Patent Description:
A medical imaging apparatus is an apparatus configured to scan an object and then obtain volume data or a tomography image of the object, and examples of the medical imaging apparatus include a computed tomography (CT) apparatus, a magnetic resonance imaging apparatus, an X-ray imaging apparatus, or the like. In a process of scanning an object, a process of processing obtained raw data and image data, and a process of reconstructing a medical image, the medical imaging apparatus sets various parameters. The setting of parameters may be automatically performed by the medical imaging apparatus or may be performed according to a user input. However, because the parameters can be variously set according to scanned environments, it is difficult to set an appropriate parameter for a certain situation.

In accordance with its description, US patent application <CIT> describes a computer-implemented method for providing image quality optimization individualized for a user includes a computer receiving raw image data acquired from an image scanner and identifying one or more raw image quality features based on the raw image data. The computer automatically determines one or more target image quality features by applying one or more user preferences to the one or more raw image quality features. The computer also automatically determines one or more processing parameters based on the one or more target image quality features. The computer may then process the raw image data using the one or more processing parameters to yield an image.

In accordance with its description, US patent application <CIT> describes that an ultrasound scanner is equipped with one or more fuzzy control units that can perform adaptive system parameter optimization anywhere in the system. In one embodiment, an ultrasound system comprises a plurality of ultrasound image generating subsystems configured to generate an ultrasound image, the plurality of ultrasound image generating subsystems including a transmitter subsystem, a receiver subsystem, and an image processing subsystem; and a fuzzy logic controller communicatively coupled with at least one of the plurality of ultrasound imaging generating subsystems. The fuzzy logic controller is configured to receive, from at least one of the plurality of ultrasound imaging generating subsystems, input data including at least one of pixel image data and data for generating pixel image data; to process the input data using a set of inference rules to produce fuzzy output; and to convert the fuzzy output into numerical values or system states for controlling at least one of the transmit subsystem and the receiver subsystem that generate the pixel image data.

Embodiments of the disclosure are provided to improve a function of automatically setting parameters in a medical imaging apparatus.

Embodiments of the disclosure are provided to automatically determine an appropriate parameter, in consideration of a user, a use environment, an apparatus used by the user, patient information, a protocol, or the like.

Embodiments of the disclosure are provided to decrease a need for development manpower, a development period, and development costs of a medical imaging apparatus by simplifying initial settings of the medical imaging apparatus.

The principles of embodiments of the disclosure will now be described and embodiments thereof will now be provided to clearly define the scope of claims and for one of ordinary skill in the art to be able to perform the present.

Throughout the specification, like reference numerals denote like elements. Not all elements of the embodiments are described in the specification, and general features in the art or redundant features among the embodiments are omitted. Throughout the specification, a term such as "module" or "unit" may be implemented as one of or a combination of at least two of software, hardware, and firmware. In some embodiments, a plurality of modules or a plurality of units may be implemented as one element, or a module or a unit may include a plurality of elements.

Hereinafter, embodiments of the present disclosure will now be described with reference to the accompanying drawings.

In embodiments, an image may include any medical image acquired by various medical imaging apparatuses such as a magnetic resonance imaging (MRI) apparatus, a computed tomography (CT) apparatus, an ultrasound imaging apparatus, or an X-ray apparatus.

Also, in the present specification, an "object", which is a thing to be imaged, may include a human, an animal, or a part thereof. For example, an object may include a part of a human, that is, an organ or a tissue, or a phantom.

Throughout the specification, an ultrasound image refers to an image of an object processed based on ultrasound signals transmitted to the object and reflected therefrom.

Hereinafter, embodiments will now be described with reference to the accompanying drawings.

In the present disclosure, a medical imaging apparatus may be embodied as an MRI apparatus, a CT apparatus, an ultrasound diagnosis apparatus, or an X-ray apparatus. In the present disclosure, it is assumed that the medical imaging apparatus is an ultrasound diagnosis apparatus, but embodiments of the present disclosure are not limited to an ultrasound diagnosis apparatus.

<FIG> is a block diagram illustrating a configuration of an ultrasound diagnosis apparatus <NUM>, according to an embodiment.

Referring to <FIG>, the ultrasound diagnosis apparatus <NUM> may include a probe <NUM>, an ultrasound transceiver <NUM>, a controller <NUM>, an image processor <NUM>, a display <NUM>, a storage <NUM>, a communicator <NUM>, and an input interface <NUM>.

The ultrasound diagnosis apparatus <NUM> may be of a cart-type or portable-type ultrasound diagnosis apparatus, that is portable, moveable, mobile, or hand-held. Examples of the portable-type ultrasound diagnosis apparatus <NUM> may include a smartphone, a laptop computer, a personal digital assistant (PDA), a tablet personal computer (PC), or the like, each of which may include a probe and a software application, but embodiments are not limited thereto.

The probe <NUM> may include a plurality of transducers. The plurality of transducers may transmit ultrasound signals to an object <NUM>, in response to transmitting signals received by the probe <NUM>, from a transmitter <NUM>. The plurality of transducers may receive ultrasound signals reflected from the object <NUM> so as to generate reception signals. In addition, the probe <NUM> and the ultrasound diagnosis apparatus <NUM> may be formed in one body (e.g., disposed in a single housing), or the probe <NUM> and the ultrasound diagnosis apparatus <NUM> may be formed separately (e.g., disposed separately in separate housings) but linked in a wired or wireless manner. In addition, the ultrasound diagnosis apparatus <NUM> may include one or more probes <NUM> according to embodiments.

The controller <NUM> may control the transmitter <NUM> to generate transmitting signals to be applied to each of the plurality of transducers, based on a position and a focal point of the plurality of transducers included in the probe <NUM>.

The controller <NUM> may control a receiver <NUM> to generate ultrasound data by converting reception signals received from the probe <NUM> from analogue to digital signals and summing the reception signals converted into digital form, based on the position and the focal point of the plurality of transducers.

The image processor <NUM> may generate an ultrasound image by using ultrasound data generated by the ultrasound receiver <NUM>.

The display <NUM> may display the generated ultrasound image and various pieces of information processed by the ultrasound diagnosis apparatus <NUM>. The ultrasound diagnosis apparatus <NUM> may include one or more displays <NUM> according to embodiments. Also, the display <NUM> may include a touchscreen in combination with a touch panel.

The controller <NUM> may control operations of the ultrasound diagnosis apparatus <NUM> and flow of signals between internal elements of the ultrasound diagnosis apparatus <NUM>. The controller <NUM> may include a memory configured to store a program or data to perform functions of the ultrasound diagnosis apparatus <NUM>, and a processor and/or a microprocessor (not shown) to process the program or data. For example, the controller <NUM> may control operations of the ultrasound diagnosis apparatus <NUM> by receiving a control signal from the input interface <NUM> or an external apparatus.

The ultrasound diagnosis apparatus <NUM> may include the communicator <NUM> and may be connected to external apparatuses, for example, servers, medical apparatuses, and portable devices such as smartphones, tablet PCs, wearable devices, or the like via the communicator <NUM>.

The communicator <NUM> may include at least one element capable of communicating with the external apparatuses. For example, the communicator <NUM> may include at least one of a short-range communication module, a wired communication module, and a wireless communication module.

The communicator <NUM> may exchange a control signal and data with the external apparatuses.

The storage <NUM> may store various data or programs for driving and controlling the ultrasound diagnosis apparatus <NUM>, input and/or output ultrasound data, obtained ultrasound images, applications, or the like.

The input interface <NUM> may receive a user input to control the ultrasound diagnosis apparatus <NUM>. Examples of the user input may include an input of manipulating a button, a keypad, a mouse, a trackball, a jog switch, knob, or the like, an input of touching a touchpad or a touchscreen, a voice input, a motion input, and a bioinformation input, for example, iris recognition or fingerprint recognition, but the present disclosure is not limited thereto.

An example of the ultrasound diagnosis apparatus <NUM> according to an embodiment will be described below with reference to <FIG>.

<FIG> are diagrams illustrating an ultrasound diagnosis apparatus according to an embodiment.

Referring to <FIG>, the ultrasound diagnosis apparatuses 100a and 100b may each include a main display <NUM> and a sub-display <NUM>. At least one of the main display <NUM> and the sub-display <NUM> may include a touchscreen. The main display <NUM> and the sub-display <NUM> may display ultrasound images and/or various information processed by the ultrasound diagnosis apparatuses 100a and 100b. The main display <NUM> and the sub-display <NUM> may provide graphical user interfaces (GUIs), thereby receiving user inputs of data to control the ultrasound diagnosis apparatuses 100a and 100b. For example, the main display <NUM> may display an ultrasound image and the sub-display <NUM> may display a control panel to control display of the ultrasound image as a GUI. The sub-display <NUM> may receive an input of data to control the display of an image through the control panel displayed as a GUI. The ultrasound diagnosis apparatuses 100a and 100b may control the display of the ultrasound image on the main display <NUM> by using the input control data.

Referring to <FIG>, the ultrasound diagnosis apparatus 100b may include a control panel <NUM>. The control panel <NUM> may include buttons, trackballs, jog switches, or knobs, and may receive data to control the ultrasound diagnosis apparatus 100b from the user. For example, the control panel <NUM> may include a time gain compensation (TGC) button <NUM> and a freeze button <NUM>. The TGC button <NUM> is to set a TGC value for each depth of an ultrasound image. Also, when an input of the freeze button <NUM> is detected during scanning an ultrasound image, the ultrasound diagnosis apparatus 100b may keep displaying a frame image at that time point.

The buttons, trackballs, jog switches, and knobs included in the control panel <NUM> may be provided as a GUI to the main display <NUM> or the sub-display <NUM>.

Referring to <FIG>, the ultrasound diagnosis apparatus 100c may include a portable device. An example of the portable ultrasound diagnosis apparatus 100c may include, for example, smart phones including probes and applications, laptop computers, personal digital assistants (PDAs), or tablet PCs, but an exemplary embodiment is not limited thereto.

The ultrasound diagnosis apparatus 100c may include the probe <NUM> and a main body <NUM>. The main body <NUM> may include a touchscreen <NUM>. The touchscreen <NUM> may display an ultrasound image, various pieces of information processed by the ultrasound diagnosis apparatus 100c, and a GUI.

<FIG> is a diagram illustrating a configuration of a medical imaging apparatus <NUM> according to an embodiment.

The medical imaging apparatus <NUM> includes an input interface <NUM>, a processor <NUM>, an output interface <NUM>, and a storage <NUM>. The processor <NUM> includes one or more processors and includes a neural network processor <NUM>.

The medical imaging apparatus <NUM> and a scan unit to scan an object may be embodied in one body or may be separately embodied. For example, the medical imaging apparatus <NUM> and the ultrasound diagnosis apparatus <NUM> may be embodied in one body, or the medical imaging apparatus <NUM> may be embodied as a computer or a portable terminal configured to communicate with the ultrasound diagnosis apparatus <NUM>. Also, according to an embodiment, the medical imaging apparatus <NUM> may be embodied as a workstation arranged outside a scan room of a CT imaging apparatus, an MRI apparatus, or an X-ray apparatus.

The medical imaging apparatus <NUM> may automatically set a parameter used by the medical imaging apparatus <NUM> or the scan unit. In the present disclosure, the parameter indicates a parameter for setting components of the medical imaging apparatus <NUM> or the scan unit, or a parameter used in processing image data. The parameter may include a gain of signal processing, a Digital Radiography (DR) index of a detector, or the like. Also, the parameter may include a TGC value, a lateral gain compensation (LGC) value, or the like of the ultrasound diagnosis apparatus <NUM>. Also, the parameter may include a window level (WL), a window width (WW), or the like of the ultrasound diagnosis apparatus <NUM>.

The input interface <NUM> receives a medical image. The medical image may be raw data or a reconstructed image. Also, the medical image may be an ultrasound image, a CT image, an MRI image, or an X-ray image.

According to the present embodiment, the input interface <NUM> may be implemented as a communicator. The communicator may receive the medical image from the scan unit of the scan room or an external apparatus. The input interface <NUM> may receive the medical image by using wired or wireless communication.

According to another embodiment, the input interface <NUM> may be embodied as a scan unit. For example, the scan unit may be embodied as an ultrasound probe, an X-ray generator and an X-ray detector of a CT imaging apparatus, a scanner of an MRI apparatus, or an X-ray generator and an X-ray detector of an X-ray apparatus. The input interface <NUM> may obtain raw data of an imaged object.

Also, the input interface <NUM> is configured to receive a control input from a user. According to an embodiment, the input interface <NUM> is configured to receive a first control input of setting a parameter from the user. The input interface <NUM> may be embodied as a keyboard, a mouse, a touchscreen, a touch pad, a wheel, a knob, or the like.

The processor <NUM> may control all operations of the medical imaging apparatus <NUM>. The processor <NUM> includes one or more processors and includes the neural network processor <NUM>. According to an embodiment, the neural network processor <NUM> may be embodied as a separate chip. The neural network processor <NUM> may include a neural network.

The processor <NUM> is configured to identify at least one image feature value from an input medical image. The at least one image feature value indicates a value obtained from the input medical image, and may include a mean, a contrast, a standard deviation, sharpness, a view type, or the like. The processor <NUM> may obtain an image feature value of raw data or reconstructed data of an input medical image. The image feature value may configure a feature vector corresponding to the input medical image. The feature vector may correspond to a set of a plurality of image feature values.

According to an embodiment, when a control signal requesting execution of an auto scan mode is input via the input interface <NUM>, the processor <NUM> performs an operation of estimating an optimal parameter.

The processor <NUM> obtains a parameter value of an input medical image, based on an optimization coefficient with respect to at least one parameter stored in the storage <NUM>. The optimization coefficient is a coefficient for calculating at least one parameter value from at least one feature value. A function of calculating a parameter value from at least one feature value may be expressed as various forms of the function, and for example, the function may be expressed as various forms of the function, the forms including a linear function, a quadratic function, an exponential function, a logarithmic function, or the like. According to an embodiment, the optimization coefficient may indicate a weight with respect to each node of a neural network processor.

The processor <NUM> is configured to estimate at least one optimization coefficient by using new training data and pre-trained data stored in the storage <NUM>. The pre-trained data and the new training data may include a feature vector and a parameter value corresponding thereto. The new training data may be obtained based on a first control signal of setting at least one parameter value. For example, when the first control signal of setting an A parameter as a second value is input, a value of the A parameter corresponding to a feature vector of an input medical image is set as the second value, and a case in which the A parameter is set as the second value with respect to the feature vector may be stored as the new training data.

Also, the processor <NUM> is configured to update the optimization coefficient with respect to the at least one parameter stored in the storage <NUM>, based on the first control signal of setting the at least one parameter value. For example, when an optimization coefficient with respect to the A parameter is stored as a first set in the storage <NUM>, and the first control signal of setting the A parameter as the second value is input, the processor <NUM> may update the optimization coefficient with respect to the A parameter, based on the first control signal. In detail, the processor <NUM> may use, as the new training data, the case of setting the A parameter as the second value, may reflect the new training data thereto, and then may update the optimization coefficient with respect to the A parameter as the second set. According to an embodiment, whenever the first control signal of setting the value of the A parameter is input, the stored optimization coefficient is updated in real time, therefore, even when a preset number of training data is not accumulated, an optimization coefficient may be immediately updated, and an optimal result based on user preference may be obtained.

When the processor <NUM> calculates an optimization coefficient with respect to a parameter, the processor <NUM> may use a method such as a linear least squares method, deep learning, or the like. For example, to estimate the optimization coefficient, the processor <NUM> may estimate the optimization coefficient by which a least square value with respect to the parameter value corresponding to the first control signal is calculated when the feature vector is inserted thereto. As another example, the processor <NUM> may estimate, by using deep learning, the optimization coefficient by which the parameter value corresponding to the first control signal is calculated with respect to the feature vector.

When the first control signal of setting at least one parameter value is input, the processor <NUM> may set the at least one parameter value based on the first control signal, may update the medical image by using the set parameter value, and may display the updated medical image. For example, the processor <NUM> may obtain a resultant medical image while the A parameter is set as a first value, and when the first control signal of setting the A parameter as the second value is input while the obtained resultant medical image is displayed on a display, the processor <NUM> may re-obtain a resultant medical image by setting the A parameter as the second value, and may update the displayed medical image to the re-obtained resultant medical image.

The output interface <NUM> is configured to output a resultant medical image. According to an embodiment, the output interface <NUM> may include a display and may display the resultant medical image on the display. According to another embodiment, the output interface <NUM> may include a communicator, and may transmit the resultant medical image to an external apparatus by using the communicator. The external apparatus may include a user terminal, an external server, a console, or the like. Examples of the user terminal may include a smartphone, a tablet PC, a PC, or the like.

The output interface <NUM> may include the display and may display a GUI on the display. The output interface <NUM> may display the resultant medical image on the GUI. Also, the output interface <NUM> may provide a GUI for receiving an input of the first control signal.

The storage <NUM> is configured to store the pre-trained data, the new training data, and the optimization coefficient with respect to at least one parameter.

<FIG> is a diagram illustrating a process of estimating a parameter value, according to an embodiment.

According to an embodiment, the medical imaging apparatus <NUM> may estimate at least one parameter value from a feature vector by using the neural network processor <NUM>.

The feature vector may include a plurality of feature values. For example, the feature vector may include at least one of an image feature value, a user feature value, a use environment feature value, an apparatus feature value, a patient feature value, and a scan feature value, or a combination thereof. According to an embodiment, raw data with respect to a feature value may be input to the neural network processor <NUM>, and then the neural network processor <NUM> may calculate the feature value from the raw data and may estimate a parameter value by using the calculated feature value. For example, image data may be input to the neural network processor <NUM>, and the neural network processor <NUM> may extract an image feature value from the image data and then may obtain a parameter value by using the image feature value.

The combination of feature values included in the feature vector may vary according to each case. For example, in a case where an ultrasound image is obtained, an image feature value is obtained from the obtained ultrasound image, the medical imaging apparatus <NUM> calculates a first parameter value from a first feature vector including the image feature value, and then a user feature value is input, a second feature vector including the image feature value and the user feature value may be input again to the neural network processor <NUM>, and the neural network processor <NUM> may calculate a second parameter value corresponding to the second feature vector.

The neural network processor <NUM> may include a plurality of layers and a plurality of nodes. The plurality of layers may include at least one of a input layer, the hidden layer, and the output layer. An optimization coefficient may be input to the neural network processor <NUM> and may be applied to the plurality of layers and the plurality of nodes.

The neural network processor <NUM> may estimate at least one parameter value from the input feature vector. For example, the neural network processor <NUM> may estimate at least one of a gain, a DR index, a TGC value, and an LGC value, or a combination thereof.

The user feature value may include a user's job and user preference. The user's job may be classified into a doctor, a radiologic technologist, a nurse, a normal person, etc. The user preference may indicate preferences respectively corresponding to a plurality of pieces of identification information, based on user identification information. For example, information indicating that a user A prefers a gain of <NUM> and a user B prefers a gain of <NUM> may be considered as a feature value.

The user feature value may be obtained by obtaining the user identification information and using user information stored in the storage <NUM>. According to an embodiment, the medical imaging apparatus <NUM> may obtain the user feature value from a user input via the input interface <NUM> or an input from an external apparatus. For example, the processor <NUM> may provide a GUI for receiving the user feature value and may obtain the user feature value via the GUI. According to another embodiment, the medical imaging apparatus <NUM> may receive the user feature value corresponding to the user identification information from an external server.

The use environment feature value may include an intensity of illumination, a use location of a medical image, or the like. The intensity of illumination indicates an intensity of illumination of a location or place where the medical image is read. The use location of the medical image may be divided into an operating room, an examining room, an ambulance, a laboratory, or the like.

The use environment feature value may be obtained from information stored in the medical imaging apparatus <NUM>, may be obtained from a sensor provided at the medical imaging apparatus <NUM>, or may be obtained by receiving information about a use environment from an external apparatus. Medical imaging apparatus <NUM> may include an illumination sensor and may obtain an illumination value from a sensing value sensed by the illumination sensor. According to an embodiment, the medical imaging apparatus <NUM> may include a global positioning system (GPS), and may determine the use location of the medical image, based on location information obtained by the GPS. According to an embodiment, the medical imaging apparatus <NUM> may store information about a place where a corresponding apparatus is installed and may obtain information about the use location by using the stored information.

The apparatus feature value may include a display feature, processor performance, a manufacturer, a model name, or the like. The display feature may include a brightness feature of a display, a dynamic range feature, or the like. The apparatus feature value indicates features of an apparatus for reading a medical image. According to an embodiment, when a medical image processed by the processor <NUM> is transmitted to an external apparatus via the communicator, a feature of the external apparatus is expressed as the apparatus feature value.

The apparatus feature value may be obtained based on identification information of an apparatus. For example, the processor <NUM> may use an apparatus feature value stored to correspond to a serial number of the apparatus. As another example, the processor <NUM> may use an apparatus feature value stored to correspond to a manufacturer or a model name of the apparatus. For example, the storage <NUM> may store a type of the apparatus (e.g., a smartphone, a tablet PC, a desktop computer, a wearable device, or the like), a display feature, or the like with respect to the serial number or the model name of the apparatus.

The patient feature value includes a medical history of a patient. For example, the patient feature value may include a region of interest (ROI), a name of disease, a disease progress, or the like of the patient. When a medical image is an ultrasound image, the patient feature value may include TGC and LGC that are appropriate for the ROI of the patient. When the medical image is a CT image, the patient feature value may include WL and WW that are appropriate for the ROI of the patient.

The processor <NUM> may receive identification information about the patient from the input interface <NUM>, and may obtain the patient feature value from the storage <NUM> or an external apparatus. As another example, the processor <NUM> may receive the patient feature value via the input interface <NUM>.

The scan feature value may include an executed protocol, a type of ROI, a position of ROI, or the like. The type of ROI may be classified according to an organ corresponding to a ROI, whether the ROI moves, a type (a bone, blood, a membrane, or the like) of tissue of the ROI, or the like. The position of ROI may indicate information about in which organ a ROI is placed, a depth of the ROI, or the like.

<FIG> illustrates medical images according to an embodiment.

According to an embodiment, while a first image <NUM> is obtained by using a first value of a first parameter with respect to an input medical image and is displayed, when a first control signal of setting the first parameter as a second value is input, the processor <NUM> generates a second image <NUM> by setting the first parameter as the second value and outputs the second image <NUM> via the output interface <NUM>. Also, the processor <NUM> not only performs a simple process of setting the first parameter as the second value but also calculates an optimization coefficient based on the first control signal and updates an optimization coefficient stored in the storage <NUM>.

<FIG> is a diagram illustrating a configuration of a medical imaging apparatus 300a according to an embodiment.

The medical imaging apparatus 300a may include an ultrasound system <NUM> and a processor <NUM>. The ultrasound system <NUM> and the processor <NUM> may be embodied in one body or may be separately embodied.

The ultrasound system <NUM> may include a probe <NUM> to output an ultrasound signal and detect an echo signal, a front-end <NUM> to process an analog signal output from the probe <NUM>, and a back-end <NUM> to process a digital signal processed by the front-end <NUM> and deliver the digital signal, a display <NUM> to display an ultrasound image and a GUI view, and the processor <NUM>.

The processor <NUM> may include an image optimizing module <NUM>, a self-learning module <NUM>, and a memory <NUM>. The image optimizing module <NUM> estimates an optimal parameter with respect to an input medial image and feeds back the optimal parameter to the ultrasound system <NUM>. The self-learning module <NUM> receives a user-input first control signal with respect to an output medical image, and updates the image optimizing module <NUM> in real time so as to output an optimal result according to user preference.

A medical image obtained via the probe <NUM> is displayed in real time on the display <NUM>. When image optimization with respect to the displayed current medical image is requested by a user (operation <NUM>), the image optimizing module <NUM> performs parameter estimation based on a feature vector.

The image optimizing module <NUM> extracts a feature vector <NUM> by extracting (operation <NUM>) an image feature value from an input image <NUM>. In this regard, the input image <NUM> and the feature vector <NUM> are respectively stored in an image data buffer <NUM> and a feature vector buffer <NUM> of the memory <NUM>. The image optimizing module <NUM> performs an operation <NUM> of estimating a value of a parameter including a gain, DR, TGC, or the like from the feature vector <NUM> stored in the feature vector buffer <NUM> and an optimization coefficient <NUM> of the memory <NUM>. For the operation <NUM> of estimating the value of the parameter, various methods including a linear least squares method, deep learning, or the like may be used. The estimated value of the parameter is fed back to the ultrasound system <NUM>, and a medical image whose brightness, a dynamic range, or the like is changed due to a change in the value of the parameter is displayed on the display <NUM>.

Also, according to an embodiment, the self-learning module <NUM> is provided to improve the image optimizing module <NUM> to operate an image optimizing function by applying user preference thereto. When a user requests execution of self-learning (operation <NUM>), the self-learning module <NUM> performs self-learning for optimization. First, a GUI module <NUM> is executed to receive, from a user, an input of a user control signal with respect to a medical image generated based on the estimated value of the parameter. Image data stored in the image data buffer <NUM> of the memory <NUM> is input to the GUI module <NUM>, and the GUI module <NUM> displays, on the display <NUM>, multi-view images of various values of the parameter.

The image optimizing module <NUM> combines a parameter value with a pre-trained feature value and stores a combined value as a new feature value in the feature vector buffer <NUM>, wherein the parameter value is based on user preference input via the GUI module <NUM> and the pre-trained feature value is stored in the feature vector buffer <NUM>. The self-learning module <NUM> performs an optimization coefficient updating process <NUM> by using the new feature value stored in the feature vector buffer <NUM> and the pre-trained feature value that is preset in a factory during the manufacture, and stores the updated optimization coefficient <NUM> in the memory <NUM>. The optimization coefficient updating process <NUM> is performed in real time and is applied when next image optimization is performed.

<FIG> illustrates a GUI view according to an embodiment.

The processor <NUM> may provide a GUI for receiving a first control signal of setting a parameter value. According to an embodiment, as illustrated in <FIG>, the GUI view may display a medical image <NUM>, and may include the GUI for receiving a first control signal of setting a parameter value. The GUI view may include a first area <NUM> and a second area <NUM>.

The first area <NUM> displays a plurality of the medical images <NUM> generated by using candidate parameter values, and provides the GUI for receiving a first control signal of selecting one of the displayed plurality of medical images <NUM>. For example, the first area <NUM> may include nine candidate medical images <NUM> generated by applying three candidate values with respect to a parameter <NUM> and three candidate values with respect to a parameter <NUM> to an input medical image.

The processor <NUM> may identify respective optimal parameter values of the parameter <NUM> and the parameter <NUM>, and may determine a plurality of candidate values based on the optimal parameter values. For example, the processor <NUM> may determine a plurality of candidate values with respect to an optimal parameter value at a preset interval.

Also, the processor <NUM> may generate the candidate medical images <NUM> by applying the candidate values with respect to the parameter <NUM> and the parameter <NUM> to the input medical image. For example, when the parameter <NUM> is a gain and the parameter <NUM> is a DR index, the processor <NUM> may determine a preset number of candidate values with respect to a value of the gain, may determine a preset number of candidate values with respect to the DR index, and may generate the candidate medical images <NUM> by using the candidate values with respect to the gain and the DR index. For example, a first candidate medical image 712a is generated by applying a gain value of <NUM> and a DR index of <NUM> thereto, a second candidate medical image 712b is generated by applying a gain value of <NUM> and a DR index of <NUM> thereto, and a third candidate medical image 712c is generated by applying a gain value of <NUM> and a DR index of <NUM> thereto.

The processor <NUM> may identify a value of the parameter <NUM> and a value of the parameter <NUM>, in response to a first control signal of selecting one of the candidate medical images <NUM>. For example, when a user selects the second candidate medical image 712b, the processor <NUM> may identify the value of the parameter <NUM> to be <NUM> and the value of the parameter <NUM> to be <NUM>.

The processor <NUM> may adjust the value of the parameter <NUM> and the value of the parameter <NUM>, according to a selected position on the candidate medical image <NUM>. For example, when the first control signal selects a position of an arrow <NUM><NUM> on the second candidate medical image 712b, the processor <NUM> may identify the value of the parameter <NUM> to be <NUM> and the value of the parameter <NUM> to be <NUM>. According to the present embodiment, a parameter value may be finely adjusted in one candidate medical image <NUM>, therefore, a user may further accurately designate a parameter value.

The second area <NUM> indicates a plurality of candidate values <NUM> with respect to a parameter <NUM> corresponding to a set of a plurality of values. The set of the plurality of values of the parameter <NUM> may be expressed as a graph. The parameter <NUM> may correspond to a TGC value, an LGC value, or the like. The second area <NUM> may include a plurality of candidate graphs <NUM>. The processor <NUM> may generate an optimal graph of the parameter <NUM>, based on a feature vector, and then may generate the plurality of candidate graphs <NUM> based on the optimal graph. In response to a first control signal of selecting one of a plurality of candidate values <NUM>, the processor <NUM> may determine a selected candidate value <NUM> to be a value of the parameter <NUM>.

The second area <NUM> may provide curve adjustment UIs 726a and 726b for changing a form of a graph of each candidate value <NUM>. According to an embodiment, the curve adjustment UIs 726a and 726b may include icons 728a and 728b for changing a form or a curvature of a curve 724a, and when positions of the icons 728a and 728b are changed in response to a control signal, the curve adjustment UIs 726a and 726b may change the form or the curvature of the curve 724a to correspond to the changed positions of the icons 728a and 728b. According to an embodiment, the icons 728a and 728b may be moved on reference lines 729a and 729b.

Only one of the first area <NUM> and the second area <NUM> may be provided to a GUI. Types of parameters that may be adjusted in the first area <NUM> or the second area <NUM> may be preset or may be determined according to user selection.

Arrangements of the first area <NUM> and the second area <NUM> illustrated in <FIG> may be an example, and in another embodiment, sizes, forms, designs, or the like of the first area <NUM> and the second area <NUM> may be changed.

The processor <NUM> may provide a GUI for inputting a feature value. For example, the GUI for inputting a feature value may be provided with a GUI for selecting a parameter value. The GUI for inputting a feature value may provide a UI for receiving an input of a user feature value, a use environment feature value, an apparatus feature value, a patient feature value, a scan feature value, or the like.

<FIG> is a block diagram of a neural network processor 325a according to an embodiment.

Referring to <FIG>, the neural network processor 325a according to an embodiment may include a data trainer <NUM> and a data determiner <NUM>.

The data trainer <NUM> may learn a criterion for determining a situation. The data trainer <NUM> may learn a criterion about which data is to be used to determine a certain situation and about how to determine a certain situation by using data. The data trainer <NUM> may obtain data to be used in learning, may apply the obtained data to a data determination model to be described below, and thus may learn the criterion for determining a situation.

The data determiner <NUM> may determine a situation, based on data. The data determiner <NUM> may determine the situation, based on certain data, by using a trained data determination model. The data determiner <NUM> may obtain certain data, based on a criterion that is preset due to training, and may determine a certain situation, based on the certain data, by using the data determination model by using the obtained data as an input value. A resultant value output via the data determination model by using the obtained data as the input value may be used in updating the data determination model.

At least one of the data trainer <NUM> and the data determiner <NUM> may be embodied as at least one hardware chip and may be mounted in the medical imaging apparatus <NUM>. For example, at least one of the data trainer <NUM> and the data determiner <NUM> may be embodied as a dedicated hardware chip for artificial intelligence (AI), or may be embodied as a part of a general-use processor (e.g., a central processing unit (CPU) or an application processor) or a graphic-dedicated processor (e.g., a graphics processing unit (GPU) and may be mounted in the medical imaging apparatus <NUM>.

In this case, the data trainer <NUM> and the data determiner <NUM> may be mounted together in the medical imaging apparatus <NUM>, or may be embodied separately in respective apparatuses. For example, one of the data trainer <NUM> and the data determiner <NUM> may be included in the medical imaging apparatus <NUM> and the other one may be included in a server. Also, the data trainer <NUM> and the data determiner <NUM> may communicate with each other in a wired or wireless manner, such that model information established by the data trainer <NUM> may be provided to the data determiner <NUM>, and data input to the data determiner <NUM> may be provided, as additional training data, to the data trainer <NUM>.

At least one of the data trainer <NUM> and the data determiner <NUM> may be embodied as a software module. When at least one of the data trainer <NUM> and the data determiner <NUM> is embodied as a software module (or a program module including instructions), the software module may be stored in a non-transitory computer-readable recording medium. In this case, one or more software modules may be provided by an operating system (OS) or a certain application. Alternatively, some of the one or more software modules may be provided by the OS and the rest of the one or more software modules may be provided by a certain application.

<FIG> is a block diagram illustrating the data trainer <NUM> according to an embodiment.

Referring to <FIG>, the data trainer <NUM> according to an embodiment may include a data obtainer <NUM>-<NUM>, a pre-processor <NUM>-<NUM>, a training data selector <NUM>-<NUM>, a model trainer <NUM>-<NUM>, and a model evaluator <NUM>-<NUM>.

The data obtainer <NUM>-<NUM> may obtain data necessary for determination of situation. The data obtainer <NUM>-<NUM> may obtain data required in training for determination of situation. For example, the data obtainer <NUM>-<NUM> may obtain raw data, a feature value, a first control signal, or the like to obtain a feature value vector.

The pre-processor <NUM>-<NUM> may pre-process the obtained data to make the obtained data used in training for determination of situation. The pre-processor <NUM>-<NUM> may process the obtained data to have a preset format so as to allow the model trainer <NUM>-<NUM> to use the obtained data in training for determination of situation.

The training data selector <NUM>-<NUM> may select, from among the pre-processed data, data required in training. The selected data may be provided to the model trainer <NUM>-<NUM>. The training data selector <NUM>-<NUM> may select, from among the pre-processed data, the data required in training, according to a preset criterion for determination of situation. Also, the training data selector <NUM>-<NUM> may select data according to a criterion that is preset via training by the model trainer <NUM>-<NUM> to be described below.

The model trainer <NUM>-<NUM> may learn a criterion about how to determine a situation, based on training data. Also, the model trainer <NUM>-<NUM> may learn a criterion about which training data is to be used to determine a situation.

Also, the model trainer <NUM>-<NUM> may train, by using training data, a data determination model to be used in determination of situation. In this case, the data determination model may be a pre-established model. For example, the data determination model may be a model that has been pre-established by receiving default training data (e.g., a sample image, or the like).

The data determination model may be established, in consideration of an application field of a determination model, an objective of training, a computing capability of an apparatus, or the like. The data determination model may be a model based on a neural network. For example, models including a deep neural network (DNN), a recurrent neural network (RNN), a bidirectional recurrent deep neural network (BRDNN), or the like may be used as the data determination model, but the present disclosure is not limited thereto.

When there are a plurality of pre-established data determination models, the model trainer <NUM>-<NUM> may determine, as a data determination model to train, a data determination model having a high relation between input training data and default training data. In this case, the default training data may be pre-classified according to types of data, and data determination models may be pre-established according to the types of data. For example, the default training data may be pre-classified according to various criteria including an area where training data is generated, a time when training data is generated, a size of training data, a genre of training data, a generator of training data, types of an object in training data, or the like.

Also, the model trainer <NUM>-<NUM> may train a data determination model by using a training algorithm including an error back-propagation algorithm, a gradient descent gradient descent, or the like.

Also, the model trainer <NUM>-<NUM> may train a data determination model by supervised learning using training data as an input value. Also, the model trainer <NUM>-<NUM> may train a data determination model by unsupervised learning in which a criterion for determination of situation is found by self-learning a type of data necessary for determination of situation without supervision. Also, the model trainer <NUM>-<NUM> may train a data determination model by reinforcement learning using a feedback about whether a result of determining a situation according to training is correct.

When a data determination model is trained, the model trainer <NUM>-<NUM> may store the trained data determination model. In this case, the model trainer <NUM>-<NUM> may store the trained data determination model in a memory of the medical imaging apparatus <NUM> including the data determiner <NUM>. Alternatively, the model trainer <NUM>-<NUM> may store the trained data determination model in a memory of a server that is connected to the medical imaging apparatus <NUM> via a wired or wireless network.

In this case, the memory that stores the trained data determination model may also store, for example, a command or data related with at least one other component of the medical imaging apparatus <NUM>. The memory may also store software and/or a program. The program may include, for example, a kernel, a middleware, an application programming interface (API), and/or an application program (or an application).

The model evaluator <NUM>-<NUM> may input evaluation data to a data determination model, and when a determination result output from the evaluation data does not satisfy a certain criterion, the model evaluator <NUM>-<NUM> may allow the model trainer <NUM>-<NUM> to train the data determination model again. In this case, the evaluation data may be preset data for evaluating a data determination model.

For example, when the number or a rate of the evaluation data whose determination result is not correct is greater than a preset threshold value, the determination result being from among determination results of the data determination model trained with respect to the evaluation data, the model evaluator <NUM>-<NUM> may evaluate that the certain criterion is not satisfied. For example, in a case where the certain criterion is defined as a rate of <NUM> %, when the trained data determination model outputs incorrect determination results for at least <NUM> items of evaluation data among a total of <NUM> items of evaluation data, the model evaluator <NUM>-<NUM> may evaluate that the trained data determination model is not appropriate.

When there are a plurality of trained data determination models, the model evaluator <NUM>-<NUM> may evaluate whether each of trained data determination models satisfies the certain criterion, and may determine a model to be a final data determination model, the model satisfying the certain criterion. In this case, when the model satisfying the certain criterion is plural in number, the model evaluator <NUM>-<NUM> may determine, as the final data determination model, one model or a certain number of models which are preset according to their respective high evaluation scores.

At least one of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the training data selector <NUM>-<NUM>, the model trainer <NUM>-<NUM>, and the model evaluator <NUM>-<NUM> in the data trainer <NUM> may be embodied as at least one hardware chip and may be mounted in the medical imaging apparatus <NUM>. For example, at least one of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the training data selector <NUM>-<NUM>, the model trainer <NUM>-<NUM>, and the model evaluator <NUM>-<NUM> may be embodied as a dedicated hardware chip for AI, or may be embodied as a part of a general-use processor (e.g., a CPU or an application processor) or a graphic-dedicated processor (e.g., a GPU and may be mounted in the medical imaging apparatus <NUM>.

In this case, the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the training data selector <NUM>-<NUM>, the model trainer <NUM>-<NUM>, and the model evaluator <NUM>-<NUM> may be mounted together in one apparatus, or may be embodied separately in respective apparatuses. For example, some of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the training data selector <NUM>-<NUM>, the model trainer <NUM>-<NUM>, and the model evaluator <NUM>-<NUM> may be included in the medical imaging apparatus <NUM> and the others may be included in a server.

At least one of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the training data selector <NUM>-<NUM>, the model trainer <NUM>-<NUM>, and the model evaluator <NUM>-<NUM> may be embodied as a software module. When at least one of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the training data selector <NUM>-<NUM>, the model trainer <NUM>-<NUM>, and the model evaluator <NUM>-<NUM> is embodied as a software module (or a program module including instructions), the software module may be stored in a non-transitory computer-readable recording medium. In this case, one or more software modules may be provided by an OS or a certain application. Alternatively, some of the one or more software modules may be provided by the OS and the rest of the one or more software modules may be provided by a certain application.

<FIG> is a block diagram illustrating the data determiner <NUM> according to an embodiment.

Referring to <FIG>, the data determiner <NUM> according to an embodiment may include a data obtainer <NUM>-<NUM>, a pre-processor <NUM>-<NUM>, a determination data selector <NUM>-<NUM>, a determination result provider <NUM>-<NUM>, and a model updater <NUM>-<NUM>.

The data obtainer <NUM>-<NUM> may obtain data necessary for determination of situation, and the pre-processor <NUM>-<NUM> may pre-process the obtained data to make the obtained data used in determination of situation. The pre-processor <NUM>-<NUM> may process the obtained data to have a preset format so as to allow the determination result provider <NUM>-<NUM> to use the obtained data for determination of situation.

The determination data selector <NUM>-<NUM> may select, from among the pre-processed data, data necessary for determination of situation. The selected data may be provided to the determination result provider <NUM>-<NUM>. The determination data selector <NUM>-<NUM> may select all or some of the pre-processed data, according to a preset criterion for determination of situation. Also, the determination data selector <NUM>-<NUM> may select data according to a criterion that is preset via training by the model trainer <NUM>-<NUM> described above.

The determination result provider <NUM>-<NUM> may determine a situation by applying the selected data to a data determination model. The determination result provider <NUM>-<NUM> may provide a determination result according to a determination objective with respect to the data. The determination result provider <NUM>-<NUM> may apply the selected data to the data determination model by using, as an input value, the data selected by the determination data selector <NUM>-<NUM>. Also, the determination result may be determined by the data determination model.

The model updater <NUM>-<NUM> may allow the data determination model to be updated, based on evaluation of the determination result provided by the determination result provider <NUM>-<NUM>. For example, the model updater <NUM>-<NUM> may allow the data determination model to be updated by the model trainer <NUM>-<NUM> by providing, to the model trainer <NUM>-<NUM>, the determination result provided by the determination result provider <NUM>-<NUM>.

At least one of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the determination data selector <NUM>-<NUM>, the determination result provider <NUM>-<NUM>, and the model updater <NUM>-<NUM> in the data determiner <NUM> may be embodied as at least one hardware chip and may be mounted in the medical imaging apparatus <NUM>. For example, at least one of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the determination data selector <NUM>-<NUM>, the determination result provider <NUM>-<NUM>, and the model updater <NUM>-<NUM> may be embodied as a dedicated hardware chip for AI, or may be embodied as a part of a general-use processor (e.g., a CPU or an application processor) or a graphic-dedicated processor (e.g., a GPU and may be mounted in the medical imaging apparatus <NUM>.

In this case, the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the determination data selector <NUM>-<NUM>, the determination result provider <NUM>-<NUM>, and the model updater <NUM>-<NUM> may be mounted together in one apparatus, or may be embodied separately in respective apparatuses. For example, some of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the determination data selector <NUM>-<NUM>, the determination result provider <NUM>-<NUM>, and the model updater <NUM>-<NUM> may be included in the medical imaging apparatus <NUM> and the others may be included in a server.

At least one of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the determination data selector <NUM>-<NUM>, the determination result provider <NUM>-<NUM>, and the model updater <NUM>-<NUM> may be embodied as a software module. When at least one of the data obtainer <NUM>-<NUM>, the pre-processor <NUM>-<NUM>, the determination data selector <NUM>-<NUM>, the determination result provider <NUM>-<NUM>, and the model updater <NUM>-<NUM> is embodied as a software module (or a program module including instructions), the software module may be stored in a non-transitory computer-readable recording medium. In this case, one or more software modules may be provided by an OS or a certain application. Alternatively, some of the one or more software modules may be provided by the OS and the rest of the one or more software modules may be provided by a certain application.

<FIG> illustrates an example of managing training data, according to an embodiment.

According to an embodiment, the medical imaging apparatus <NUM> is configured to independently manage training data and an optimization coefficient with respect to a preset feature value. The preset feature value may be at least one of a user feature, a use environment feature, an apparatus feature, a patient feature, and a scan control feature. For example, the medical imaging apparatus <NUM> is configured to independently manage an optimization coefficient according to each of users. To do so, the medical imaging apparatus <NUM> ins configured to independently manage training data according to each of users, and is configured to calculate an optimization coefficient based on training data of each of users. When the optimization coefficient and the training data are managed according to each of users, the optimization coefficient and the training data may be separately managed according to user identification information.

The medical imaging apparatus <NUM> is configured to manage the training data and the optimization coefficient by allocating first and second work areas <NUM> and <NUM> for the preset feature value to the storage <NUM>, the first and second work areas <NUM> and <NUM> being independent from each other. <FIG> illustrates training data <NUM> where an estimated parameter value and a user-designated parameter value in response to a first control signal are matched. According to an embodiment, the medical imaging apparatus <NUM> may allocate a main work area <NUM>, the first work area <NUM>, and the second work area <NUM> to the storage <NUM>. The main work area <NUM> may store and manage the training data <NUM> and an optimization coefficient <NUM> regardless of a feature value. The first work area <NUM> may store and manage training data <NUM> and an optimization coefficient <NUM> for a first user. The second work area <NUM> may store and manage training data <NUM> and an optimization coefficient <NUM> for a second user. In response to an input of a first control signal with respect to the first user, the processor <NUM> stores corresponding training data in the first work area <NUM>, and updates the optimization coefficient <NUM>, based on the training data in the first work area <NUM>. In response to an input of the first control signal with respect to the second user, the processor <NUM> stores corresponding training data in the second work area <NUM>, and updates the optimization coefficient <NUM>, based on the training data in the second work area <NUM>. The main work area <NUM> may include pre-trained data and new training data, and the first work area <NUM> and the second work area <NUM> may each include new training data.

<FIG> is a flowchart of a medical imaging apparatus control method according to an embodiment.

The medical imaging apparatus control method according to the present embodiment may be performed by one of various medical imaging apparatuses. In the present embodiment, it is assumed that the medical imaging apparatus <NUM> according to the one or more embodiments performs the medical imaging apparatus control method, but the present embodiment is not limited thereto. The one or more embodiments of the medical imaging apparatus <NUM> which are disclosed in the present disclosure may be applied to the medical imaging apparatus control method, and one or more embodiments of the medical imaging apparatus control method may be applied to the medical imaging apparatus <NUM>.

The medical imaging apparatus <NUM> identifies a feature value from a medical image (S1202). The medical imaging apparatus <NUM> may obtain an image feature value from the medical image.

Next, the medical imaging apparatus <NUM> identifies a parameter value based on the image feature value and an optimization coefficient, by using the neural network processor <NUM> (e.g., a deep neural network processor) (S1204).

The medical imaging apparatus <NUM> outputs a resultant medical image generated based on the identified parameter value (S1206). For example, the medical imaging apparatus <NUM> may display the resultant medical image or may transmit the resultant medical image to an external apparatus via the communicator.

The medical imaging apparatus <NUM> receives a first control input of adjusting the parameter value (S1208), and may set a parameter value based on the first control input.

Also, the medical imaging apparatus <NUM> updates the optimization coefficient by using the parameter value as training data, the parameter value being set in response to the first control input (S1210).

According to the embodiments, it is possible to improve a function of automatically setting parameters in a medical imaging apparatus.

Also, according to the embodiments, it is possible to automatically identify an appropriate parameter, in consideration of a user, a use environment, an apparatus used by the user, patient information, a protocol, or the like.

Also, according to the embodiments, it is possible to decrease a need for development manpower, a development period, and development costs of a medical imaging apparatus by simplifying initial settings of the medical imaging apparatus.

The embodiments may be implemented in a software program including instructions stored in a computer-readable storage medium.

The computer is a device capable of calling the stored instructions from the storage medium and operating according to the embodiments in accordance with the called instructions, and may include the ultrasound diagnosis apparatus according to the embodiments.

The computer-readable storage medium may be provided in the form of a non-transitory storage medium. Here, the term 'non-transitory' means that the storage medium is tangible and does not refer to a transitory electrical signal, but does not distinguish that data is stored semi-permanently or temporarily on the storage medium.

Furthermore, the medical imaging apparatus and the medical imaging apparatus control method according to the embodiments may be provided in a computer program product. The computer program product may be traded between a seller and a purchaser as a commodity.

The computer program product may include a software program and a computer-readable storage medium having stored thereon the software program. For example, the computer program product may include a product (e.g. a downloadable application) in a software program distributed electronically through a manufacturer of the medical imaging apparatus or an electronic market (e.g., Google Play Store and App Store). For electronic distribution, at least a part of the software program may be stored on the storage medium or may be generated temporarily. In this case, the storage medium may be a storage medium of a server of the manufacturer, a server of the electronic market, or a relay server for temporarily storing the software program.

The computer program product may include a storage medium of a server or a storage medium of a terminal, in a system including the server and the terminal (e.g., the ultrasound diagnosis apparatus). Alternatively, when there is a third device (e.g., a smartphone) that communicates with the server or the terminal, the computer program product may include a storage medium of the third device. Alternatively, the computer program product may include a software program that is transmitted from the server to the terminal or the third device or from the third device to the terminal.

In this case, one of the server, the terminal, and the third device may perform the method according to the embodiments by executing the computer program product. Alternatively, at least two of the server, the terminal, and the third device may divide and perform the method according to the embodiments by executing the computer program product.

For example, the server (e.g., a cloud server, an AI server, or the like) may execute the computer program product stored in the server, thereby controlling the terminal to perform the method according to the embodiments, the terminal communicating with the server.

As another example, the third device may execute the computer program product, thereby controlling the terminal to perform the method according to the embodiments, the terminal communicating with the terminal. In detail, the third device may remotely control the ultrasound diagnosis apparatus, thereby controlling the ultrasound diagnosis apparatus to irradiate an ultrasound signal to an object and to generate an image of an inside part of the object, based on information of a signal reflected from the object,.

As another example, the third device may directly perform, by executing the computer program product, the method according to the embodiments based on a value input from an auxiliary device (e.g., a probe of a medical apparatus). In detail, the auxiliary device may irradiate an ultrasound signal to an object and may obtain an ultrasound signal reflected from the object. The third device may receive an input of signal information about the reflected ultrasound signal from the auxiliary device, and may generate an image of an inside part of the object, based on the input signal information.

Claim 1:
A medical imaging apparatus (<NUM>) comprising:
a storage (<NUM>) configured to store training data and an optimization coefficient with respect to at least one parameter, the training data being pre-trained data and new training data;
at least one processor (<NUM>) configured to identify at least one image feature value from an input medical image, and to identify a value of at least one parameter of the medical imaging apparatus, based on the at least one image feature value and the optimization coefficient, by using a neural network processor (<NUM>);
an output interface (<NUM>) configured to output a resultant image generated based on the value of the at least one parameter; and
an input interface (<NUM>) configured to receive a first control input of adjusting the value of the at least one parameter,
wherein the at least one parameter indicates a parameter for setting and processing components of medical imaging data,
wherein the at least one processor (<NUM>) is further configured to estimate the optimization coefficient by the training data and update the optimization coefficient by performing training using the training data and the first control input, and
wherein the at least one processor (<NUM>) is further configured to manage the training data and the optimization coefficient with respect to a preset feature value, by independently managing the training data according to each of the users and independently manage the optimization coefficient according to each of the users, by allocating first and second work areas for the preset feature value, the first and second work areas being independent from each other, and calculate the optimization coefficient of a first user based on the training data corresponding to the first user.