i.e., one or more measured image recordings of a real examination object can be used to generate a synthetic image. Examples of synthetic images are described in the introduction and in the further description of the present disclosure.

[0062] The “object under investigation” is preferably a human or an animal, preferably a mammal, most preferably a human.

[0063] The "area of ​​investigation" is a part of the object under investigation, for example, an organ of a human or animal such as the liver, brain, heart, kidney, lung, stomach, intestine, pancreas, thyroid, prostate, breast, or a part of the aforementioned organs, or multiple organs, or another part of the object under investigation. The area of ​​investigation may also include multiple organs and / or parts of multiple organs.

[0064] The term "image" refers to a data structure representing the spatial distribution of a physical signal. The spatial distribution can have any dimension, e.g., 2D, 3D, 4D, or a higher dimension. The spatial distribution can have any shape, e.g., forming a grid and thereby defining pixels or voxels, where the grid can be irregular or regular. The physical signal can be any signal, e.g., proton density, echogenicity, transmittance, absorbance, relaxivity, information about rotating hydrogen nuclei in a magnetic field, color, grayscale, depth, surface or volume occupancy.

[0065] The term "image" preferably refers to a two-, three-, or higher-dimensional, visually perceivable representation of the examination area of ​​the object under investigation. The received image is typically a digital image. The term "digital" means that the image can be processed by a machine, usually a computer system. "Processing" refers to the well-known methods of electronic data processing (EDP).

[0066] A digital image can be processed, edited, and reproduced using computer systems and software, and converted into standardized data formats such as JPEG (Joint Photographie Experts Group graphics format), PNG (Portable Network Graphics), or SVG (Scalable Vector Graphics). Digital images can be visualized using suitable display devices such as computer monitors, projectors, and / or printers.

[0067] In a digital image, image content is usually represented and stored as whole numbers. In most cases, these are two- or three-dimensional images that can be binary-coded and, if necessary, compressed. Digital images are usually raster graphics in which the image information is stored in a uniform raster. Raster graphics consist of a grid-like arrangement of so-called image points (pixels) in the case of two-dimensional representations or volume elements (voxels) in the case of three-dimensional representations. For four-dimensional representations, the term doxel (dynamic voxel) is often used for the image elements. For higher-dimensional representations or in general, the term "n-xel" is sometimes used, where n indicates the respective dimension. In this disclosure, the term image element is generally used.A picture element can therefore be a picture point (pixel) in the case of a two-dimensional representation, a volume element (voxel) in the case of a three-dimensional representation, a dynamic voxel (doxel) in the case of a four-dimensional representation or a higher-dimensional picture element in the case of a higher-dimensional representation.

[0068] Each image element is assigned a color value. The color value specifies how (e.g., in which color) the image element should be visually displayed (e.g., on a monitor or printer).

[0069] The simplest case is a binary image, where an image element is displayed as either white or black. Typically, the color value "0" represents "black" and the color value "1" represents "white."

[0070] In a grayscale image, each image element is assigned a gray level, ranging from black through a defined number of shades of gray to white. The gray levels are also referred to as gray values. The number of shades can range, for example, from 0 to 255 (i.e., encompassing 256 gray levels / gray values), where the value "0" typically represents "black" and the highest gray value (in this example, the value 255) represents "white."

[0071] In a color image, the color coding used for an image element is defined, among other things, by the color space and the color depth. In an image whose color is defined using the so-called RGB color space (RGB stands for the primary colors red, green, and blue), each pixel is assigned three color values: one color value for the color red, one color value for the color green, and one color value for the color blue. The color of an image element results from the superposition (additive mixing) of the three color values. The individual color value can, for example, be discretized into 256 distinguishable levels called tonal values, which usually range from 0 to 255. The tonal value "0" of each color channel is usually the darkest color nuance. If all three color channels have a tonal value of 0, the corresponding image element appears black; if all three color channels have a tonal value of 255, the corresponding image element appears white.

[0072] Regardless of whether it is a binary image, a grayscale image, or a color image, the term "color value" is used in this disclosure to refer to the information about the color (including the "colors" "black" and "white," as well as all shades of gray) in which an image element is to be displayed. A color value can therefore be a tonal value of a color channel, a shade of gray, or represent "black" or "white."

[0073] A color value in an image (especially a medical image) typically represents the strength of a physical signal (see above). Note that the "color value" can also be a value for the physical signal itself.

[0074] There are a multitude of possible digital image formats and color codings. For the sake of simplicity, this description assumes that the images in question are raster graphics with a specific number of image elements. However, this assumption should not be interpreted as limiting in any way. Those skilled in image processing will understand how to apply the teachings of this description to image files in other image formats and / or where the color values ​​are coded differently.

[0075] An “image” within the meaning of the present disclosure may also be one or more excerpts from a video sequence.

[0076] In a first step, at least one image of an examination area of ​​an object under examination is received.

[0077] The term "receiving" encompasses both retrieving images and receiving images that are transmitted, for example, to the computer system of the present disclosure. The at least one image can be received from a computer tomography scanner, a magnetic resonance imaging scanner, an ultrasound scanner, a camera, and / or another device for generating images. The at least one image can be read from a data storage device and / or transmitted from a separate computer system.

[0078] Preferably, the at least one received image is a two-dimensional or three-dimensional representation of an examination area of ​​an examination object.

[0079] In one embodiment of the present disclosure, the at least one received image is a medical image.

[0080] A “medical image” is a visual representation of an area of ​​examination of a human or animal that can be used for diagnostic and / or therapeutic purposes.

[0081] There are a variety of techniques that can be used to produce medical images; examples of such techniques include X-ray, computed tomography (CT), fluoroscopy, magnetic resonance imaging (MRI), ultrasound (sonography), endoscopy, elastography, tactile imaging, thermography, microscopy, positron emission tomography, optical coherence tomography (OCT), fundus photography, and others.

[0082] Examples of medical images include CT scans, X-ray images, MRI images, fluorescein angiography images, OCT images, histological images, ultrasound images, fundus images and / or others.

[0083] The at least one received image may be a CT scan, MRI scan, ultrasound scan, OCT scan, and / or another representation of an examination region of an examination object.

[0084] The at least one received image may also include representations of different modalities, e.g. a CT scan and an MRI scan.

[0085] Each received image comprises a plurality of image elements. Each image element of the plurality of image elements represents a sub-area of ​​the examination area of ​​the examination subject. The term "plurality of image elements" means at least 1,000, preferably at least 10,000, and even more preferably more than 100,000. It is conceivable for a received image to comprise one or more image elements that do not represent the examination area of ​​the examination subject, but rather another area, such as an adjacent and / or surrounding area.

[0086] In a further step, a plurality of synthetic images are generated from the at least one received image. The term "plurality of synthetic images" means at least two, preferably at least five, and even more preferably at least ten different synthetic images.

[0087] The term "different" means that the majority of synthetic images does not include any synthetic images that are identical. In other words, two synthetic images randomly selected from the majority of different synthetic images are never identical, but rather different from each other.

[0088] The majority of synthetic images are generated using a generative model, with each synthetic image being created using a different variant of the generative model.

[0089] Each variant of the generative model is configured to generate a synthetic image of the examination area of ​​the examination object based on the at least one received image of the examination area of ​​the examination object.

[0090] In the following, the term “the synthetic image” or “a synthetic image” refers to each of the plurality of synthetic images.

[0091] The at least one received image may, for example, comprise a radiological image of the examination area without contrast agent and / or with a smaller amount of contrast agent than the standard amount of contrast agent, and the synthetic image may be a synthetic radiological image of the examination area after application of the standard amount of contrast agent (as described, for example, in WO2019 / 074938A1 or WO2022184297A1). The "standard amount" is typically the amount recommended by the manufacturer and / or distributor of the contrast agent and / or the amount approved by a regulatory authority and / or the amount listed in a package insert for the contrast agent.In such a case, the generative model is configured to generate, based on at least one radiological image of the examination area before and / or after application of a first amount of contrast agent, a synthetic radiological image after application of a second amount of the contrast agent, wherein the second amount is preferably greater than the first amount (as described, for example, in WO2019 / 074938A1 or WO2022184297A1). The at least one received radiological image can be, for example, an MRI image, and the synthetic radiological image can be a synthetic MRI image.The at least one received image may also comprise a CT scan before and / or after the application of a first amount of an MRI contrast agent, and the synthetic representation may be a synthetic CT scan after the application of a second amount of an MRI contrast agent, wherein the second amount is preferably greater than the first amount and preferably greater than the standard amount of MRI contrast agent for MRI examinations (as described, for example, in PCT / EP2023 / 053324).

[0092] The at least one received image can, for example, comprise one or more radiological images of the examination region in a first time period before and / or after the application of a contrast agent, and the synthetic image can be a synthetic radiological image of the examination region in a second time period after the application of the contrast agent (as described, for example, in WO2021052896A1). The generative model can thus be configured to generate, based on at least one radiological image of the examination region in a first time period before and / or after the application of a contrast agent, a synthetic radiological image of the examination region in a second time period after the application of the contrast agent, wherein the second time period preferably follows chronologically the first time period (as described, for example, in WO2021052896A1).

[0093] The generative model can be a model of the machine learning process. The generative model can include one or more algorithms that specify how the synthetic image can be generated based on the at least one received image. Typically, the at least one received image is fed to the generative model, and the model generates the synthetic image based on the supplied at least one image, model parameters, and optionally further input data (see, for example, WO2019 / 074938A1, WO2022184297A1, WO2021052896A1, PCT / EP2023 / 053324).

[0094] The plurality of synthetic images of the examination area of ​​the examination object are generated based on received, preferably measured, images using a generative model, wherein each synthetic image is generated using a different variant of the generative model. Different variants of the generative model can be achieved by applying different techniques. One embodiment for providing different variants of the generative model involves the application of the "dropout" technique.

[0095] Generative models, such as large-scale neural networks, trained on relatively small datasets can lead to overfitting of the training data. Dropout is a regularization method that approximates the parallel training of a large number of generative models, such as neural networks, with different architectures.

[0096] In the typical application of dropouts, some parts of the model (e.g., nodes and / or layer outputs) are ignored, "turned off," or "omitted" during model training, either randomly or according to predefined rules. For example, activations of nodes in a neural network can be set to zero, so that the corresponding nodes always return the output value zero, regardless of the input values.

[0097] The proportion of dropped units can be predefined or variable within defined limits. For example, it can be 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 1.0%, 1.2%, 2%, 5%, or another value. Dropped units can be distributed throughout the entire neural network; however, dropped units can also be restricted to predefined layers, such as fully connected layers (fully connected layers), convolutional layers (convolutional layers), pooling layers (pooling layers), and / or other layers / units. Preferably, no nodes are dropped in the input layer(s) or the output layer(s). The application of the dropout technique leads to generative models with different architectures and / or structures, thus to different variants of a generative model.For example, in the case of a neural network, "switching off" or "omitting" a layer can result in that layer appearing and being treated as a layer with a different number of nodes and different connectivity than the previous layer. By switching off various nodes and / or layers, a multitude of variants of the generative model can be created, each differing in its architecture.

[0098] In the present disclosure, dropout is used not (only) during the training of a machine learning model, but when using the trained model to generate synthetic images. Dropout may be used for training the model; however, this is not required.

[0099] A preferred embodiment for creating different variants of the generative model is Monte Carlo Dropout (Gal, Y, Ghahramani, Z., "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning". Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 2016. JMLR: W&CP volume 48, doi: https : / / doi .org / 10.48550 / arXiv.1506.02142) .

[0100] This dropout method is typically used when training a model. A key advantage of the Monte Carlo dropout method is that it can be applied when using the trained model to generate synthetic images. It is possible to train a model (e.g., a neural network) using the regular dropout method and keep dropout enabled while using the trained model. In this way, different variants of a generative model can be created. Crucially, the Monte Carlo dropout method can also be applied to previously trained models. In neural networks that use the Monte Carlo method, dropout nodes and / or dropout layers can be randomly distributed throughout the neural network, e.g., at the beginning (e.g., stochastic masking of inputs), in early layers, or in deep layers.This allows for increased flexibility and variability in the generation of different variants of a generative model. A unified synthetic image can be created from the generated plurality of synthetic images. In other words, the generated plurality of synthetic images can be combined into a unified synthetic image.

[0101] Each received image comprises a plurality of image elements. Each image element of the plurality of image elements represents a sub-area of ​​the examination area of ​​the examination subject. Likewise, each synthetic image of the examination area of ​​the examination subject comprises a plurality of image elements. Typically, each image element of a synthetic image represents a sub-area of ​​the examination area of ​​the examination subject.

[0102] Thus, there are a plurality of subregions of the examination area of ​​the examination object, which are represented by an image element (or multiple image elements) of the at least one received image and by an image element (or multiple image elements) of each synthetic image of the generated plurality of synthetic images. Preferably, all subregions of the examination area are represented by an image element (or multiple image elements) of the at least one received image and by an image element (or multiple image elements) of each synthetic image of the generated plurality of synthetic images.

[0103] Image elements that represent the same sub-area of ​​the examination zone are referred to in this disclosure as corresponding image elements or, for short, corresponding image elements. For example, these may be image elements that have the same coordinates if the image (and / or modification) is a raster graphic.

[0104] If corresponding image elements of the generated plurality of synthetic images are combined, a combined synthetic image can be generated. This can be done, for example, based on the color values ​​of the corresponding image elements of the generated plurality of synthetic images.

[0105] For each tuple of corresponding pixels of the generated synthetic images, the color values ​​are determined. Where k denotes the number of corresponding pixels.

[0106] A mean value (e.g., arithmetic mean, geometric mean, root mean square, or other mean) can be calculated from the color values. The mean of the color values ​​for a tuple of corresponding image elements can be set as the color value of the corresponding image element of the combined synthetic image.

[0107] In the case of multiple color values ​​(e.g., three color values ​​as with RGB color values), an average can be calculated for each color channel. The respective average can then be set as the color value of the corresponding color channel of the corresponding pixel of the combined synthetic image.

[0108] It is also conceivable to determine a maximum or minimum color value for corresponding image elements instead of an average, and to assemble the combined synthetic image from the image elements with the respective maximum or minimum color values. Instead of maxima / minima, other statistical values ​​can also be determined and used to generate the combined synthetic image.

[0109] Other possibilities for combining the individual synthetic images into a unified synthetic image are conceivable. For example, a machine learning model (e.g. an artificial neural network) can be trained to generate the unified synthetic image from the synthetic images of the plurality of synthetic images according to predetermined criteria. If training data is available which, in addition to synthetic images as input data, also includes images that can be used as target data, the machine learning model can be trained in a supervised learning process to combine synthetic images of a plurality of synthetic images. For example, attention mechanisms can be used in which, for example, the individual synthetic images of the plurality of synthetic images are assigned different weights when combined to form the unified synthetic image.

[0110] The combined synthetic image may be output (e.g., displayed on a monitor and / or output to a printer) and / or stored in a data storage device and / or transmitted to a separate computer system, e.g., via a network connection.

[0111] It is also possible to generate more than one combined synthetic image. Different combined synthetic images can also be generated by applying different methods. It is also possible to generate more than one combined synthetic image, e.g., two, three, four, or more than four. For example, a first combined synthetic image can be generated with the respective maximum color values, and a second combined synthetic image with the respective minimum color values. A third combined synthetic image can also be generated with average color values.

[0112] It is also possible that no combined synthetic image is generated and / or output. It is possible that the analysis of corresponding image elements of synthetic images described below reveals that a combined synthetic image has low trustworthiness. For example, a determined trust value that correlates positively with trustworthiness may be lower than a predefined threshold. It may be that the determined trust value and thus the trustworthiness is so low that no diagnosis should be made and / or therapeutic measures initiated based on the combined synthetic image. In such a case, a combined synthetic image may be worthless or even misleading and thus dangerous. The generation and / or output of such a combined synthetic image with low trustworthiness can then be dispensed with.

[0113] In a further step, a confidence value is determined. The plurality of synthetic images are generated to determine a confidence value. The confidence value described in this disclosure can be a confidence value for a single synthetic image generated using a variant of the generative model or for multiple synthetic images generated using different variants of the generative model. The confidence value described in this disclosure can also be a confidence value for a combined synthetic image generated based on the plurality of synthetic images.

[0114] The at least one trust value can be a value that indicates the extent to which a synthetic image (e.g. one of the plurality of generated synthetic images or a synthetic image (overall image, i.e. a combined synthetic image resulting from the combination of the individual images from the plurality of synthetic images) can be trusted.

[0115] The confidence value can correlate positively with the trustworthiness of the synthetic image, i.e., if the confidence value is low, the trustworthiness is also low, and if the confidence value is high, the trustworthiness is also high. However, it is also possible that the confidence value correlates negatively with the trustworthiness; i.e., if the confidence value is low, the trustworthiness is high, and if the confidence value is high, the trustworthiness is low. In the case of a negative correlation, one can also speak of an uncertainty value instead of a confidence value: if the uncertainty value is high, then the synthetic image has a high degree of uncertainty; it is possible that the synthetic image contains one or more artifacts; it is possible that structures and / or morphologies and / or textures in the synthetic image have no equivalent in reality, i.e.that structures and / or morphologies and / or textures in the synthetic image cannot be traced back to real structures and / or real morphologies and / or real textures in the examination area. A low uncertainty value, on the other hand, indicates that the synthetic image has low uncertainty; features in the synthetic image have a counterpart in reality; the synthetic image can be trusted; a medical diagnosis can be made based on the synthetic image and / or medical therapy can be initiated based on the synthetic image.

[0116] A confidence value that correlates positively with trustworthiness can, in principle, also be converted into a confidence value that correlates negatively with trustworthiness (an uncertainty value), for example, by taking the reciprocal. Conversely, a confidence value that correlates negatively with trustworthiness (an uncertainty value) can also be converted into a confidence value that correlates positively with trustworthiness.

[0117] The at least one confidence value can be determined based on corresponding image elements of synthetic images, preferably based on a comparison of corresponding image elements of two or more of the generated plurality of synthetic images. There are different properties of corresponding image elements that can be compared with each other to determine the confidence value.

[0118] One such way to determine the confidence value is based on the color values ​​of corresponding image elements. The explanations below for determining a confidence value based on the color values ​​of corresponding image elements can also be applied to other parameters that could be suitable for determining the confidence value. The color values ​​can be determined for each tuple of corresponding image elements of the generated plurality of synthetic images. Here, k indicates the number of corresponding image elements.

[0119] The more the color values ​​of corresponding image elements in the synthetic images differ from each other, the greater the uncertainty. The greater the differences in the color values ​​of corresponding image elements, the lower the confidence.

[0120] Therefore, the extent to which color values ​​of corresponding image elements differ can be used as a measure of confidence / uncertainty: the more color values ​​of corresponding image elements differ, the lower the confidence, the higher the uncertainty; the less color values ​​of corresponding image elements differ, the lower the uncertainty, the higher the confidence.

[0121] The trustworthiness / uncertainty can thus be determined for each tuple of corresponding image elements of synthetic images of the plurality of synthetic images and then represents the trustworthiness / uncertainty of (i) each individual synthetic image of the plurality of synthetic images, (ii) the total of the synthetic images of the plurality of synthetic images and (iii) the combined synthetic image.

[0122] In other words, for each individual pixel of the combined synthetic image and / or each synthetic image of the plurality of synthetic images, a confidence value can be determined that indicates how much one can trust the color value of the pixel.

[0123] Such a confidence value could, for example, be the spread of the tuple of corresponding image elements. The spread is defined as the difference between the largest and smallest value of a variable. Thus, for each tuple of corresponding image elements, a maximum color value and a minimum color value can be determined, and the difference between the maximum and minimum color values ​​can be calculated. The result is the spread of the color values ​​of the tuple of corresponding image elements, which can be used as a confidence value.

[0124] If there is more than one color value (for example, three color values, as in the case of images whose color values ​​are specified according to the RGB color model), a maximum and a minimum color value can be determined for each color channel and the difference calculated for each color channel. This results in three spreads. For each color channel, the respective spread can be used as a separate confidence value; it is also possible to combine the spreads of the color channels into a single value; it is possible to use the maximum spread as the confidence value; it is possible to use an average value (e.g.It is possible to use the mean (e.g., the arithmetic mean, the geometric mean, the root mean square or any other mean) of the scatter widths as the confidence value; it is possible to use the length of the vector specified by the scatter widths in a three-dimensional space (or a higher-dimensional space when using more than three colour channels) as the confidence value; other possibilities are conceivable.

[0125] A confidence value for a tuple of corresponding image elements can also be the variance and / or standard deviation of the color values ​​of the corresponding image elements. Variance is defined as the mean square deviation of a variable from its expected value; standard deviation is defined as the square root of the variance.

[0126] A confidence value can also be another measure of dispersion, such as the sum of squared deviations, the coefficient of variation, the mean absolute deviation, a quantile distance, an interquantile distance, the mean absolute distance from the median, the median of absolute deviations, and / or the geometric standard deviation. It is also possible for there to be more than one confidence value for a tuple of corresponding image elements.

[0127] The confidence values ​​determined for tuples of corresponding image elements can be output (e.g., displayed on a monitor or printed on a printer), stored in a data storage device, and / or transmitted to a separate computer system, e.g., via a network. The confidence values ​​determined for tuples of corresponding image elements can also be represented graphically.

[0128] In addition to the combined synthetic image, another representation of the examination area can be output (e.g., displayed on a monitor), indicating the trustworthiness of each image element. Such a representation is also referred to in this description as a trust representation. The trust representation preferably has the same dimension and size as the combined synthetic image; each image element of the combined synthetic image is preferably assigned an image element in the trust representation.

[0129] Using such a trust representation, a user (e.g. a doctor) can determine how much they can trust the color value of each individual image element. The trust representation can be fully or partially overlaid with the combined synthetic image. The overlaid representation can be designed so that the user can show or hide it. For example, the user can display the combined synthetic image layer by layer, as is common for computed tomography, magnetic resonance imaging, and other three-dimensional or higher-dimensional representations. For each layer, the corresponding layer of the trust representation can be displayed to check whether image elements in the layer that show structures, morphologies, and / or textures are trustworthy or unreliable.In this way, the user can determine the risk that the structures, morphologies and / or textures are real properties of the area under investigation or are artifacts.

[0130] For example, image elements with a low level of trustworthiness (with a high level of uncertainty) can be displayed brightly and / or in a signal color (e.g., red, orange, or yellow), while image elements with a high level of trustworthiness (with a low level of uncertainty) can be displayed darkly or in a more inconspicuous or calming color (e.g., green or blue). It is also possible to display only those image elements in an overlay whose trust value exceeds or falls below a predefined threshold. If the trust value correlates positively with the trustworthiness, for example, only those image elements of the trust representation whose trust value lies below a predefined threshold can be displayed; in such a case, a user (e.g., a doctor) is only shown those image elements that they should not trust.

[0131] It is also possible to determine confidence values ​​for sub-areas of the combined synthetic image (e.g. layers within the respective synthetic image) and / or for the entire combined synthetic image. Such confidence values ​​for sub-areas or entire images can be determined based on the confidence values ​​of the image elements from which they are composed. To determine a confidence value for a layer, for example, all confidence values ​​of the image elements located in this layer can be taken into account. However, it is also possible to take neighboring image elements (e.g. image elements of the layer above and / or below the layer under consideration) into account. A confidence value for a sub-area or the entire area can be determined, for example, by averaging (e.g. arithmetic mean, geometric mean, root mean square or another mean).It is also possible to determine the maximum value (e.g., for a confidence value that negatively correlates with trustworthiness) or the minimum value (e.g., for a confidence value that negatively correlates with trustworthiness) of the confidence values ​​of the image elements of a sub-area or the entire area and use this as the confidence value of the sub-area or the entire area. Other ways of determining a confidence value for a sub-area or the entire area based on the confidence values ​​of individual image elements are conceivable.

[0132] Such a confidence value for a sub-range or the overall range can also be output (e.g. displayed on a monitor or printed out), stored in a data storage and / or transmitted to a separate computer system. It can also be represented graphically (e.g. in color) as described for the individual confidence values. If a confidence value for a sub-range or the overall range that correlates positively with confidence is lower than a predefined threshold, then it is possible that the corresponding sub-range or overall range should not be trusted. It is possible that such a sub-range or the corresponding overall range is not output at all (e.g. not displayed at all) as described above, or that it is displayed with a warning message that a user should be careful when interpreting the displayed data because the displayed data is unreliable.

[0133] The invention is explained in more detail below with reference to the drawings, without intending to limit the invention to the features and combinations of features shown in the drawings. Statements made with reference to the images shown in the drawings are intended to apply generally, i.e., to other embodiments as well and are not limited to the embodiment shown.

[0134] Fig. 1 shows, by way of example and schematically, the method for generating a plurality of synthetic images and a combined synthetic image, as well as the options for determining a confidence value. The received image (E) represents an examination region of an examination object. By applying different variants of a generative model (GM1, GM2, GM3, GM4 ... GMn), a plurality of synthetic images of the examination region of the examination object (S1, S2, S3, S4 ... Sn) can be generated, as well as a combined synthetic image (S). A confidence value can be determined based on the comparison of corresponding image elements of two or more of the generated plurality of synthetic images ((1), @).

[0135] Fig. 2 shows an example and schematically the combining of synthetic images into a unified synthetic image.

[0136] Three synthetic images SU, S2', and S3' are shown. The three synthetic images can, for example, be enlarged sections of the synthetic images SI, S2, and S3 shown in Fig. 1.

[0137] Each of the three synthetic images SU, S2', and S3' has a number of 10 • 10 = 100 pixels. The pixels are arranged in a grid; each row and column is assigned a number, so that each pixel can be uniquely identified by its coordinates (row value, column value).

[0138] The synthetic images SU, S2' and S3' are binary images, ie each image element is assigned either the color value "white" or the color value "black".

[0139] The combined synthetic image S' is generated by combining the synthetic images SU, S2', and S3'. The combining is performed based on corresponding image elements. Corresponding image elements each represent the same sub-area of ​​the examination area of ​​the object under examination. In the present example, the coordinates of corresponding image elements match. For example, the image element with coordinates (1,1) of the synthetic image SU corresponds to the image element with coordinates (1,1) of the synthetic image S2' and to the image element with coordinates (1,1) of the synthetic image S3'. The image elements with coordinates (1,1) of the synthetic images SU, S2', and S3' form a tuple of corresponding image elements.

[0140] For each tuple of corresponding image elements, the color values ​​are determined and, based on the determined color values, the color value of the corresponding image element of the combined synthetic image is determined.

[0141] In the present example, synthetic images are combined into the unified synthetic image according to the following rule: the color value of each image element of the unified synthetic image S' corresponds to the color value of the majority of the color values ​​of the corresponding image elements of the synthetic representations S1', S2' and S3'.

[0142] For example, the color value for the image element with coordinates (1,1) of the synthetic representation S1' is "white." The color value for the image element with coordinates (1,1) of the corresponding synthetic representation S2' is also "white." The color value for the image element with coordinates (1,1) of the corresponding synthetic representation S3' is also "white." The majority of the corresponding image elements (namely, all image elements) have the color value "white." Accordingly, the color value of the image element with coordinates (1,1) of the combined synthetic image is also set to "white."

[0143] For example, the color value for the image element with coordinates (1,4) of the synthetic representation S1' is "white." The color value for the image element with coordinates (1,4) of the corresponding synthetic representation S2' is "black." The color value for the image element with coordinates (1,4) of the corresponding synthetic representation S3' is "white." The majority of the corresponding image elements have the color value "white." Accordingly, the color value of the image element with coordinates (1,4) of the combined synthetic image is set to "white."

[0144] For example, the color value for the image element with coordinates (7,10) of the synthetic representation S1' is "black." The color value for the image element with coordinates (7,10) of the corresponding synthetic representation S2' is also "black." The color value for the image element with coordinates (7,10) of the corresponding synthetic representation S3' is "white." The majority of the corresponding image elements have the color value "black." Accordingly, the color value of the image element with coordinates (7,10) of the combined synthetic image is set to "black."

[0145] There are numerous other methods by which synthetic images can be combined into a unified synthetic image.

[0146] Fig. 3 shows an exemplary and schematic illustration of the determination of at least one confidence value. The determination of at least one confidence value is based on corresponding image elements of the synthetic images S1', S2', and S3' already shown in Fig. 2.

[0147] A confidence value is determined for each tuple of corresponding image elements. In a first step, the color values ​​of all image elements are determined. In this example, the color "black" is assigned the color value "0" as is generally the case, and the color "white" is assigned the color value "1."

[0148] As a confidence value, the scatter is calculated for each tuple of corresponding image elements of the synthetic images S1', S2' and S3'.

[0149] For example, the color value for the image element with coordinates (1,1) of the synthetic representation S1' is "1" (white). The color value for the image element with coordinates (1,1) of the corresponding synthetic representation S2' is also "1" (white). The color value for the image element with coordinates (1,1) of the corresponding synthetic representation S3' is also "1" (white). The range for the tuple of corresponding image elements is therefore 1 - 1 = 0.

[0150] For example, the color value for the image element with coordinates (1,4) of the synthetic representation S1' is "1" (white). The color value for the image element with coordinates (1,4) of the corresponding synthetic representation S2' is "0" (black). The color value for the image element with coordinates (1,4) of the corresponding synthetic representation S3' is "1" (white). The range for the tuple of corresponding image elements is therefore 1 - 0 = 1.

[0151] For example, the color value for the image element with coordinates (7,10) of the synthetic representation S1' is "0" (black). The color value for the image element with coordinates (7,10) of the corresponding synthetic representation S2' is also "0" (black). The color value for the image element with coordinates (7,10) of the corresponding synthetic representation S3' is "1" (white). The spread for the tuple of corresponding image elements is therefore 1 - 0 = 1. The confidence values ​​are listed in Table CV.

[0152] The trust values ​​determined in this way correlate negatively with trustworthiness.

[0153] A trust representation can be determined based on the trust values. In the example shown in Fig. 3, the color value of each image element in the trust representation SR is set to the corresponding trust value of the tuple of corresponding image elements. For example, the image element with coordinates (1,1) in the trust representation is assigned the color black, while the image elements with coordinates (1,4) and (7,10) are assigned the color white. Based on the trust representation SR, a user (e.g., a doctor) can immediately recognize which image elements are safe (black) and which are unsafe (white). The user should have less trust in areas where there are many white image elements in the trust representation SR.

[0154] Fig. 4 shows an embodiment of the method of the present disclosure in the form of a flowchart.

[0155] The method (100) comprises the steps:

[0156] (110) Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area,

[0157] (120) generating a plurality of synthetic images of the examination area of ​​the examination object on the basis of the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model,

[0158] (130) Determining a measure of dispersion of corresponding image elements of the generated synthetic images, wherein corresponding image elements represent the same sub-area of ​​the examination area,

[0159] (140) Determining at least one confidence value based on the determined dispersion measure,

[0160] (150) Outputting at least one trust value.

[0161] Fig. 5 shows an exemplary and schematic illustration of a computer system according to the present disclosure.

[0162] A "computer system" is an electronic data processing system that processes data using programmable computing instructions. Such a system typically includes a "computer," the unit that includes a processor for performing logical operations, and peripherals.

[0163] In computer technology, "peripherals" refers to all devices connected to a computer that serve to control the computer and / or act as input and output devices. Examples include monitors, printers, scanners, mice, keyboards, drives, cameras, microphones, speakers, etc. Internal connectors and expansion cards are also considered peripherals in computer technology.

[0164] The computer system (10) shown in Fig. 5 comprises a receiving unit (11), a control and computing unit (12) and an output unit (13).

[0165] The control and computing unit (12) serves to control the computer system (10), to coordinate the data flows between the units of the computer system (10) and to carry out calculations.

[0166] The control and computing unit (12) is configured to: cause the receiving unit (11) to receive at least one image of an examination region of an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a sub-region of the examination region, to generate a plurality of synthetic images of the examination region of the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model, to determine a degree of dispersion of corresponding image elements of the generated synthetic images, wherein mutually corresponding image elements represent the same sub-region of the examination region, to determine at least one confidence value based on the determined degree of dispersion, to cause the output unit (13),to output the trust value.,

[0167] Fig. 6 shows a schematic, exemplary embodiment of a further embodiment of the computer system. The computer system (10) comprises a processing unit (21) connected to a memory (22). The processing unit (21) and the memory (22) form a control and computing unit, as shown in Fig. 5.

[0168] The processing unit (21) may comprise one or more processors alone or in combination with one or more memories. The processing unit (21) may be conventional computer hardware capable of processing information such as digital images, computer programs, and / or other digital information. The processing unit (21) typically consists of an arrangement of electronic circuits, some of which may be embodied as an integrated circuit or as multiple interconnected integrated circuits (an integrated circuit is sometimes referred to as a "chip"). The processing unit (21) may be configured to execute computer programs that may be stored in a main memory of the processing unit (21) or in the memory (22) of the same or another computer system.

[0169] The memory (22) may be ordinary computer hardware capable of storing information such as digital images (e.g. representations of the examination area), data, computer programs and / or other digital information either temporarily and / or permanently. The memory (22) may comprise volatile and / or non-volatile memory and may be permanently installed or removable. Examples of suitable memories are RAM (Random Access Memory), ROM (Read-Only Memory), a hard disk, flash memory, a removable computer diskette, an optical disc, magnetic tape or a combination of the above. Optical discs may include read-only compact discs (CD-ROM), read / write compact discs (CD-R / W), DVDs, Blu-ray discs and the like.

[0170] In addition to the memory (22), the processing unit (21) can also be connected to one or more interfaces (11, 12, 31, 32, 33) for displaying, transmitting, and / or receiving information. The interfaces can comprise one or more communication interfaces (11, 32, 33) and / or one or more user interfaces (12, 31). The one or more communication interfaces can be configured to send and / or receive information, e.g., to and / or from an MRI scanner, a CT scanner, an ultrasound camera, other computer systems, networks, data storage devices, or the like. The one or more communication interfaces can be configured to transmit and / or receive information via physical (wired) and / or wireless communication connections.The one or more communication interfaces may include one or more interfaces for connecting to a network, e.g., using technologies such as cellular, Wi-Fi, satellite, cable, DSU, fiber optic, and / or the like. In some examples, the one or more communication interfaces may include one or more short-range communication interfaces configured to connect devices using short-range communication technologies such as NFC, RFID, Bluetooth, Bluetooth LE, ZigBee, infrared (e.g., IrDA), or the like.

[0171] The user interfaces may comprise a display (31). A display (31) may be configured to display information to a user. Suitable examples include a liquid crystal display (LCD), a light-emitting diode display (LED), a plasma display (PDP), or the like. The user input interface(s) (11, 12) may be wired or wireless and may be configured to receive information from a user into the computer system (1), e.g., for processing, storage, and / or display. Suitable examples of user input interfaces include a microphone, an image or video recording device (e.g., a camera), a keyboard or keypad, a joystick, a touch-sensitive surface (separate from or integrated with a touchscreen), or the like.In some examples, the user interfaces may include automatic identification and data capture (AIDC) technology for machine-readable information. This may include barcodes, radio frequency identification (RFID), magnetic stripes, optical character recognition (OCR), integrated circuit cards (ICC), and the like. The user interfaces may further include one or more interfaces for communicating with peripheral devices such as printers and the like.

[0172] One or more computer programs (40) can be stored in the memory (22) and executed by the processing unit (21), which is thereby programmed to perform the functions described in this description. The retrieval, loading, and execution of instructions of the computer program (40) can occur sequentially, such that one instruction is retrieved, loaded, and executed at a time. However, the retrieval, loading, and / or execution can also occur in parallel.

[0173] The computer system of the present disclosure may be embodied as a laptop, notebook, netbook and / or tablet PC, and may also be a component of an MRI scanner, a CT scanner or an ultrasound diagnostic device.

Claims

Patent claims 1. A computer-implemented method for generating at least one confidence value for a synthetic image, comprising the steps: Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area, Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model, Determine at least one trust value, Output at least one trust value.

2. A computer-implemented method according to claim 1, comprising the steps: Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area, Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model, Determining at least one confidence value based on the color values ​​of corresponding image elements of the generated synthetic images, wherein corresponding image elements represent the same sub-area of ​​the examination area, Output at least one trust value.

3. A computer-implemented method according to claim 1, comprising the steps: Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area, Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model, Determining a measure of dispersion of corresponding image elements of the generated synthetic images, whereby corresponding image elements represent the same sub-area of ​​the examination area, Determine at least one confidence value based on the determined dispersion measure, Output at least one trust value.

4. Computer-implemented method according to claim 1 or 3, comprising the steps of: receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area, Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model, Determining a scatter measure based on the color values ​​of corresponding image elements of the generated synthetic images, whereby corresponding image elements represent the same part of the examination area, Determining at least one confidence value based on the determined measure of dispersion, outputting at least one confidence value.

5. A computer-implemented method according to claim 1, comprising the steps: Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area, Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model, Combining the plurality of synthetic images of the examination area of ​​the examination object into an aggregated synthetic image of the examination area of ​​the examination object, Determining a measure of dispersion of corresponding image elements of the received image and the aggregated synthetic image, wherein corresponding image elements represent the same sub-area of ​​the examination area, Determine at least one confidence value based on the determined dispersion measure, Output at least one trust value.

6. Computer-implemented method according to claim 1 or 5, comprising the steps of: receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial area of ​​the examination area, Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, where each synthetic image is created with a different variant of the generative model, Combining the plurality of synthetic images of the examination area of ​​the examination object into an aggregated synthetic image of the examination area of ​​the examination object, Determining a scatter measure based on the color values ​​of corresponding image elements of the received image and the aggregated synthetic image, wherein corresponding image elements represent the same sub-area of ​​the examination area, Determining at least one confidence value based on the determined measure of dispersion, outputting at least one confidence value.

7. Computer-implemented method according to one of claims 1 to 6, wherein the different variants of the generative model for generating a plurality of synthetic images of the examination region of the examination object on the basis of the received image are created by applying a dropout method.

8. The computer-implemented method of claim 7, wherein the dropout method is the Monte Carlo dropout method.

9. Computer system comprehensive - a receiving unit, - a control and computing unit and - an output unit, wherein the control and computing unit is configured, To cause the receiving unit to receive at least one image of an examination region of an examination object, wherein the at least one image comprises a plurality of image elements, wherein each image element of the plurality of image elements represents a partial region of the examination region, to generate a plurality of synthetic images of the examination region of the examination object on the basis of the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model, to determine at least one confidence value, to cause the output unit to output the at least one confidence value.

10. A computer-readable storage medium comprising a computer program which, when loaded into a memory of a computer system, causes the computer system to perform the following steps: Receiving at least one image of an examination area of ​​an examination object, wherein the at least one image comprises a plurality of image elements, each Image element of the plurality of image elements represents a part of the examination area, Generating a plurality of synthetic images of the examination area of ​​the examination object based on the received image by means of a generative model, wherein each synthetic image is created with a different variant of the generative model, Determine at least one trust value, Outputting the at least one confidence value.

11. The computer system or computer-readable storage medium according to claim 7 or 8, wherein the confidence value is determined based on the color values ​​of corresponding image elements of the generated synthetic images, wherein corresponding image elements represent the same sub-area of ​​the examination area.

Images