Patent Description:
An X-ray system, e.g. a radiography system or a fluoroscopy system, comprises an X-ray source and an X-ray detector. The examination object, in particular a patient, is arranged between the X-ray source and the X-ray detector so that an X-ray image of an examination region can be acquired. The X-ray beam of the X-ray source is limited by a collimator. The collimator defines a collimation region. A typical shape of the collimation region is a rectangular shape delimited on four sides by collimator blades. Within the collimation region, X-rays penetrate the examination object and the X-rays, which penetrated the object, are detected by the X-ray detector.

One known technique to define collimator borders or the collimation region is to use an RGB camera together with a depth camera, or a 3D camera. The camera in the acquisition room would first capture an image from the patient or examination object. A couple of key elements can be identified on the image, e.g. by using an AI model. The key elements can be used to define the collimator borders or the collimation region.

Auto-collimation techniques are a necessary part in acquiring X-ray images. Collimators are devices used to restrict and narrow beams which define the border of the X-ray image. Well-chosen collimator settings or a well-chosen collimation region are one of the crucial aspects of improving the radiographic imaging technique. The well-chosen collimation region prevents unnecessary exposure outside the region of interest. Further, the well-chosen collimation region improves the image quality by producing less scatter radiation, e.g. generated outside the region of interest. The region of interest, e.g. the lung, lies within an examination region.

However, it might be possible that some necessary parts of the organ to be imaged is not shown in its entirety in the X-ray image due to a faulty setting of the collimation region. the collimation region is not large enough to image the organ in total.

<CIT> discloses a method for anomaly detection. It is disclosed that a surface image of a patient is acquired and based on that a topogram representing an interior anatomy of the patient is determined. Additionally, an X-ray image of the patient is acquired and compared to the topogram. Based on differences between the topogram and the X-ray image, anatomical anomalies can be determined.

It is an object of the invention to provide a computer-implemented method for providing a complete set of second key elements in an X-ray image, a computer-implemented method for providing a trained function, a providing system, a computer program product, a computer-readable medium, a training system, a computer program product, a computer-readable medium, and an X-ray system, which reduce the need for retakes of X-ray images.

The object of the invention is solved by a computer-implemented method for providing a complete set of second key elements in an X-ray image according to claim <NUM>, a computer-implemented method for providing a trained function according to claim <NUM>, a providing system according to claim <NUM>, a computer program product according to claim <NUM>, a computer-readable medium according to claim <NUM>, a training system according to claim <NUM>, a computer program product according to claim <NUM>, a computer-readable medium according to claim <NUM>, and an X-ray system according to claim <NUM>.

In the following the solution according to the invention is described with respect to the claimed providing systems as well as with respect to the claimed methods. Features, advantages or alternative embodiments herein can be assigned to the other claimed objects and vice versa. In other words, claims for the providing systems can be improved with features described or claimed in the context of the methods. In this case, the functional features of the method are embodied by objective units of the providing system.

Furthermore, in the following the solution according to the invention is described with respect to methods and systems for providing complete set of second key elements in an X-ray image as well as with respect to methods and systems for the training of the trained function. Features, advantages or alternative embodiments herein can be assigned to the other claimed objects and vice versa. In other words, claims for methods and systems for training of the trained function can be improved with features described or claimed in context of the methods and systems for providing complete set of second key elements in an X-ray image, and vice versa.

In particular, the trained function of the methods and systems for providing complete set of second key elements in an X-ray image can be adapted by the methods and systems for training of the trained function. Furthermore, the input data can comprise advantageous features and embodiments of the training input data, and vice versa. Furthermore, the output data can comprise advantageous features and embodiments of the output training data, and vice versa.

The invention relates to a computer-implemented method for a complete set of second key elements in an X-ray image, comprising:.

The invention can be applied to various X-ray systems, especially radiography systems, mammography systems, or fluoroscopy systems. The X-ray system can comprise a camera, in particular a 3D camera, an X-ray source, and an X-ray detector. In this invention, the example of a chest X-ray image is considered to explain the invention. The invention can be applied to other anatomical regions of the body. The key elements, in particular number and location, can differ from body region to body region.

A key element can describe a position or a coordinate of a relevant anatomical feature, e.g. lung border, bone, joint etc. The key element can be a point, a line, an area, or a volume. In a preferred embodiment, the key element can be a point, also called a key point. In another embodiment, the key element can be a line, e.g. describing the border of a bone. The key element can comprise a shape, e.g. point, line, area, or volume, and a position. The position can be defined for example by the center of the shape. The key element and, preferably, a set of key elements can describe an anatomical feature and/or its position.

The first input data is an optical image of an examination region. The term "optical" can describe that optical means were used for the acquisition of the optical image. A 2D or 3D camera can be used to acquire the optical image. The optical means can use a wavelength from <NUM> to <NUM>. The optical image can comprise color information, in particular RGB information, of the examination region. The optical image can comprise depth information of the examination region. In a preferred embodiment, the optical image comprises RGB information and depth information. In another embodiment, the optical image comprises RGB information only or depth information only. In a use case in the field of mammography, the optical image can comprise depth information only. A depth camera can be used.

A first trained function, in particular a deep learning algorithm, is applied to the first input data, wherein first output data is generated. The first output data comprises detected first key elements in the optical image. A first collimation region is determined based on the first key elements. The first collimation region can be calculated or computed based on the first key elements. The first key elements are detected in the optical image. Key elements can be defined as characteristic features within the examination region. Image recognition algorithms, pattern recognition algorithms or other image processing algorithms can be used to detect the key elements. Especially, the first trained function can be based on a machine learning algorithm or deep learning algorithm.

The second input data is an X-ray image of an examination region, acquired using the first collimation region. A second trained function is applied to the second input data wherein second output data is generated. The second trained function can be based on a machine learning algorithm or a deep learning algorithm. The second output data comprises detected second key elements in the X-ray image. A set of key elements can comprise all detected second key elements. The X-ray image can correspond to a first collimation region.

In a next step, a set of second key elements is checked for completeness. A certain number of key elements is required for setting the collimation region. For example, four key elements can be used to set a rectangular shaped collimation region.

In case of an incomplete set of second key elements, third input data is received. The third input data comprises an x-ray image of an examination region acquired using the first collimation region, and the second key elements. A third trained function is applied to the third input data wherein third output data is generated. The third trained function can be based on a machine learning algorithm or a deep learning algorithm. The third output data comprises at least one estimated third key element to complete the set of second key elements.

Final output data is provided which comprises a complete set of second key elements. The complete set of second key elements can comprise the at least one estimated third key element.

According to an aspect of the invention, the complete set of second key elements is transferred to the optical image. A second key element can be transferred via image registration techniques to the optical image. The key element is chosen in such a way that the key element can be detected in the optical image as well as in the X-ray image. A mapping of a second key element in the X-ray image and a first key element in the optical image can be carried out.

According to an aspect of the invention, a second collimation region is determined based on the transferred complete set of second key elements. The second collimation region can be used for the re-take of the X-ray image.

According to an aspect of the invention, a second X-ray image of the examination region is acquired using the second collimation region. The second X-ray image can correspond to a second collimation region.

The general concept of the invention is based on using an X-ray image to create a feedback loop to check whether the collimation region was correct. The first collimation region with its borders is defined by the optical image, in particular an RGB image in combination with a depth image. The first collimation region is used to acquire the (first) X-ray image, e.g. of the chest.

This X-ray image will be processed by the second trained function, in particular embodied as an AI model, to detect key elements which are relevant to those detected in the optical image by the first trained function which can be called a RGB/depth key elements model. In case of at least one missing key element in the X-ray image, the third trained function will be used to estimate the position of the at least one missing key element. It is clear that the at least one missing key element should be located somewhere outside of the image. The third trained function can estimate or predict the location of the at least one missing key element, also called the at least one third key element.

After estimating the at least one third key element, the at least one third key element and the detected second key elements form a complete set of second key elements. The complete set of second key elements in coordinates of the X-ray image can be mapped, e.g. by using an image registration technique, to the optical image. In doing so, the location of the at least one third key element can be determined in the optical image. In a subsequent step, a second collimation region can be computed or calculated based on the complete set of second key elements, which are now available in coordinates of the optical image, based on the optical image.

A second X-ray image can be acquired using the second collimation region. The second X-ray image can be used again as second input data in order to determine second key elements in the second X-ray image by applying the second trained function. If all second key elements are found in the second X-ray image, the second X-ray image can be used for further steps, e.g. diagnosis by a radiologist. If at least one second key element is missing again, the procedure of estimating the at least one third key element is repeated, and a third collimation region can be calculated. The method can be used in an iterative way in order to get an X-ray image with a complete set of second key elements. An X-ray image with a complete set of second key elements is considered as complete X-ray image of the examination region or region of interest. An X-ray image with an incomplete set of second key elements is considered as incomplete X-ray image of the examination region or region of interest. The incomplete X-ray image can be considered as a cropped X-ray image in which details of the region of interest are missing.

According to an aspect of the invention, the choice of the key elements as such should consider that the key elements are definable both in the optical image and the X-ray image.

The key element can be defined at the location of a body part, e.g. a shoulder, or a structural feature of the body, e.g. a bone. In a preferred embodiment, the key elements can be defined by the user. In another embodiment, a set of key elements can be defined for a region of interest or an examination region. As an example, for a chest X-ray image four key elements can be defined which can be located at four corners of a rectangle. The collimation region can be determined based on the first key elements. The first key elements can be located within the collimation region, e.g. with a certain distance to the border of the collimation region.

The invention allows the use of the X-ray image to correct a possible collimation error which can be caused by determining the collimation region based on the optical image, especially using the first trained function. Furthermore, X-ray images can be checked repeatedly to make sure all necessary key elements are available. In case of missing key elements, the procedure would be repeated to define a new collimation region.

The invention further relates to a computer-implemented method for providing a third trained function, comprising:.

The input training data comprises an X-ray image acquired using the first collimation region. The X-ray image can correspond to the first collimation region. The output training data is related to the input training data, wherein the output training data comprises the at least one third key element. In a preferred embodiment, the complete X-ray image can form the basis for the input training data and the output training data. A second collimation region can be determined based on the complete X-ray image. The border or outline of the complete X-ray image can be used for defining the second collimation region and/or the at least one third key element. A collimation region in general can be defined by an outline or a border of an area. For the input training data, the X-ray image is used. This X-ray image can be a cropped version of the complete X-ray image, e.g. the complete X-ray image can be cropped or cut off at one side. The border or outline of the X-ray image can be used for defining the collimation region. Therefore, the input training data and the output training data are related and a ground truth is available for the training.

In a preferred embodiment, the third trained function can be based on a deep learning method. In general, a trained function mimics cognitive functions that humans associate with other human minds. In particular, by training based on training data the trained function is able to adapt to new circumstances and to detect and extrapolate patterns.

In general, parameters of a trained function can be adapted by means of training. In particular, supervised training, semi-supervised training, unsupervised training, reinforcement learning and/or active learning can be used. Furthermore, representation learning (an alternative term is "feature learning") can be used. In particular, the parameters of the trained functions can be adapted iteratively by several steps of training.

In particular, a trained function can comprise a neural network, a support vector machine, a decision tree and/or a Bayesian network, and/or the trained function can be based on k-means clustering, Q-learning, genetic algorithms and/or association rules. In particular, a neural network can be a deep neural network, a convolutional neural network or a convolutional deep neural network. Furthermore, a neural network can be an adversarial network, a deep adversarial network and/or a generative adversarial network.

According to an aspect of the invention the at least one estimated third key element is deduced from a complete X-ray image. According to an aspect of the invention, the input training data is based on a cropped X-ray image of the complete X-ray image.

The invention further relates to a method for providing a complete set of second key elements in an X-ray image, wherein the trained function was provided by the method for providing the trained function.

The invention further relates to a providing system, comprising.

The invention further relates to a computer program product comprising instructions which, when the program is executed by a providing system, cause the providing system to carry out the method for providing a complete set of second key elements in an X-ray image.

The invention further relates to a computer-readable medium comprising instructions which, when executed by a providing system, cause the providing system to carry out the method for providing a complete set of second key elements in an X-ray image.

The invention further relates to a training system, comprising:.

The invention further relates to a computer program product comprising instructions which, when the program is executed by a training system, cause the training system to carry out the method of providing a trained function.

The invention further relates to a computer-readable medium comprising instructions which, when executed by a training system, cause the training system to carry out the method of providing a trained function.

The invention further relates to an X-ray system comprising the providing system.

Examples of embodiments of the invention are explained in more detail below by means of drawings.

<FIG> displays an embodiment of method <NUM> for providing a complete set of second key elements in an X-ray image according to the invention in a first embodiment.

The computer-implemented method <NUM> for providing a complete set of second key elements in an X-ray image, comprises the following steps, preferably in the following order:.

In a preferred embodiment, the complete set of second key elements is transferred to the optical image. A second collimation region is determined based on the transferred complete set of second key elements. A second X-ray image of the examination region is acquired using the second collimation region. A second X-ray image can correspond to the second collimation region.

<FIG> displays an embodiment of the method <NUM> for providing a complete set of second key elements in an X-ray image according to the invention in a second embodiment. In step <NUM>, an optical image <NUM> of the examination region of the patient <NUM> is acquired by a 3D camera <NUM>. A first trained function <NUM> determines first key elements <NUM> in the optical image. Based on the first key elements <NUM> a collimation region <NUM> is calculated in step <NUM>.

The collimation region <NUM> is used to acquire an X-ray image <NUM> in step <NUM>. The X-ray system comprises an X-ray source <NUM> and an X-ray detector <NUM>. The patient <NUM> is located between the X-ray source <NUM> and the X-ray detector <NUM>. A second trained function <NUM> is applied to the X-ray image and second key elements <NUM> are determined.

In step <NUM>, the second key elements <NUM> are checked, if they form a complete set of second key elements. If at least one second key element is missing, a third trained function <NUM> is applied to the X-ray image <NUM> to estimate at least one third key element <NUM> to complete the set of second key elements. A second collimation region <NUM> is determined based on the complete set of second key elements including the at least one third key element <NUM>. A second X-ray image is acquired, and the process starts again.

If a complete set of second key elements <NUM> is found in X-ray image <NUM>, then the X-ray image <NUM> is complete.

<FIG> further displays an embodiment of an X-ray system <NUM> according to the invention. The X-ray system <NUM> comprises the providing system <NUM>. The providing <NUM> system, comprises:.

The X-ray system can comprise a training system <NUM>. The training system comprises:.

<FIG> displays an embodiment of method for mapping second key elements in an X-ray image to an optical image according to the invention. As input, the X-ray image <NUM> with the second key elements <NUM> and the at least one third key element <NUM>, and the optical image <NUM> with the first key elements <NUM> are used. In step <NUM>, the X-ray image <NUM> is mapped to the optical image <NUM> taking in consideration the first key elements <NUM>, the second key elements <NUM> and the at least one third key element <NUM>. The at least one third key element <NUM> is mapped to the optical image <NUM>. In a step <NUM>, a second collimation region <NUM> is calculated.

<FIG> displays an embodiment of a computer-implemented method <NUM> for providing a third trained function, comprising:.

<FIG> displays example X-ray images <NUM>, <NUM> for use in the method for providing a trained function according to the invention. The at least one estimated third key element is deduced from a complete X-ray image <NUM>. The second collimation region is determined by the outline of the complete X-ray image <NUM>. The input training data is based on a cropped X-ray image <NUM> of the complete X-ray image <NUM>. The collimation region is determined by the outline of the cropped X-ray image <NUM>.

The example of chest x-ray image is considered to explain the invention. The invention can be applied to other body parts as well. The cropped X-ray image <NUM> is an example of a bad collimation region for acquiring a chest X-ray image, as it can be seen lower part of left lung is missing in the cropped X-ray image <NUM> due to incorrect collimator parameters. The complete X-ray image <NUM> shows the whole lung.

<FIG> displays an embodiment of an artificial neural network <NUM>. Alternative terms for "artificial neural network" are "neural network", "artificial neural net" or "neural net".

The artificial neural network <NUM> comprises nodes <NUM>,. , <NUM> and edges <NUM>,. , <NUM>, wherein each edge <NUM>,. , <NUM> is a directed connection from a first node <NUM>,. , <NUM> to a second node <NUM>,. In general, the first node <NUM>,. , <NUM> and the second node <NUM>,. , <NUM> are different nodes <NUM>,. , <NUM>, it is also possible that the first node <NUM>,. , <NUM> and the second node <NUM>,. , <NUM> are identical. For example, in <FIG> the edge <NUM> is a directed connection from the node <NUM> to the node <NUM>, and the edge <NUM> is a directed connection from the node <NUM> to the node <NUM>. An edge <NUM>,. , <NUM> from a first node <NUM>,. , <NUM> to a second node <NUM>,. , <NUM> is also denoted as "ingoing edge" for the second node <NUM>,. , <NUM> and as "outgoing edge" for the first node <NUM>,.

In this embodiment, the nodes <NUM>,. , <NUM> of the artificial neural network <NUM> can be arranged in layers <NUM>,. , <NUM>, wherein the layers can comprise an intrinsic order introduced by the edges <NUM>,. , <NUM> between the nodes <NUM>,. In particular, edges <NUM>,. , <NUM> can exist only between neighboring layers of nodes. In the displayed embodiment, there is an input layer <NUM> comprising only nodes <NUM>,. , <NUM> without an incoming edge, an output layer <NUM> comprising only nodes <NUM>, <NUM> without outgoing edges, and hidden layers <NUM>, <NUM> in-between the input layer <NUM> and the output layer <NUM>. In general, the number of hidden layers <NUM>, <NUM> can be chosen arbitrarily. The number of nodes <NUM>,. , <NUM> within the input layer <NUM> usually relates to the number of input values of the neural network, and the number of nodes <NUM>, <NUM> within the output layer <NUM> usually relates to the number of output values of the neural network.

In particular, a (real) number can be assigned as a value to every node <NUM>,. , <NUM> of the neural network <NUM>. Here, x(n)i denotes the value of the i-th node <NUM>,. , <NUM> of the n-th layer <NUM>,. The values of the nodes <NUM>,. , <NUM> of the input layer <NUM> are equivalent to the input values of the neural network <NUM>, the values of the nodes <NUM>, <NUM> of the output layer <NUM> are equivalent to the output value of the neural network <NUM>. Furthermore, each edge <NUM>,. , <NUM> can comprise a weight being a real number, in particular, the weight is a real number within the interval [-<NUM>, <NUM>] or within the interval [<NUM>, <NUM>]. Here, w(m,n)i,j denotes the weight of the edge between the i-th node <NUM>,. , <NUM> of the m-th layer <NUM>,. , <NUM> and the j-th node <NUM>,. , <NUM> of the n-th layer <NUM>,. Furthermore, the abbreviation w(n)i,j is defined for the weight w(n,n+<NUM>)i,j.

In particular, to calculate the output values of the neural network <NUM>, the input values are propagated through the neural network. In particular, the values of the nodes <NUM>,. , <NUM> of the (n+<NUM>)-th layer <NUM>,. , <NUM> can be calculated based on the values of the nodes <NUM>,. , <NUM> of the n-th layer <NUM>,. , <NUM> by <MAT>.

Herein, the function f is a transfer function (another term is "activation function"). Known transfer functions are step functions, sigmoid function (e.g. the logistic function, the generalized logistic function, the hyperbolic tangent, the Arctangent function, the error function, the smoothstep function) or rectifier functions. The transfer function is mainly used for normalization purposes.

In particular, the values are propagated layer-wise through the neural network, wherein values of the input layer <NUM> are given by the input of the neural network <NUM>, wherein values of the first hidden layer <NUM> can be calculated based on the values of the input layer <NUM> of the neural network, wherein values of the second hidden layer <NUM> can be calculated based in the values of the first hidden layer <NUM>, etc..

In order to set the values w(m,n)i,j for the edges, the neural network <NUM> has to be trained using training data. In particular, training data comprises training input data and training output data (denoted as ti). For a training step, the neural network <NUM> is applied to the training input data to generate calculated output data. In particular, the training data and the calculated output data comprise a number of values, said number being equal with the number of nodes of the output layer.

In particular, a comparison between the calculated output data and the training data is used to recursively adapt the weights within the neural network <NUM> (backpropagation algorithm). In particular, the weights are changed according to <MAT> wherein γ is a learning rate, and the numbers δ(n)j can be recursively calculated as <MAT> based on δ(n+<NUM>)j, if the (n+<NUM>)-th layer is not the output layer, and <MAT> if the (n+<NUM>)-th layer is the output layer <NUM>, wherein f' is the first derivative of the activation function, and y(n+<NUM>)j is the comparison training value for the j-th node of the output layer <NUM>.

<FIG> displays an embodiment of a convolutional neural network <NUM>. In the displayed embodiment, the convolutional neural network comprises <NUM> an input layer <NUM>, a convolutional layer <NUM>, a pooling layer <NUM>, a fully connected layer <NUM> and an output layer <NUM>. Alternatively, the convolutional neural network <NUM> can comprise several convolutional layers <NUM>, several pooling layers <NUM> and several fully connected layers <NUM>, as well as other types of layers. The order of the layers can be chosen arbitrarily, usually fully connected layers <NUM> are used as the last layers before the output layer <NUM>.

In particular, within a convolutional neural network <NUM> the nodes <NUM>,. , <NUM> of one layer <NUM>,. , <NUM> can be considered to be arranged as a d-dimensional matrix or as a d-dimensional image. In particular, in the two-dimensional case the value of the node <NUM>,. , <NUM> indexed with i and j in the n-th layer <NUM>,. , <NUM> can be denoted as x(n)[i,j]. However, the arrangement of the nodes <NUM>,. , <NUM> of one layer <NUM>,. , <NUM> does not have an effect on the calculations executed within the convolutional neural network <NUM> as such, since these are given solely by the structure and the weights of the edges.

In particular, a convolutional layer <NUM> is characterized by the structure and the weights of the incoming edges forming a convolution operation based on a certain number of kernels. In particular, the structure and the weights of the incoming edges are chosen such that the values x(n)k of the nodes <NUM> of the convolutional layer <NUM> are calculated as a convolution x(n)k = Kk * x(n-<NUM>) based on the values x(n-<NUM>) of the nodes <NUM> of the preceding layer <NUM>, where the convolution * is defined in the two-dimensional case as <MAT>.

Here the k-th kernel Kk is a d-dimensional matrix (in this embodiment a two-dimensional matrix), which is usually small compared to the number of nodes <NUM>,. , <NUM> (e.g. a 3x3 matrix, or a 5x5 matrix). In particular, this implies that the weights of the incoming edges are not independent, but chosen such that they produce said convolution equation. In particular, for a kernel being a 3x3 matrix, there are only <NUM> independent weights (each entry of the kernel matrix corresponding to one independent weight), irrespectively of the number of nodes <NUM>,. , <NUM> in the respective layer <NUM>,. In particular, for a convolutional layer <NUM> the number of nodes <NUM> in the convolutional layer is equivalent to the number of nodes <NUM> in the preceding layer <NUM> multiplied with the number of kernels.

If the nodes <NUM> of the preceding layer <NUM> are arranged as a d-dimensional matrix, using a plurality of kernels can be interpreted as adding a further dimension (denoted as "depth" dimension), so that the nodes <NUM> of the convolutional layer <NUM> are arranged as a (d+<NUM>)-dimensional matrix. If the nodes <NUM> of the preceding layer <NUM> are already arranged as a (d+<NUM>)-dimensional matrix comprising a depth dimension, using a plurality of kernels can be interpreted as expanding along the depth dimension, so that the nodes <NUM> of the convolutional layer <NUM> are arranged also as a (d+<NUM>)-dimensional matrix, wherein the size of the (d+<NUM>)-dimensional matrix with respect to the depth dimension is by a factor of the number of kernels larger than in the preceding layer <NUM>.

The advantage of using convolutional layers <NUM> is that spatially local correlation of the input data can exploited by enforcing a local connectivity pattern between nodes of adjacent layers, in particular by each node being connected to only a small region of the nodes of the preceding layer.

In the displayed embodiment, the input layer <NUM> comprises <NUM> nodes <NUM>, arranged as a two-dimensional 6x6 matrix. The convolutional layer <NUM> comprises <NUM> nodes <NUM>, arranged as two two-dimensional 6x6 matrices, each of the two matrices being the result of a convolution of the values of the input layer with a kernel. Equivalently, the nodes <NUM> of the convolutional layer <NUM> can be interpreted as arranges as a three-dimensional 6x6x2 matrix, wherein the last dimension is the depth dimension.

A pooling layer <NUM> can be characterized by the structure and the weights of the incoming edges and the activation function of its nodes <NUM> forming a pooling operation based on a nonlinear pooling function f. For example, in the two dimensional case the values x(n) of the nodes <NUM> of the pooling layer <NUM> can be calculated based on the values x(n-<NUM>) of the nodes <NUM> of the preceding layer <NUM> as <MAT>.

In other words, by using a pooling layer <NUM> the number of nodes <NUM>, <NUM> can be reduced, by replacing a number d<NUM>·d<NUM> of neighboring nodes <NUM> in the preceding layer <NUM> with a single node <NUM> being calculated as a function of the values of said number of neighboring nodes in the pooling layer. In particular, the pooling function f can be the max-function, the average or the L2-Norm. In particular, for a pooling layer <NUM> the weights of the incoming edges are fixed and are not modified by training.

The advantage of using a pooling layer <NUM> is that the number of nodes <NUM>, <NUM> and the number of parameters is reduced. This leads to the amount of computation in the network being reduced and to a control of overfitting.

In the displayed embodiment, the pooling layer <NUM> is a max-pooling, replacing four neighboring nodes with only one node, the value being the maximum of the values of the four neighboring nodes. The max-pooling is applied to each d-dimensional matrix of the previous layer; in this embodiment, the max-pooling is applied to each of the two two-dimensional matrices, reducing the number of nodes from <NUM> to <NUM>.

A fully-connected layer <NUM> can be characterized by the fact that a majority, in particular, all edges between nodes <NUM> of the previous layer <NUM> and the nodes <NUM> of the fully-connected layer <NUM> are present, and wherein the weight of each of the edges can be adjusted individually.

In this embodiment, the nodes <NUM> of the preceding layer <NUM> of the fully-connected layer <NUM> are displayed both as two-dimensional matrices, and additionally as non-related nodes (indicated as a line of nodes, wherein the number of nodes was reduced for a better presentability). In this embodiment, the number of nodes <NUM> in the fully connected layer <NUM> is equal to the number of nodes <NUM> in the preceding layer <NUM>. Alternatively, the number of nodes <NUM>, <NUM> can differ.

Furthermore, in this embodiment the values of the nodes <NUM> of the output layer <NUM> are determined by applying the Softmax function onto the values of the nodes <NUM> of the preceding layer <NUM>. By applying the Softmax function, the sum of the values of all nodes <NUM> of the output layer is <NUM>, and all values of all nodes <NUM> of the output layer are real numbers between <NUM> and <NUM>. In particular, if using the convolutional neural network <NUM> for categorizing input data, the values of the output layer can be interpreted as the probability of the input data falling into one of the different categories.

A convolutional neural network <NUM> can also comprise a ReLU (acronym for "rectified linear units") layer. In particular, the number of nodes and the structure of the nodes contained in a ReLU layer is equivalent to the number of nodes and the structure of the nodes contained in the preceding layer. In particular, the value of each node in the ReLU layer is calculated by applying a rectifying function to the value of the corresponding node of the preceding layer. Examples for rectifying functions are f(x) = max(<NUM>,x), the tangent hyperbolics function or the sigmoid function.

Claim 1:
A computer-implemented method (<NUM>) for providing a complete set of second key elements in an X-ray image, comprising:
- receiving (<NUM>) first input data, wherein the first input data is an optical image of an examination region,
- applying (<NUM>) a first trained function to the first input data, wherein first output data is generated, wherein the first output data comprises detected first key elements, and a first collimation region is determined based on the first key elements,
- receiving (<NUM>) second input data, wherein the second input data is an X-ray image of an examination region, acquired using the first collimation region,
- applying (<NUM>) a second trained function to the second input data, wherein second output data is generated, wherein the second output data comprises detected second key elements,
- checking (<NUM>) a set of second key elements for completeness, in case of an incomplete set of second key elements:
- receiving (<NUM>) third input data, wherein the third input data comprises an x-ray image of an examination region acquired using the first collimation region, and the second key elements,
- applying (<NUM>) a third trained function to the third input data, wherein third output data is generated, wherein the third output data comprises at least one estimated third key element to complete the set of second key elements,
- providing (<NUM>) a second collimation region based on the complete set of second key elements to acquire a further x-ray image.