Patent Description:
In recent years, radiation imaging apparatuses have been widely used in medical settings, and radiographs are obtained as digital signals, subjected to image processing, and then displayed by a display apparatus and used to make a diagnosis.

In radiation imaging, irradiation of a region other than a field of interest (hereinafter referred to as an "irradiation field") that is necessary for diagnosis is usually prevented by narrowing down the irradiation field using a collimator in order to suppress the influence of radiation on a region other than the irradiation field, prevent scattering from the outside of the irradiation field, and prevent a reduction in contrast.

With regard to images obtained by narrowing down the irradiation field, in order to perform image processing on the irradiation field that is the field of interest in diagnosis, various types of technology have been proposed for extracting the irradiation field.

For example, <CIT> proposes technology for extracting an irradiation field by obtaining a plurality of contours based on the edge intensity in an image and performing correctness determination. Also, <CIT> proposes technology for inputting image data to a neural network and outputting an irradiation field as a result.

However, a human body, which is the subject, may include a structure such as a bone, an implant, or the like that has a strong edge component and is difficult to distinguish from contours used for narrowing down the irradiation field, and there may be a case where the irradiation field cannot be recognized using the technology disclosed in <CIT>.

The technology disclosed in <CIT> enables determination of the irradiation field based on comprehensive features of a larger number of images using a neural network, but there may be a case where it is difficult to classify a region as the irradiation field using the neural network only.

The present invention was made in view of the above problems and provides image processing technology that enables extraction of an irradiation field. <NPL>, describes automatic collimation detection in digital radiographs. A method of performing the over-segmentation on an image and calculating the probability of being included in the ROI is performed for each pair of neighboring super pixels by using random forests. <CIT> discloses that information associated with the shape of an irradiation field candidate area is input from an operation panel, and a second irradiation field recognition circuit performs irradiation field recognition on the basis of the information associated with the shape. <CIT> discloses a technique for precisely recognizing a field of view of collimation using a neural network, when a radiation image which has been picked up with the use of a collimation field stop is read.

The present invention provides an image processing apparatus, an image processing method, and a storage medium as specified in the appended claims.

According to the present invention, image processing technology that enables extraction of an irradiation field can be provided.

The following describes exemplary embodiments of the present invention in detail with reference to the drawings. It should be noted that radiation in the present invention includes not only commonly used X-rays but also α-rays, β-rays, γ-rays, and the like that are beams constituted by particles (including photons) that are emitted through radioactive decay, as well as beams (for example, particle beams and cosmic rays) that have substantially equivalent or higher levels of energy. The following describes an exemplary case where X-rays are used as radiation.

First, an exemplary configuration of an image processing apparatus according to a first embodiment of the present invention will be described with reference to <FIG> is a block diagram showing an exemplary basic configuration of a radiation imaging system that includes the image processing apparatus of the first embodiment.

A radiation imaging system <NUM> includes a radiation generating apparatus <NUM> that generates radiation, a bed <NUM> on which a subject <NUM> is arranged, a detection apparatus <NUM> (FPD) that detects radiation and outputs image data corresponding to radiation that has passed through the subject <NUM>, a control apparatus <NUM> that controls radiation generation timing and radiation generation conditions of the radiation generating apparatus <NUM>, a data collecting apparatus <NUM> that collects various types of digital data, and an information processing apparatus <NUM> that performs image processing and controls the entire apparatus according to instructions from a user. It should be noted that the configuration of the radiation imaging system <NUM> may also be referred to as a radiation imaging apparatus.

The information processing apparatus <NUM> includes an image processing apparatus <NUM> that includes an irradiation field recognizing unit <NUM> and a diagnosis image processing unit <NUM>, a CPU <NUM>, a memory <NUM>, an operation panel <NUM>, a storage apparatus <NUM>, and a display apparatus <NUM>, and these are electrically connected to each other via a CPU bus <NUM>.

Various types of data and the like that are necessary for processing performed in the CPU <NUM> are stored in the memory <NUM>, and the memory <NUM> includes a working memory that is used by the CPU <NUM>. The CPU <NUM> is configured to control operations of the entire apparatus using the memory <NUM> according to instructions input by a user through the operation panel <NUM>.

<FIG> is a block diagram showing an example of a basic functional configuration of the irradiation field recognizing unit <NUM> in the image processing apparatus of the first embodiment. The irradiation field recognizing unit <NUM> includes, as functional units, a preprocessing unit <NUM>, an inference unit <NUM>, a contour extracting unit <NUM>, and a field extracting unit <NUM>.

The radiation imaging system <NUM> starts an imaging sequence of the subject <NUM> according to an instruction given by a user through the operation panel <NUM>. Radiation according to predetermined conditions is generated by the radiation generating apparatus <NUM>, and the detection apparatus <NUM> is irradiated with radiation that has passed through the subject <NUM>. Here, the control apparatus <NUM> controls the radiation generating apparatus <NUM> based on radiation generation conditions such as the voltage, current, irradiation period, and the like, and causes the radiation generating apparatus <NUM> to generate radiation under predetermined conditions.

The detection apparatus <NUM> detects radiation that has passed through the subject <NUM>, converts the detected radiation into an electrical signal, and outputs the signal as image data that corresponds to the radiation. The image data output from the detection apparatus <NUM> is collected as digital image data by the data collecting apparatus <NUM>. The data collecting apparatus <NUM> transfers the image data collected from the detection apparatus <NUM> to the information processing apparatus <NUM>. In the information processing apparatus <NUM>, the image data is transferred via the CPU bus <NUM> to the memory <NUM> under control performed by the CPU <NUM>.

The image processing apparatus <NUM> applies various types of image processing to the image data stored in the memory <NUM> to extract an irradiation field from the image obtained through radiation imaging. After a subject region is extracted by the irradiation field recognizing unit <NUM>, the diagnosis image processing unit <NUM> of the image processing apparatus <NUM> applies diagnosis image processing such as gradation processing, enhancement processing, or noise reduction processing to create an image that is suitable for diagnosis. The image processing apparatus <NUM> stores the result of processing performed by the diagnosis image processing unit <NUM> in the storage apparatus <NUM> and displays the result on the display apparatus <NUM>.

Next, processing that is performed by the irradiation field recognizing unit <NUM> will be described with reference to <FIG> is a flowchart showing the flow of processing that is performed by the irradiation field recognizing unit <NUM>, and <FIG> is a schematic diagram showing examples of images that are processed in the processing performed by the irradiation field recognizing unit <NUM>. The following describes an example in which a hand of the subject <NUM> is imaged using a rectangular collimator. However, the present invention is not limited by the imaged portion and the shape of the collimator described in the first embodiment, and can also be applied to other portions of the subject <NUM> such as the chest or abdomen, or any shape for narrowing down the irradiation field, such as a case where a circular collimator is used, for example.

In step S201, the preprocessing unit <NUM> performs preprocessing of an input image. Through this preprocessing, the input image is converted into a format with which subsequent inference processing that is performed using a neural network can be effectively performed. Here, the preprocessing unit <NUM> can convert the input image into an image of the same type as images that are used for learning by the inference unit <NUM>, for example, learning by a neural network. As one example of the preprocessing performed by the preprocessing unit <NUM>, the preprocessing unit <NUM> can perform grid removal processing, scattered ray reduction processing, noise reduction processing, logarithmic conversion processing, and normalization processing, and thereafter perform processing for scaling up or down to an image size that is suitable for a neural network that is used in the inference processing performed by the inference unit <NUM>.

Although any image size can be employed, if the image input to the preprocessing unit <NUM> has an aspect ratio of <NUM>:<NUM> in length and width, such as <NUM> × <NUM> pixels or <NUM> × <NUM> pixels, for example, capability of generalization to rotation can be enhanced. Also, as one example of the normalization processing, the preprocessing unit <NUM> can normalize the signal level such that the signal of the input image is changed from <NUM> to <NUM>. The preprocessing unit <NUM> obtains a preprocessed image <NUM> by executing the above processing. As shown in <FIG>, the preprocessed image <NUM> includes an irradiation field <NUM> and a collimator region <NUM> at a given ratio.

In step S202, the inference unit <NUM> obtains, based on inference processing, an irradiation field candidate in the image obtained through radiation imaging. Using a neural network, the inference unit <NUM> performs the inference processing on the preprocessed image <NUM>, which has been preprocessed, to obtain the irradiation field candidate from the image obtained through radiation imaging. The inference unit <NUM> performs the inference processing based on learning that is performed using a set of data that includes images obtained through radiation imaging as input and irradiation fields as output. The inference unit <NUM> performs the inference processing based on a result of learning that includes learning by a neural network. The inference unit <NUM> uses a neural network that has performed learning in advance, as the neural network used in the inference processing. Details of learning by the neural network and the inference processing using the neural network will be described later. In further examples that do not fall under the scope of the present invention, the inference unit <NUM> can also use a processing unit that is created through machine learning performed using a support-vector machine or boosting.

The inference unit <NUM> obtains, as the irradiation field candidate, a probability map <NUM> that indicates the probability of "being the irradiation field" or "not being the irradiation field (i.e., being the collimator region)" for each pixel of the input image. Here, the inference processing performed using a neural network uses a large amount of features that cannot be obtained by a person and enables determination of the irradiation field using comprehensive features of an image that are not limited to edge features, but it is sometimes difficult to classify a region as the irradiation field through this processing alone. For example, in the probability map <NUM> shown in <FIG>, a region <NUM> that is highly likely to be the irradiation field and a region <NUM> that is highly likely to be the collimator are obtained, but there may be a case where the probability map includes a misdetection region <NUM> that is actually not the collimator region but is determined as being the collimator region.

In step S203, the contour extracting unit <NUM> extracts contours of the irradiation field based on the irradiation field candidate. The contour extracting unit <NUM> extracts the contours of the irradiation field based on contour extraction processing performed on the irradiation field candidate. The contour extracting unit <NUM> performs the contour extraction processing on the probability map <NUM> (irradiation field candidate) obtained in step S202 by extracting contours <NUM> based on the shape of the collimator.

The contour extracting unit <NUM> performs the contour extraction processing on the irradiation field candidate by extracting contours based on the shape of the collimator. The contour extracting unit <NUM> changes the contour extraction processing according to the shape of the collimator. For example, the contour extracting unit <NUM> is capable of performing rectangular contour extraction processing when the shape of the collimator is rectangular and performing circular contour extraction processing when the shape of the collimator is circular. The contour extracting unit <NUM> is capable of selecting contour extraction processing for a rectangular shape of the collimator and contour extraction processing for a circular shape of the collimator.

In the first embodiment, it is assumed that the collimator (rectangular collimator) has a rectangular contour, and the contour extracting unit <NUM> extracts straight lines as contour candidates by performing rectangular contour extraction processing. That is, the contour extracting unit <NUM> performs contour extraction processing for obtaining, from the probability map <NUM>, at most four straight lines as contour lines that constitute the contours of the collimator based on the rectangular shape of the collimator. Then, final contours are set based on validity confirmation processing that is performed on the extracted contour candidates. Details of this contour extraction processing will be described later.

In step S204, the field extracting unit <NUM> extracts the irradiation field based on the contours <NUM>. Specifically, the field extracting unit <NUM> performs irradiation field extraction processing to obtain a divided region image <NUM> that is an image obtained by dividing the input image into an irradiation field <NUM> and a collimator region <NUM>. Here, the field extracting unit <NUM> extracts a region inside the contours <NUM> as the irradiation field <NUM>. The region inside the contours <NUM> is a region that is surrounded by the contours <NUM>. That is, the field extracting unit <NUM> extracts a region surrounded by the contours <NUM> as the irradiation field <NUM> based on information regarding the contours <NUM> extracted in step S203, and extracts a region other than this region (i.e., region that is not surrounded by the contours <NUM>) as the collimator region <NUM>. Through the irradiation field extraction processing performed by the field extracting unit <NUM>, the irradiation field <NUM> can be extracted with the misdetection region <NUM> included in the result of the inference processing performed using the neural network being eliminated.

It should be noted that the field extracting unit <NUM> can perform the irradiation field extraction processing by extracting the irradiation field based on a region in which an irradiation field that is presumed from the contours <NUM> overlaps with an irradiation field that is presumed from the probability map <NUM>. That is, the field extracting unit <NUM> can extract, as the irradiation field, a region in which the ratio of overlap between the irradiation field presumed from the contours <NUM> and the irradiation field presumed from the probability map <NUM> is at least a set value. The field extracting unit <NUM> performs the irradiation field extraction processing based on information regarding the contours <NUM> (S203) and information regarding the probability map <NUM> (S202) by extracting, as the irradiation field <NUM>, a region in which the ratio of overlap between a region that can be formed by the contours <NUM> and the region <NUM> that is highly likely to be the irradiation field in the probability map <NUM> is at least a predetermined reference value (for example, <NUM>%).

Through the processing performed in above steps S201 to S204, the irradiation field recognizing unit <NUM> can extract the irradiation field with high accuracy.

Next, details of processing performed by the inference unit <NUM> will be described with reference to <FIG> is an illustrative diagram showing the concept of learning by a neural network and <FIG> is an illustrative diagram showing the concept of inference by the neural network.

Learning by the neural network is performed using a set of input data <NUM> and a corresponding set of training data <NUM>.

First, inference processing is performed on the set of input data <NUM> using a neural network <NUM> that is in the process of learning, to output a set of inference results <NUM>. Next, a loss function is calculated from the set of inference results <NUM> and the set of training data <NUM>. Any function such as a square error function or a cross entropy error function can be used as the loss function. Further, back propagation starting from the loss function is performed to update a parameter group of the neural network <NUM> in the process of learning. Learning by the neural network <NUM> in the process of learning can be advanced by repeating the above processing while changing the set of input data <NUM> and the set of training data <NUM>.

Inference by the neural network is processing for applying inference processing that is performed using a learned neural network <NUM> to input data <NUM> and outputting an inference result <NUM>.

In the first embodiment, the set of training data <NUM> is set such that, in the set of input data <NUM>, the irradiation field is represented by <NUM> and the collimator region is represented by <NUM>. The set of training data <NUM> may be created as appropriate through manual input or may be obtained by the irradiation field recognizing unit <NUM> from an outside source. Alternatively, the irradiation field recognizing unit <NUM> may automatically create standard training data according to the imaged portion.

Here, the neural network <NUM> has a structure in which a large number of processing units <NUM> are randomly connected to each other. Examples of the processing units <NUM> perform processing including convolution calculation, normalization processing such as batch normalization, and processing performed using an activation function such as ReLU or Sigmoid, and the processing units have parameter groups for describing content of processing. For example, a set of these processing units that perform processing in the order of, for example, convolution calculation, normalization, and an activation function are arranged in three to about a few hundred layers and are connected to each other, and can form a structure called a convolutional neural network or a recurrent neural network.

It should be noted that input and output of the neural network <NUM> take the form of an image that has been processed by the preprocessing unit <NUM>. It is possible to employ a configuration in which an output image has the same resolution as an input image and indicates the probability of being the irradiation field for each pixel. In another example, the resolution of the output image may be set lower than that of the input image. In this case, the processing period of learning and inference can be reduced, but accuracy of the entire processing including subsequent processing may be reduced as a tradeoff.

Next, details of the contour extraction processing performed by the contour extracting unit <NUM> will be described with reference to <FIG> is a flowchart showing the flow of the contour extraction processing, <FIG> is a schematic diagram showing an image that is processed in the contour extraction processing, and <FIG> is a diagram showing an example of transformation of an image into a polar coordinate space. The contour extracting unit <NUM> extracts contours of the irradiation field from an image that is obtained from the probability map <NUM> and includes edges that indicate boundaries between the irradiation field and the collimator region.

In step S401, the contour extracting unit <NUM> extracts, from the probability map <NUM>, edges that indicate boundaries between the region <NUM> that is highly likely to be the irradiation field and the region <NUM> that is highly likely to be the collimator. Although the method for extracting edges is not limited, edges can be extracted by applying a differential filter such as a sobel filter to the probability map <NUM>, for example. Although a probability from <NUM> to <NUM> is set for each pixel of the probability map <NUM>, it is also possible to perform binary coded processing based on a preset threshold value (for example, <NUM>) and perform processing for extracting pixels having at least the threshold value in order to simplify edge extraction processing. Through the above processing, an image <NUM> including edges <NUM> that include boundaries between the irradiation field and the collimator region can be obtained.

In step S402, the contour extracting unit <NUM> applies the Hough transform to the image <NUM>. Here, a point on the image <NUM> that is represented as (x, y) in a rectangular coordinate system is transformed into a polar coordinate space of angle θ and distance ρ using Formula <NUM>. Here, θ is the angle between the x axis and a line that is drawn from the origin perpendicularly to a straight line that passes through (x, y) and ρ is the length of the line drawn from the origin perpendicularly to the straight line passing through (x, y). When transformation is performed in a range of -<NUM>° < θ ≤ <NUM>°, for example, distribution in the polar coordinate space is obtained as shown in <FIG>. Here, a pair (θ, ρ) that takes a local maximum value in the polar coordinate space indicates a high probability of existence of a straight line in the image in the rectangular coordinate system. Using this feature, contours of the rectangular collimator that has a linear structure can be easily extracted through application of the Hough transform.

In step S403, the contour extracting unit <NUM> extracts the longest straight line <NUM> as a contour candidate from the image <NUM>. In this step, the contour extracting unit <NUM> searches the entire polar coordinate space and extracts a straight line that is formed by a pair (θ, ρ) <NUM> that takes the maximum value in the polar coordinate space.

In step S404, the contour extracting unit <NUM> extracts a straight line <NUM> that is opposite and parallel to the straight line <NUM> as a contour candidate. Assuming that the collimator has a rectangular shape, it is thought that there is one side that extends in a direction parallel to another side. Based on this assumption, the contour extracting unit <NUM> searches the polar coordinate space for a local maximum value in a region <NUM> where θ is in a predetermined range with respect to the pair (θ, ρ) <NUM> that corresponds to the straight line <NUM>. The range of θ can be set to about <NUM>° to <NUM>° relative to θ = -<NUM>° or about -(<NUM>° to <NUM>°) relative to θ = <NUM>°. Thus, the contour extracting unit <NUM> can extract a pair (θ, ρ) <NUM> as a local maximum value other than the pair (θ, ρ) <NUM> and a straight line <NUM> corresponding to the pair <NUM>.

In step S405, the contour extracting unit <NUM> extracts a straight line <NUM> that crosses and is perpendicular to the straight line <NUM> as a contour candidate. Assuming that the collimator has a rectangular shape, it is thought that there is one side that extends in a direction perpendicular to another side. Based on this assumption, the contour extracting unit <NUM> searches the polar coordinate space for a pair (θ, ρ) in a region <NUM> where θ is in a predetermined range in the polar coordinate space with respect to the pair (θ, ρ) <NUM> corresponding to the straight line <NUM>. The search range can be set to any range that is about ±<NUM>° relative to θ = <NUM>° that has a phase difference of +<NUM>° from θ (= -<NUM>°) of the reference pair <NUM>. Thus, the contour extracting unit <NUM> can extract a pair (θ, ρ) <NUM> as a point at which a waveform <NUM> passing through the pair (θ, ρ) <NUM> and a waveform <NUM> passing through the pair (θ, ρ) <NUM> cross each other, and a straight line <NUM> corresponding to the pair <NUM>.

In step S406, the contour extracting unit <NUM> extracts a straight line <NUM> that is opposite and parallel to the straight line <NUM> as a contour candidate. Similarly to step S404, the contour extracting unit <NUM> searches for a pair (θ, ρ) from a region <NUM> in the polar coordinate space to search for a side that extends in a direction parallel to the straight line <NUM>. The region <NUM> used as the search range can be set narrower than the region <NUM> from which the pair (θ, ρ) <NUM> has been extracted. The contour extracting unit <NUM> extracts a pair (θ, ρ) <NUM> at which a waveform <NUM> passing through the pair (θ, ρ) <NUM> and a waveform <NUM> passing through the pair (θ, ρ) <NUM> cross each other, and a straight line <NUM> corresponding to the pair <NUM> from the region <NUM>. It should be noted that, if a straight line is not found in any of steps S403 to S406, processing in that step can be skipped supposing that there is no straight line.

In step S407, the contour extracting unit <NUM> confirms whether the straight lines <NUM> to <NUM> that are contour candidates extracted in steps S403 to S406 are valid as contours of the irradiation field and the collimator region. For example, the contour extracting unit <NUM> can determine whether the extracted straight lines are longer than a predetermined length. Based on this determination, the contour extracting unit <NUM> extracts, out of the straight lines extracted as contour candidates, straight lines that are longer than the predetermined length as contours.

Alternatively, the contour extracting unit <NUM> can determine whether a region that is formed by the extracted straight lines overlaps and matches well with the region <NUM> that is highly likely to be the irradiation field in the probability map <NUM>, for example, whether an overlap ratio that indicates the ratio of overlap between these regions is at least a threshold value. If the overlap ratio that indicates the ratio of overlap between the region based on the contour candidates (region based on the extracted straight lines <NUM> to <NUM>) and an irradiation field presumed from the probability map <NUM> is at least a threshold value, the contour extracting unit <NUM> extracts the contour candidates (straight lines <NUM> to <NUM>) as contours.

With regard to confirmation of validity of the contours, the contour extracting unit <NUM> can perform determination processing that matches features of the image obtained through radiation imaging, such as the imaged portion of the subject. If validity of a straight line is not confirmed in this step, the contour extracting unit <NUM> eliminates the straight line, performs another search as necessary, and outputs a group of remaining straight lines as final contours. Through the processing performed in above steps S401 to S407, the contour extracting unit <NUM> can extract contours with high accuracy.

According to the first embodiment, image processing technology can be provided with which an irradiation field can be accurately extracted even if the irradiation field includes a structure that has a strong edge component and is difficult to distinguish from contours used for narrowing down the irradiation field.

In the second embodiment, an exemplary configuration of a case where a circular collimator is used for narrowing down the irradiation field will be described. Examples of configurations of the radiation imaging system <NUM> and the image processing apparatus are similar to those in the first embodiment. The second embodiment differs from the first embodiment in that the contour extracting unit <NUM> performs circular contour extraction processing, assuming the use of a circular collimator. In the second embodiment, the contour extracting unit <NUM> extracts a circle or an ellipse as a contour candidate by performing the circular contour extraction processing.

Details of the contour extraction processing performed by the contour extracting unit <NUM> will be described with reference to <FIG> is a flowchart showing the flow of the contour extraction processing in the second embodiment and <FIG> is a schematic diagram showing images that are processed in the contour extraction processing in the second embodiment.

Assume that an image <NUM> that is obtained by narrowing down the irradiation field using a circular collimator is input to the irradiation field recognizing unit <NUM> and a probability map <NUM> is output by the inference unit <NUM>. In the probability map <NUM>, a region <NUM> that is highly likely to be the irradiation field and a region <NUM> that is highly likely to be the collimator are obtained, but there may be a case where the probability map includes a misdetection region <NUM> that is actually not the collimator region but is determined as being the collimator region.

In step S501, the contour extracting unit <NUM> obtains, from the probability map <NUM>, an image <NUM> that includes edges that indicate boundaries between the region <NUM> that is highly likely to be the irradiation field and the region <NUM> that is highly likely to be the collimator. This processing is equivalent to step S401 in <FIG>.

In step S502, the contour extracting unit <NUM> applies the Hough transform to the image <NUM>. Here, a point on the image <NUM> that is represented as (x, y) in a rectangular coordinate system is transformed into a three-dimensional Hough space of the center point (center X, center Y) and the radius r of a circle using Formula <NUM>.

Alternatively, assuming that the collimator has an elliptical contour, the contour extracting unit <NUM> can transform a point on the image <NUM> that is represented as (x, y) in a rectangular coordinate system into a four-dimensional Hough space of the center point (center X, center Y) of an ellipse and the major axis a and the minor axis b of the ellipse using Formula <NUM>.

In step S503, the contour extracting unit <NUM> extracts a circular contour <NUM> by selecting coordinates in the Hough space that correspond to the circular contour <NUM> from the result of the Hough transform.

In step S504, the contour extracting unit <NUM> confirms whether the circular contour <NUM> extracted in step S503 is valid as a contour of the irradiation field and the collimator region. For example, the contour extracting unit <NUM> can determine whether the position of center coordinates of the extracted circle (or ellipse) and its radius (or major axis and minor axis) are included in a predetermined range. For example, the contour extracting unit <NUM> extracts, as the contour, a circle that is extracted as a contour candidate and for which the position of center coordinates and the radius are included in a predetermined range. Alternatively, the contour extracting unit <NUM> extracts, as the contour, an ellipse that is extracted as a contour candidate and for which the position of center coordinates and the major axis and the minor axis are included in a predetermined range.

Alternatively, the contour extracting unit <NUM> can determine whether a region that is formed by the extracted circle (or ellipse) overlaps and matches well with the region <NUM> that is highly likely to be the irradiation field in the probability map <NUM>, for example, whether an overlap ratio that indicates the ratio of overlap between these regions is at least a reference value. If the overlap ratio that indicates the ratio of overlap between a region based on the contour candidate (region based on the extracted circular contour <NUM>) and an irradiation field presumed from the probability map <NUM> is at least a threshold value, the contour extracting unit <NUM> extracts the contour candidate (circular contour <NUM>) as the contour.

With regard to confirmation of validity of the contour, the contour extracting unit <NUM> can perform determination processing that matches features of the image obtained through radiation imaging, such as the imaged portion of the subject.

Through the processing performed in above steps S501 to S504, the contour extracting unit <NUM> can extract contours with high accuracy, even when a circular collimator is used for narrowing down the irradiation field.

It should be noted that, if examples of rectangular collimators and examples of circular collimators (including elliptical collimators) are both included in the training data used in learning by the inference unit <NUM>, the inference unit <NUM> can obtain the probability map <NUM> regardless of whether the collimator has a rectangular shape or a circular shape. Using these properties, a configuration may be employed in which, according to the shape of the obtained probability map, the most suitable contour extracting unit <NUM> can be selected from that for rectangular contours described in the first embodiment and that for circular contours described in the second embodiment.

For example, a configuration may be employed in which a user performs the selection through the operation panel <NUM> or a configuration may be employed in which the contour extracting unit <NUM> performs both processing for rectangular contours and processing for circular contours and the field extracting unit <NUM> automatically extracts, as the irradiation field <NUM>, a region in which the ratio of overlap between a region formed by extracted contours and a region that is highly likely to be the irradiation field in the probability map is at least a predetermined reference value.

Next, a third embodiment will be described. <FIG> is a block diagram showing an exemplary basic configuration of a radiation imaging system that includes an image processing apparatus of the third embodiment. The configuration of the third embodiment differs from that of the first embodiment in that, in addition to the same elements as those in the first embodiment, a learning apparatus <NUM> is included in the information processing apparatus <NUM>.

In the first and second embodiments, the radiation imaging system <NUM> is configured such that the inference unit <NUM> only performs inference processing using a neural network and learning by the neural network is performed in advance.

In the third embodiment, the radiation imaging system <NUM> is configured such that images that are obtained in the usage environment of the user and a set of datasets of the irradiation field are accumulated in the storage apparatus <NUM>. As a result of the learning apparatus <NUM> being electrically connected to the CPU bus <NUM> in the information processing apparatus <NUM>, learning processing can also be performed in the information processing apparatus <NUM> of the radiation imaging system <NUM>. The inference unit <NUM> performs inference processing based on a result of learning to which the images obtained in the usage environment of the user and the set of datasets of the irradiation field are newly added and a result of learning performed in advance. Thus, additional learning processing using the learning apparatus <NUM> can be performed using the set of datasets stored in the storage apparatus <NUM> as new training data to update a parameter group in the inference unit <NUM>. It should be noted that a calculation unit that has high parallel operation performance such as GPU can be used as the learning apparatus <NUM>.

Any timing can be selected as the timing for performing the additional learning such as when at least a predetermined number of datasets that serve as new training data are accumulated in the storage apparatus <NUM> or at least a predetermined number of datasets that are obtained through modification of irradiation field recognition results by the user are accumulated. Also, transfer learning can be performed by setting a parameter group that was used prior to learning as default values of a parameter group of the neural network when additional learning is performed by the inference unit <NUM>.

The storage apparatus <NUM> and the learning apparatus <NUM> do not necessarily have to be directly installed in the information processing apparatus <NUM>, and may be provided on a cloud server that is connected to the information processing apparatus <NUM> via a network. In this case, datasets that are obtained by a plurality of radiation imaging systems <NUM> can be collected and stored on the cloud server and additional learning can also be performed using these datasets.

As described above, according to the third embodiment, image processing technology can be provided that not only achieves the effects of the first and second embodiments but also enables more accurate extraction of an irradiation field by optimizing irradiation field recognition processing so as to match the usage environment of the user.

Although some embodiments of the present invention have been described, it goes without saying that the present invention is not limited to those embodiments and can be carried out with various alterations and modifications within the scope of the gist of the present invention.

Claim 1:
An image processing apparatus (<NUM>) configured to extract an irradiation field from an image obtained through radiation imaging, comprising:
preprocessing means (<NUM>) configured to preprocess an input image;
inference means (<NUM>) configured to infer an irradiation field candidate by inputting the preprocessed input image into a neural network learned using a set of input image data (<NUM>) and a corresponding set of training data (<NUM>);
contour extracting means (<NUM>) configured to extract at least one contour candidate of the irradiation field based on the inferred irradiation field candidate, and extract a contour of the irradiation field; and
field extracting means (<NUM>) configured to extract the irradiation field based on the contour, wherein
the inference means (<NUM>) is configured to infer, as the irradiation field candidate, a probability map that indicates a probability of being the irradiation field or a probability of not being the irradiation field for each pixel of the input image input into the neural network, wherein the probability map includes a first region that is highly likely to be the irradiation field and a second region that is highly likely to be a collimator, and
the contour extracting means (<NUM>) is configured to extract the at least one contour candidate by performing contour extraction processing on the probability map based on a shape of the collimator,
wherein, if an overlap ratio that indicates a ratio of overlap between a region that is formed by the at least one contour candidate and the first region that is highly likely to be the irradiation field in the probability map is at least a threshold value, the contour extracting means (<NUM>) is configured to extract the at least one contour candidate as the contour of the irradiation field.