Patent Description:
Image quality assessment plays an important role in machine learning, medical imaging, computer vision, image compression, etc. Although a vast number of techniques exists to assess the image quality of 2D (two-dimensional) medical images, there has been little research performed for assessing the image quality of 3D (three-dimensional) medical images in a systematic and clinically meaningful way. The assessment of 3D medical images remains a subjective, mostly manual, and computationally expensive process. <NPL>, a deep-learning-based super resolution framework with an emphasis on data construction to achieve better performance on real low-resolution magnetic resonance images. <NPL>, a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE), uses scene statistics of locally normalized luminance coefficients to quantify possible losses of "naturalness" in the image due to the presence of distortions, thereby leading to a holistic measure of quality.

In accordance with one or more embodiments, systems and methods for automatically determining an image quality assessment of a rendered medical image are provided. A rendered medical image is received. One or more measures of interest are extracted from the rendered medical image. An image quality assessment of the rendered medical image is determined using a machine learning based image quality assessment network based on the one or more measures of interest. The image quality assessment of the rendered medical image is output.

An input depth map of the rendered medical image is received. An estimated depth map is generated from the rendered medical image. The input depth map is compared with the estimated depth map. The image quality assessment of the rendered medical image is further determined based on results of the comparison.

In one embodiment, the image quality assessment is compared with a threshold. In response to determining that the image quality assessment does not satisfy the threshold, imaging rendering parameters from which the rendered medical image was rendered are modified. In response to determining that the image quality assessment does not satisfy the threshold, an updated rendered medical image is generated based on the modified imaging rendering parameters and the extracting, the determining, and the modifying are repeated using the updated rendered medical image as the rendered medical image until the image quality assessment satisfies a threshold. In response to determining that the image quality assessment satisfies the threshold, the machine learning based image quality assessment network is retrained based on the rendered medical image and the image quality assessment and the imaging rendering parameters are stored in memory.

In one embodiment, the one or more measures of interest comprise natural scene statistics. In embodiments, an apparatus comprises means for carrying out or executing the steps according to the claimed method, e.g. in a computer-implemented fashion. The means may be one or more processors configured to perform operations comprising the steps of the claimed method.

The present invention generally relates to methods and systems for image quality assessment for refinement of imaging rendering parameters for rendering medical images. Embodiments of the present invention are described herein to give a visual understanding of such methods and systems. A digital image (such as, e.g., a medical image) is often composed of digital representations of one or more objects (or shapes). The digital representation of an object is often described herein in terms of identifying and manipulating the objects. Such manipulations are virtual manipulations accomplished in the memory or other circuitry/hardware of a computer system. Accordingly, is to be understood that embodiments of the present invention may be performed within a computer system using data stored within the computer system.

Embodiments described herein provide for automatic image quality assessment of rendered medical images using a machine learning based image quality assessment network. The image quality assessment may be performed based on measurements of depth perception of the rendered medical images and/or measures of interest extracted from the rendered medical images. The image quality assessment network automatically evaluates the image quality of the rendered medical images not only for assessing the perceptual photorealistic appearance of the rendered medical images, but also for assessing the clinically meaningful features represented in the rendered medical images. Advantageously, such image quality assessment in accordance with embodiments described herein may be utilized for automatic or semi-automatic refinement of the imaging rendering parameters and generation of clinical presets of the imaging rendering parameters for generating rendered medical images for various clinical use cases and imaging modalities.

<FIG> shows a method <NUM> for image quality assessment of a rendered medical image, in accordance with one or more embodiments. The steps of method <NUM> may be performed by one or more suitable computing devices, such as, e.g., computer <NUM> of <FIG>. <FIG> shows a workflow <NUM> for image quality assessment of a rendered medical image, in accordance with one or more embodiments. <FIG> and <FIG> will be described together.

At step <NUM> of <FIG>, a rendered medical image is received. In one example, as shown in <FIG>, the rendered medical image is rendered medical image <NUM> in workflow <NUM>. Rendered medical image <NUM> may have been generated by renderer <NUM> from 3D (three-dimensional) volumetric data <NUM> based on imaging rendering parameters <NUM> by applying any suitable image rendering algorithm. Imaging rendering parameters <NUM> may initially be predefined imaging rendering parameters or user defined imaging rendering parameters. Exemplary imaging rendering parameters <NUM> include different transfer functions and window/level values (that map intensity and opacity values of the volumetric data into presets for different tissue type), exposure of light, aperture of the virtual camera, etc. The image rendering algorithm may be, for example, a standard ray casting algorithm with simple shading (e.g., Phong shading), a ray casting algorithm with real-time approximate global illumination, a cinematic renderer applying a path tracer (e.g., Monte Carlo path tracer) that generates images by simulating the physics of light along light paths for each pixel, or any other suitable image rendering algorithm. The rendered medical image <NUM> may be rendered using any other suitable approach.

The rendered medical image <NUM> may be of any suitable modality, such as, e.g., CT (computed tomography), dynaCT, MRI (magnetic resonance imaging), ultrasound, x-ray, or any other medical imaging modality or combinations of medical imaging modalities. The rendered medical image <NUM> may be a 2D (two-dimensional) image that can represent 2D instances and/or one or more 3D volumes, and may comprise a single rendered medical image or a plurality of rendered medical images. The rendered medical image <NUM> may be received by loading a previously generated rendered image from a storage or memory of a computer system or receiving a rendered medical image that has been transmitted from a remote computer system.

At step <NUM> of <FIG>, an input depth map of the rendered medical image is received. For example, as shown in <FIG>, the input depth map is input depth map <NUM> in workflow <NUM>. A depth map is a pixelwise heat map having pixels corresponding to pixels of, e.g., the rendered medical image, where each respective pixel of the depth map has an intensity value ranging from, e.g., <NUM> to <NUM> representing a depth depicted by that respective pixel. In one embodiment, the input depth map <NUM> may have been generated by the renderer <NUM> as part of the rendering of the rendered medical image <NUM> from the 3D volumetric data <NUM> based on the imaging rendering parameters <NUM>. The input depth map <NUM> represents the depth that the rendered medical image <NUM> should depict.

At step <NUM> of <FIG>, an estimated depth map is generated from the rendered medical image. For example, as shown in <FIG>, the estimated depth map may be estimated depth map <NUM> of workflow <NUM>. In one embodiment, the estimated depth map <NUM> is generated using a machine learning depth estimation network (e.g., a convolutional neural network) trained using residual learning. The estimated depth map <NUM> may be generated according to any other suitable approach. The estimated depth map <NUM> represents the depth that the rendered medical image <NUM> actually depicts.

At step <NUM> of <FIG>, the input depth map and the estimated depth map are compared. For example, as shown in <FIG>, the input depth map <NUM> and the estimated depth map <NUM> are compared at block <NUM> of workflow <NUM>. As the input depth map <NUM> represents the depth that the rendered medical image <NUM> should depict and the estimated depth map <NUM> represents the depth that the rendered medical image <NUM> actually depicts, by comparing the input depth map <NUM> and the estimated depth map <NUM>, an assessment of the depth perception of the rendered medical image <NUM> is determined.

The comparison may be performed according to any suitable approach. For example, the comparison may be performed by comparing the intensity of each corresponding pixel in the input depth map <NUM> and the estimated depth map <NUM>, by comparing a mean intensity for windows of a predetermined size (e.g., 5x5 pixels), or by any other suitable approach. The results of the comparison may be in the form of a depth score or in any other suitable format.

At step <NUM> of <FIG>, one or more measures of interest are extracted from the rendered medical image. For example, as shown in <FIG>, the measures of interest may be extracted measures of interest <NUM> of workflow <NUM>. The extracted measures of interest <NUM> may be any metric or metrics relating to the image quality of the rendered medical image.

In one embodiment, the extracted measures of interest <NUM> are natural scene statistics. Natural scene statistics modeling assumes that natural images have regular statistical properties that play an important role in human perception. Image distortions cause images to deviate from these natural scene statistics. One example of a natural scene statistic includes locally normalized luminance coefficients to describe the "naturalness" of an image and/or the presence of distortions.

In one embodiment, the one or more measures of interest <NUM> may be extracted from the rendered medical image via photogrammetry and such extracted measures of interest may be compared with their corresponding measurements from the 3D volume data from which the rendered medical image was rendered. Examples of such measures of interest <NUM> may include distances between automatically detected features (e.g., important anatomical landmarks, markers, annotations, etc.), iso-surfaces (i.e., extracted regions of a volume having common data values, such as, e.g., lesions, the surface of an organ, etc.) derived from the rendered medical image and compared with computed iso-surfaces, etc..

At step <NUM> of <FIG>, an image quality assessment of the rendered medical image is determined using a machine learning based image quality assessment network based on the one or more measures of interest and results of the comparison of the input depth map and the estimated depth map. The image quality assessment may be represented in any suitable format, such as, e.g., a classification, a score, etc. For example, as shown in <FIG>, the image quality assessment may be determined by SVM (support vector machine) <NUM> using image quality assessment network <NUM> to generate an image quality score in workflow <NUM>. SVM <NUM> receives the one or more measures of interest <NUM> and the results of the comparison <NUM> as input and generates an image quality score as output using image quality assessment network <NUM>. Image quality assessment network <NUM> helps detect (e.g., classify) if an image is distorted and, if so, what kind of distortion. The image quality score represents the image quality of the rendered medical image.

In one embodiment, the image quality assessment network <NUM> may be implemented as a regression model to map the one or more measures of interest <NUM> and the results of the comparison <NUM> to the image quality score. However, the image quality assessment network may be implemented using any other suitable machine learning based architecture. The image quality assessment network is initially trained during a prior offline or training stage using a set of training data, as described in further detail below with respect to <FIG>. Once trained, the trained image quality assessment network <NUM> is applied during an online or inference stage, for example, to determine an image quality assessment of the rendered medical image by at step <NUM> of <FIG> or by SVM <NUM> in <FIG>.

At step <NUM> of <FIG>, the image quality assessment of the rendered medical image is output. For example, the image quality assessment of the rendered medical image can be output by displaying the image quality assessment on a display device of a computer system, storing the image quality assessment on a memory or storage of a computer system, or by transmitting the image quality assessment to a remote computer system.

In one embodiment, the image quality assessment determined in method <NUM> may be utilized for imaging rendering parameter refinement using continuous learning. For example, as shown in <FIG>, the image quality score output by SVM <NUM> is compared with a predefined threshold v at comparison block <NUM>. The image quality score satisfying (e.g., is greater than) the threshold v indicates relatively high image quality while the image quality score not satisfying (e.g., is not greater than) the threshold v indicates relatively low image quality. In response to the image quality score not satisfying the threshold v, the imaging rendering parameters <NUM> are modified at block <NUM>. For example, the imaging rendering parameters <NUM> may be modified by a clinician or other user. Workflow <NUM> may then be repeated for one or more additional iterations using the modified imaging rendering parameters to generate updated rendered medical image <NUM> and updated input depth map <NUM> by renderer <NUM> until the image quality score satisfies the threshold v at comparison block <NUM> (or any other stopping condition is satisfied, such as, e.g., a predetermined number of iterations is performed). In other words, with respect to method <NUM> of <FIG>, an updated rendered medical image and an updated input depth map may be generated using the modified imaging rendering parameters and method <NUM> may be repeated any number of iterations using the updated rendered medical image as the rendered medical image received at step <NUM> and the updated input depth map as the input depth map received at step <NUM>. In response to the image quality score satisfying the threshold v, the rendered medical image <NUM>, input depth map <NUM>, and the image quality score are added to the set of training data for retraining the image quality assessment network <NUM>. In this way, the image quality assessment network <NUM> is continually improved and the renderer <NUM> is fine-tuned by optimizing parameters to generate rendered medical images that are not only photorealistic in appearance, but also clinically relevant.

In one embodiment, in response to the image quality score satisfying the threshold v at comparison block <NUM>, the imaging rendering parameters <NUM> may be stored in memory as a clinical preset. The imaging rendering parameters <NUM> may be stored in response to user input and associated with a clinical application (e.g., a particular clinical procedure or use case, such as stent placements, aortic valve guidance, etc.) and/or a particular image modality of the rendered medical image. The user may then retrieve the imaging rendering parameters <NUM> from the memory when encountering the clinical application. The clinical presets of imaging rendering parameters may be customized for a hospital site or institution based on the set of training data and the user input.

In one embodiment, the image quality assessment network may be utilized for other applications. For example, the image quality assessment network may be utilized in software development tools for automatic testing and evaluating new algorithms or features. In another example, the image quality score generated by the image quality assessment network may be incorporated into the loss function of artificial intelligence based algorithms (e.g., image denoising) or into a differentiable renderer.

<FIG> shows a workflow <NUM> for training a machine learning based image quality assessment network for determining an image quality assessment of a rendered medical image, in accordance with one or more embodiments. For example, workflow <NUM> may be applied to train the image quality assessment network utilized at step <NUM> of <FIG> or image quality assessment network <NUM> of <FIG>.

Workflow <NUM> is performed during a prior offline or training stage to train the image quality assessment network using a set of training data. The set of training data comprises training rendered medical images <NUM> with corresponding training input depth maps <NUM> and clinician input <NUM> of an image quality assessment of the training rendered medical images <NUM>. The clinician input <NUM> may be received from one or more clinicians, medical professionals, or any other suitable user or users. In one embodiment, the clinician input <NUM> comprises an image quality score representing the opinion of the image quality of the training rendered medical images <NUM> from one or more users. Where the clinician input <NUM> is determined from a plurality of users, the image quality score may be calculated as a mean image quality score of the plurality of clinicians.

Estimated depth maps <NUM> are generated from the training rendered medical image <NUM>, for example, as described with respect to step <NUM> of <FIG> and the training input depth maps <NUM> and the estimated depth maps <NUM> are compared. One or more measures of interest <NUM> are extracted from training rendered medical images <NUM>, for example, as described with respect to step <NUM> of <FIG>. Image quality assessment network <NUM> is then trained to map extracted measures of interest <NUM> and results of the depth map comparison <NUM> to the image quality assessment (e.g., image quality score) as defined by clinician input <NUM>. Once trained, the trained image quality assessment network <NUM> is applied during an online or inference stage, for example, to determine an image quality assessment of the rendered medical image at step <NUM> of <FIG> and/or as image quality assessment network <NUM> of <FIG>.

Embodiments described herein are described with respect to the claimed 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 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, certain embodiments described herein are described with respect to methods and systems utilizing trained machine learning based networks (or models), as well as with respect to methods and systems for training machine learning based networks. 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 a machine learning based network can be improved with features described or claimed in context of the methods and systems for utilizing a trained machine learning based network, and vice versa.

In particular, the trained machine learning based networks applied in embodiments described herein can be adapted by the methods and systems for training the machine learning based networks. Furthermore, the input data of the trained machine learning based network can comprise advantageous features and embodiments of the training input data, and vice versa. Furthermore, the output data of the trained machine learning based network can comprise advantageous features and embodiments of the output training data, and vice versa.

In general, a trained machine learning based network mimics cognitive functions that humans associate with other human minds. In particular, by training based on training data, the trained machine learning based network is able to adapt to new circumstances and to detect and extrapolate patterns.

In general, parameters of a machine learning based network 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 machine learning based network can be adapted iteratively by several steps of training.

In particular, a trained machine learning based network can comprise a neural network, a support vector machine, a decision tree, and/or a Bayesian network, and/or the trained machine learning based network 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.

<FIG> shows an embodiment of an artificial neural network <NUM>, in accordance with one or more embodiments. Alternative terms for "artificial neural network" are "neural network", "artificial neural net" or "neural net". Machine learning networks described herein, such as, e.g., the depth estimation network utilized at step <NUM> and the image quality assessment network utilized at step <NUM> of <FIG>, the SVM <NUM> or the image quality assessment network <NUM> of <FIG>, or the image equality assessment network <NUM> of <FIG>, may be implemented using artificial neural network <NUM>.

The artificial neural network <NUM> comprises nodes <NUM>-<NUM> and edges <NUM>, <NUM>,. , <NUM>, wherein each edge <NUM>, <NUM>,. , <NUM> is a directed connection from a first node <NUM>-<NUM> to a second node <NUM>-<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>,. , <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>-<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>,. , <NUM> between the nodes <NUM>-<NUM>. In particular, edges <NUM>, <NUM>,. , <NUM> can exist only between neighboring layers of nodes. In the embodiment shown in <FIG>, there is an input layer <NUM> comprising only nodes <NUM> and <NUM> without an incoming edge, an output layer <NUM> comprising only node <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> and <NUM> within the input layer <NUM> usually relates to the number of input values of the neural network <NUM>, and the number of nodes <NUM> within the output layer <NUM> usually relates to the number of output values of the neural network <NUM>.

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>-<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 value of the node <NUM> of the output layer <NUM> is equivalent to the output value of the neural network <NUM>. Furthermore, each edge <NUM>, <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>-<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> shows a convolutional neural network <NUM>, in accordance with one or more embodiments. Machine learning networks described herein, such as, e.g., the depth estimation network utilized at step <NUM> and the image quality assessment network utilized at step <NUM> of <FIG>, the SVM <NUM> or the image quality assessment network <NUM> of <FIG>, or the image equality assessment network <NUM> of <FIG>, may be implemented using convolutional neural network <NUM>.

In the embodiment shown in <FIG>, 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>-<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 embodiment shown in <FIG>, 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 non-linear 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 d1 ·d2 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 embodiment shown in <FIG>, 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 the values of all nodes <NUM> of the output layer <NUM> is <NUM>, and all values of all nodes <NUM> of the output layer are real numbers between <NUM> and <NUM>.

A convolutional neural network <NUM> can also comprise a ReLU (rectified linear units) layer or activation layers with non-linear transfer functions. 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.

The input and output of different convolutional neural network blocks can be wired using summation (residual / dense neural networks), element-wise multiplication (attention) or other differentiable operators. Therefore, the convolutional neural network architecture can be nested rather than being sequential if the whole pipeline is differentiable.

In particular, convolutional neural networks <NUM> can be trained based on the backpropagation algorithm. For preventing overfitting, methods of regularization can be used, e.g. dropout of nodes <NUM>-<NUM>, stochastic pooling, use of artificial data, weight decay based on the L1 or the L2 norm, or max norm constraints. Different loss functions can be combined for training the same neural network to reflect the joint training objectives. A subset of the neural network parameters can be excluded from optimization to retain the weights pretrained on another datasets.

Systems, apparatuses, and methods described herein may be implemented using digital circuitry, or using one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components. Typically, a computer includes a processor for executing instructions and one or more memories for storing instructions and data. A computer may also include, or be coupled to, one or more mass storage devices, such as one or more magnetic disks, internal hard disks and removable disks, magneto-optical disks, optical disks, etc..

Systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computers are located remotely from the server computer and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers.

Systems, apparatus, and methods described herein may be implemented within a network-based cloud computing system. In such a network-based cloud computing system, a server or another processor that is connected to a network communicates with one or more client computers via a network. A client computer may communicate with the server via a network browser application residing and operating on the client computer, for example. A client computer may store data on the server and access the data via the network. A client computer may transmit requests for data, or requests for online services, to the server via the network. The server may perform requested services and provide data to the client computer(s). The server may also transmit data adapted to cause a client computer to perform a specified function, e.g., to perform a calculation, to display specified data on a screen, etc. For example, the server may transmit a request adapted to cause a client computer to perform one or more of the steps or functions of the methods and workflows described herein, including one or more of the steps or functions of <FIG>. Certain steps or functions of the methods and workflows described herein, including one or more of the steps or functions of <FIG>, may be performed by a server or by another processor in a network-based cloud-computing system. Certain steps or functions of the methods and workflows described herein, including one or more of the steps of <FIG>, may be performed by a client computer in a network-based cloud computing system. The steps or functions of the methods and workflows described herein, including one or more of the steps of <FIG>, may be performed by a server and/or by a client computer in a network-based cloud computing system, in any combination.

Systems, apparatus, and methods described herein may be implemented using a computer program product tangibly embodied in an information carrier, e.g., in a non-transitory machine-readable storage device, for execution by a programmable processor; and the method and workflow steps described herein, including one or more of the steps or functions of <FIG>, may be implemented using one or more computer programs that are executable by such a processor. A computer program is a set of computer program instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result.

A high-level block diagram of an example computer <NUM> that may be used to implement systems, apparatus, and methods described herein is depicted in <FIG>. Computer <NUM> includes a processor <NUM> operatively coupled to a data storage device <NUM> and a memory <NUM>. Processor <NUM> controls the overall operation of computer <NUM> by executing computer program instructions that define such operations. The computer program instructions may be stored in data storage device <NUM>, or other computer readable medium, and loaded into memory <NUM> when execution of the computer program instructions is desired. Thus, the method and workflow steps or functions of <FIG> can be defined by the computer program instructions stored in memory <NUM> and/or data storage device <NUM> and controlled by processor <NUM> executing the computer program instructions. For example, the computer program instructions can be implemented as computer executable code programmed by one skilled in the art to perform the method and workflow steps or functions of <FIG>. Accordingly, by executing the computer program instructions, the processor <NUM> executes the method and workflow steps or functions of <FIG>. Computer <NUM> may also include one or more network interfaces <NUM> for communicating with other devices via a network. Computer <NUM> may also include one or more input/output devices <NUM> that enable user interaction with computer <NUM> (e.g., display, keyboard, mouse, speakers, buttons, etc.).

Processor <NUM> may include both general and special purpose microprocessors, and may be the sole processor or one of multiple processors of computer <NUM>. Processor <NUM> may include one or more central processing units (CPUs), for example. Processor <NUM>, data storage device <NUM>, and/or memory <NUM> may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs) and/or one or more field programmable gate arrays (FPGAs).

Data storage device <NUM> and memory <NUM> each include a tangible non-transitory computer readable storage medium. Data storage device <NUM>, and memory <NUM>, may each include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices such as internal hard disks and removable disks, magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (DVD-ROM) disks, or other non-volatile solid state storage devices.

Input/output devices <NUM> may include peripherals, such as a printer, scanner, display screen, etc. For example, input/output devices <NUM> may include a display device such as a cathode ray tube (CRT) or liquid crystal display (LCD) monitor for displaying information to the user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to computer <NUM>.

An image acquisition device <NUM> can be connected to the computer <NUM> to input image data (e.g., medical images) to the computer <NUM>. It is possible to implement the image acquisition device <NUM> and the computer <NUM> as one device. It is also possible that the image acquisition device <NUM> and the computer <NUM> communicate wirelessly through a network. In a possible embodiment, the computer <NUM> can be located remotely with respect to the image acquisition device <NUM>.

Any or all of the systems and apparatus discussed herein may be implemented using one or more computers such as computer <NUM>.

One skilled in the art will recognize that an implementation of an actual computer or computer system may have other structures and may contain other components as well, and that <FIG> is a high level representation of some of the components of such a computer for illustrative purposes.

Claim 1:
A computer-implemented method for automatically determining an image quality assessment of a rendered medical image comprising:
receiving (<NUM>) a rendered medical image (<NUM>);
extracting (<NUM>) one or more measures of interest (<NUM>) from the rendered medical image;
determining (<NUM>) an image quality assessment of the rendered medical image using a machine learning based image quality assessment network (<NUM>) based on the one or more measures of interest; and
outputting (<NUM>) the image quality assessment of the rendered medical image,
characterized by, further comprising:
receiving (<NUM>) an input depth map (<NUM>) of the rendered medical image;
generating (<NUM>) an estimated depth map from the rendered medical image; and
comparing (<NUM>) the input depth map with the estimated depth map,
wherein determining an image quality assessment of the rendered medical image using a machine learning based image quality assessment network based on the one or more measures of interest comprises determining (<NUM>) the image quality assessment of the rendered medical image using the machine learning based image quality assessment network based on results of the comparison.