Patent Application
20230237619
The present invention relates to a medical-image processing apparatus, a medical-image processing method, and a program for the same.
X-ray diagnosis and treatment based on radiography are widely performed in medical front, and digital diagnostic imaging based on radiographic images captured using a radiation detector (hereinafter referred to as “sensor”) is in widespread use all over the world. The sensor can image output immediately and can therefore capture not only still images but also moving images. Furthermore, an increase in the resolution of the sensor allows imaging that provides more detailed information.
In contrast, reduced-resolution radiographic images are sometimes obtained to reduce radiation exposure to the examinee. One example is a use case in which X rays are applied for a long time, such as moving images. In this case, the sensor increases X-ray dose per pixel by operating using multiple pixels as one pixel. This allows the overall X-ray radiation to be reduced, thereby reducing radiation exposure to the examinee.
However, the reduction in resolution causes loss of detailed information in the radiographic images, such as lesion information and information for accurate positioning of the imaging apparatus.
One example of a process for decompressing detailed information in low-resolution images (increasing the resolution) is superresolution processing. Known examples of the superresolution processing include a method for converting multiple low-resolution images to a high-resolution image and a method for associating the features of a low-resolution image with the features of a high-resolution image and providing a high-resolution image on the basis of the information (PTL 1). A recent example method for associating features is machine learning. In particular, supervised learning using a convolutional neural network (CNN) is rapidly becoming popular because of their high performance (PTL 2). Superresolution processing using the CNN decompresses detailed information in input low-resolution images using learning parameters created by means of supervised learning. The superresolution processing is also applied to medical images.
Superresolution processing using the CNN makes an inference using a low-resolution image as an input and outputs a superresolution image as an inference. A high-resolution image is used as a training image for training. For this reason, multiple sets of a high-resolution image and a low-resolution image are prepared as training data. In learning, a method for generating a low-resolution image from a high-resolution image is learned. However, a method for generating a low-resolution image from a high-resolution image varies according to the operating method of the sensor. Using a CNN that has learned one generation method and using a low-resolution image generated using another generation method as an input for inference will result in a decrease in the quality of the superresolution image.
The present invention is made in view of the above problems, and an object is to provide an apparatus and a method for processing medical images of appropriately improved resolution, and a program for the same.
Another object of the present invention is to offer operational advantages that are provided using the configurations of the following embodiments and that are not provided using known techniques.
A medical-image processing apparatus according to the present invention includes an obtaining unit configured to obtain a medical image obtained by capturing an image of an examinee and a generation unit configured to input the medical image to a learning model selected based on an operation mode of a sensor at the image capturing to generate a medical image of a higher resolution than a resolution of the medical image.
Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings.
The following embodiments illustrate a representative example in which radiographic images are used as an example of medical images. More specifically, an example in which radiographic images obtained using simple roentgenography are used as an example of the radiographic images will be described. The medical images applicable to the embodiments are illustrative only, and other medical images can also be suitably applied. Examples include medical images obtained using a computed tomography (CT) scanner, a magnetic resonance imaging (MRI) scanner, a three-dimensional ultrasonic imaging system, a photoacoustic tomography scanner, a positron emission tomography (PET)/single photon emission computed tomography (SPECT) scanner, an optical coherence tomography (OCT) scanner, and a digital radiography scanner.
The following embodiments illustrate building of a learning model based on supervised learning using a convolutional neural network (CNN) in which a low-resolution medical image, which is input data, and a high-resolution medical image, which serves as correct data, are used as training data. For this reason, the learning model is hereinafter referred to as CNN. Not the learning using the CNN but any machine learning capable of building a learning model capable of outputting medical images with improved resolution and reduced noise may be used.
A medical-image processing apparatus according to this embodiment inputs a medical image to a learning model selected on the basis of the operation mode of a sensor used for capturing the medical image and generates a medical image of a resolution higher than that of the medical image.
The learning-model selecting unit 101 obtains the operation mode of the sensor and outputs a learning model for machine learning. The image obtaining unit 102 obtains a radiographic image from an external device and outputs a low-resolution radiographic image. The machine learning unit 103 receives the low-resolution radiographic image and the learning model for machine learning as an input and performs inference processing of superresolution processing CNN and output a superresolution image.
The learning-model selecting unit 101, the image obtaining unit 102, and the machine learning unit 103 shown in
In the configuration example of
The processing will be described with reference to the functional block diagram in
First at S301, the learning-model selecting unit 101 obtains the operation mode of the sensor in capturing an image of the examinee. The operation mode of the sensor is a method whereby the sensor generates an image and outputs it. Examples of the operation mode include a binning count, a method of adding pixels in the binning area, and a frame rate.
Next at S302, the learning-model selecting unit 101 selects a learning model on the basis of the operation mode of the sensor. The learning model is a training parameter set of the CNN that has performed supervised learning in advance. The association of the operation mode of the sensor with the learning model is set in advance. More specifically, the operation mode of the sensor and the learning model trained in advance using an image captured in the same operation mode as the operation mode of the sensor are associated with each other and is set. For example, a setting screen, as shown in
The operation of the CNN at the training will be described with reference to
At S501, the machine learning unit 103 builds a learning model by performing supervised learning using a set of input data and output data as training data. The training data is a set of low-resolution images 511, or input data, and high-resolution images 515, or correct data corresponding thereto. For the low-resolution images 511 and the high-resolution images 515 for use as training data, for example, the machine learning unit 103 converts the resolution of the high-resolution images 515 to generate the low-resolution images 511 of a lower resolution than the resolution of the high-resolution images 515. The resolution of the high-resolution images 515 subjected to a noise reduction process in advance may be converted to generate the low-resolution images 511 of reduced noise.
The machine learning unit 103 performs inference processing on the low-resolution images 511 using the parameters of a CNN 512 in the course of learning and outputs superresolution images 514 as inferences (S501). The CNN 512 has a structure in which multiple processing units 513 are freely connected. Example processes performed by the processing units 513 include a convolutional operation, a normalization process, and processes using an activating function such as ReLU or Sigmoid, for which a parameter set for describing the individual processing details is present. The parameters can take various structures. For example, parameter sets are connected in about three to hundreds layers in the order of convolutional operation, normalization, and activating functions.
Next at S502, the machine learning unit 103 calculates a loss function from the superresolution images 514, which are inferences, and the high-resolution images 515. The loss function may be any function, such as a square error or a cross entropy error.
Next at S503, the machine learning unit 103 performs error backpropagation starting from the loss function calculated at S502 to update the parameter set of the CNN 512.
Finally at S504, the machine learning unit 103 determines whether to end the learning, and if the learning is to be continued, goes to S501. Repeating the processes from S501 to 503 while changing the low-resolution images 511 and the high-resolution images 515 allows the update of the parameters of the CNN 512 to be repeated so that the loss function is decreased, thereby increasing the accuracy of the machine learning unit 103. When the learning is sufficiently advanced and is determined to be ended, the process is completed. The determination whether to end the learning is performed on the basis of criteria set for the problems, for example, that the accuracy of the inference has reached a fixed value or greater without occurrence of over-training or that the loss function has reached a fixed value or less.
Thus, the training of the CNN is performed.
Examples of a combination of training parameters and the operation mode of the sensor are shown in
As shown in
Next at S303, the image obtaining unit 102 obtains an image from the X-ray sensor.
Next at S304, the image obtaining unit 102 preprocesses the obtained image to output a preprocessed image. The preprocessing is processing for preparing for superresolution processing. For example, the image obtaining unit 102 performs at least one of processing for correcting the characteristics of the sensor, frequency processing, and gradation processing. In the processing for correcting the characteristics of the sensor, offset correction, (dark-current correction), gain correction, and loss correction are performed to keep correlation with the peripheral pixels.
Finally at S305, the machine learning unit 103 receives the preprocessed image as an input, performs CNN inference processing using the learning model selected at S302, and outputs a superresolution image.
Thus, the processing of the medical-image processing apparatus 100 is performed.
As described above, a learning model is selected using a medical image, which is captured on the basis of the operation mode of the sensor at the image capturing, as an input, and a resolution-increased medical image as an output. The selected learning model has learned a medical image, in advance, captured in the same operation mode as the operation mode of the sensor at the image capturing. This matches the generation method for the input medical image with the generation method for the medical image used in training the learning model, allowing generation of a medical image with appropriated improved resolution.
In this embodiment, the addition method and the binning count are used as examples of the operation mode of the sensor. Alternative examples include the image obtaining rate (frame rate) and the reading area size of the sensor and other items related to a change in the sensor operation method. The operation mode of the sensor may be changed not only in a single sensor but also across a plurality of sensors. If the same addition method applies to the same sensor, the learning model is changed for each sensor.
There is no need to prepare different learning models for all operation modes. If a sensor operation mode that can be shared, such as an operation mode in which the process of generating high-resolution images from low-resolution images is the same, the same learning model may be used among the operation modes of the sensor.
Another embodiment of the learning model setting different from S302 in the first embodiment will be described with reference to the block diagram in
First at S301, the learning-model selecting unit 101 obtains the operation mode of the sensor. The operation mode of the sensor is a pattern indicating how the sensor generates and outputs an image.
At S302, the learning-model selecting unit 101 selects a learning model on the basis of the operation mode of the sensor. The learning model includes a learning network (CNN) that performed supervised learning in advance and CNN training parameters obtained by learning.
The operation of the CNN at the learning is the same as that of the first embodiment, and a description thereof is omitted. Examples of a combination of the learning model and the operation mode of the sensor are shown in
For example, the number of processing units 513 constituting the CNN 512 in
The steps from S303 to S305 are the same as those of the first embodiment, and descriptions thereof are omitted.
In this embodiment, the binning count is used as an example of the operation mode of the sensor. The same applies to another operation mode of the sensor related to an increase in the sensor operation speed.
Another embodiment of the learning model setting different from S302 of the first embodiment will be described with reference to the block diagram of
At S301, the learning-model selecting unit 101 obtains the operation mode of the sensor. The operation mode of the sensor is a pattern indicating how the sensor generates and outputs an image.
At S302, the learning-model selecting unit 101 obtains a learning model on the basis of the operation mode of the sensor. The learning model includes the training parameters of the CNN that performed supervised learning in advance.
The operation of the CNN at the learning is the same as that of the first embodiment, and a description thereof is omitted. Examples of a combination of the learning model and the operation mode of the sensor are shown in
where J is an error in the parameter W, := is assignment operation, ∇ is gradient, and α is a training rate. Decreasing the value of α decreases the reflection of the error J on the parameter W, and increasing the value of α increases the reflection of the error J on the parameter W. Accordingly, for the addition method in which the loss curve fluctuates, the reflection of the error is decreased by decreasing the training parameter.
Steps from S303 to S305 are the same as those of the first embodiment, and descriptions thereof are omitted.
Although this embodiment uses the training rate as the hyperparameter, a batch size or an epoch count may be used.
It is to be understood that the present invention can also be implemented by supplying a program for implementing one or more functions of the above embodiments to a system or an apparatus via a network or a storage medium and by reading and executing the program with one or more processors of the computer of the system or the apparatus. The present invention can also be implemented by a circuit for performing one or more of the functions.
The processor or the circuit can include a central processing unit (CPU), a microprocessing unit (MPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), and a field programmable gateway (FPGA). The processor or the circuit can include a digital signal processor (DSP), a data flow processor (DFP), and a neural processing unit (NPU).
The medical-image processing apparatuses according to the embodiments may be realized as stand-alone apparatuses or may be a communicable combination of a plurality of apparatuses combined so as to execute the above processes, both of which are included in the embodiments of the present invention. The above processes may be executed by a common server or a server group. The plurality of units constituting each medical-image processing apparatus only needs to be able to communicate with one another at a predetermined communication rate and does not have to be present in the same facility or in the same country.
The embodiments of the present invention include a configuration in which a program of software for implementing the functions of the above embodiments is supplied to a system or an apparatus and the computer of the system or the apparatus reads and executes the code of the supplied program.
Accordingly, the program code installed in a computer to implement the processes according to the embodiments is also one of the embodiments of the present invention. The functions of the embodiments can also be implemented by part or all of the actual processes performed by an operating system (OS) operating in the computer according to instructions included in a program read by the computer.
The present invention allows generation of a medical image of appropriately improved resolution.
While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2020-179042 | Oct 2020 | JP | national |
This application is a Continuation of International Patent Application No. PCT/JP2021/038606, filed Oct. 19, 2021, which claims the benefit of Japanese Patent Application No. 2020-179042, filed Oct. 26, 2020, both of which are hereby incorporated by reference herein in their entirety.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/JP2021/038606 | Oct 2021 | WO |
| Child | 18295079 | US |
