JP2021007510A - Information processor, information processing method, and program - Google Patents

Abstract

To provide an information processor capable of selecting a medical image to be compared from a plurality of medical images according to a condition related to an imaging date and time.SOLUTION: An information processor 10 which has a selection part 55 selecting a second image to be compared with a first image from a plurality of candidate images includes: an imaging date and time acquisition part 53 acquiring imaging dates and times when the plurality of candidate images were captured; and a condition setting part 54 setting a condition related to the imaging dates and times. The selection part 55 selects at least one candidate image as a second image from the plurality of candidate images on the basis of the condition related to the imaging dates and times set by the condition setting part 54 and the imaging dates and times of the plurality of candidate images.SELECTED DRAWING: Figure 1

Description

The present invention relates to an information processing apparatus, an information processing method and a program for processing medical images captured by various modality.

In the medical field, doctors make a diagnosis using medical images taken by various modality. In particular, in order to follow up the state of the subject, a doctor compares a plurality of medical images taken at different times with the same modality to observe changes in the subject over time. As a method of supporting the observation of changes over time of a subject, in Patent Document 1, a medical image (difference) depicting a difference between a medical image to be diagnosed (diagnosis target image) and a medical image to be compared (comparison image). A technique for generating an image) and displaying a change over time of a subject is disclosed.

Japanese Unexamined Patent Publication No. 2013-126575

However, when observing changes over time of a subject using a plurality of medical images, it is necessary to select an appropriate comparative image from a plurality of medical images captured at different time points.

Therefore, an object of the present invention is to provide an information processing device capable of selecting a medical image to be compared from a plurality of medical images depending on conditions related to the imaging date and time.

In order to achieve the object of the present invention, an information processing apparatus having a selection means for selecting a second image to be compared with the first image from a plurality of candidate images has captured a plurality of candidate images. It has an acquisition means for acquiring an imaging date and time and a condition setting means for setting conditions related to the imaging date and time, and the selection means is based on the conditions related to the imaging date and time set by the condition setting means and the imaging date and time of a plurality of candidate images. , At least one candidate image is selected as the second image from the plurality of candidate images.

According to the present invention, a medical image to be compared can be selected from a plurality of medical images depending on the conditions relating to the imaging date and time.

The figure which shows the structure of the information processing apparatus of Example 1 of this invention. The figure which shows the condition about the imaging date and time by the condition setting part of this invention. The flowchart which shows the operation of Example 1 of this invention. The figure which shows the structure of the information processing apparatus of Example 2 of this invention. The flowchart which shows the operation of Example 2 of this invention. The figure which shows an example of the relationship between the imaging range of the image to be diagnosed and the imaging range of a candidate image of the present invention. The figure which shows an example of the relationship between the imaging range of the image to be diagnosed and the imaging range of a candidate image of the present invention. The figure which shows an example of the relationship between the imaging range of the image to be diagnosed and the imaging range of a candidate image of the present invention. The figure which shows an example of the relationship between the imaging range of the image to be diagnosed and the imaging range of a candidate image of the present invention.

Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings.

The information processing apparatus 10 of the first embodiment will be described with reference to FIGS. 1 to 3. The information processing device 10 according to the embodiment of the present invention provides the following functions to users such as doctors and radiologists in medical institutions. That is, the information processing device 10 performs a comparison process between the diagnosis target image and the comparison image of the patient (subject) for which the user intends to perform medical treatment, and provides the user with the result of the comparison process.

In this embodiment, a case where there are a plurality of medical images (candidate images) in which the same subject is imaged at a time different from the time when the image to be diagnosed is imaged will be described. The information processing device 10 selects a medical image suitable for comparison with the diagnosis target image from a plurality of candidate images as a comparison image, and performs a comparison process between the diagnosis target image and the comparison image. Here, the comparison process is, for example, to display each medical image so that the user can compare it, or to perform a difference process between the diagnosis target image and the candidate image and display the difference image.

The information processing apparatus 10 is a subject by comparing a medical image captured at the present time or the latest medical image (diagnosis target image) with a medical image (comparative image) captured in the past than the diagnosis target image. Depict the development and progression of lesions.

The information processing device 10 acquires the imaging date and time when a plurality of candidate images are captured, and sets conditions related to the imaging date and time. Then, a comparison image is selected from the plurality of candidate images based on the set conditions and the imaging date and time of the plurality of candidate images. Then, the information processing device 10 generates a difference image that depicts the difference between the image to be diagnosed and the comparison image, and displays the change over time of the subject on the display unit 36. In the following description, the imaging date and time shall include not only information representing both the imaging date and the imaging time, but also information representing only the imaging date. In addition, it shall include information indicating only the imaging time. For example, it is assumed to include information recording only the elapsed time from a predetermined time as a starting point (for example, 0:00:00 on January 1, 1970). The imaging date is generally the imaging date, but may be information representing only the imaging date.

FIG. 1 is a diagram showing the overall configuration of the medical information processing system of the present invention and the configuration of the information processing device 10. The medical information processing system includes an information processing device 10 and a medical image database 23. The information processing device 10 and the medical image database 23 are communicably connected to each other via a communication means. In this embodiment, the communication means is composed of a LAN (Local Area Network) 21, but may be a WAN (Wide Area Network). Further, the connection method of the communication means may be a wired connection or a wireless connection.

The medical image database 23 holds a plurality of medical images related to a plurality of subjects and incidental information thereof. The medical image is an image captured by a modality such as a CT device or an MRI device, and various images such as two-dimensional, three-dimensional, monochrome, and color can be targeted. The incidental information of the medical image is information such as a subject ID (patient ID), an imaging date and time, an imaging site, a reconstruction condition, and an imaging condition such as a tube voltage value and a tube current value. In addition to this, the purpose of the test, the name of the organ to be tested, the name of the disease to be tested, and the like can be incidental information.

A unique identifier (inspection ID) is attached to each medical image and its incidental information in order to enable identification from other information, and the information processing device 10 reads out the information based on the inspection ID. Will be. When the medical image is a three-dimensional volume image composed of a plurality of two-dimensional cross-sectional images (tomographic images), each of the two-dimensional cross-sectional images (tomographic images) and the three-dimensional volume image which is a set of tomographic images On the other hand, an inspection ID is attached. In addition to reading information, the medical image database 23 provides functions such as list display of medical images, thumbnail display, search, and writing of information in cooperation with the information processing device 10. In this embodiment, the medical image refers to medical image data.

The information processing device 10 acquires the information held by the medical image database 23 via the LAN 21. The information processing device 10 includes a communication IF 31, a ROM 32, a RAM 33, a storage unit 34, an operation unit 35, a display unit 36, and a control unit 50.

The communication IF (Interface) 31 is realized by, for example, a LAN card or the like, and communicates between the external device (medical image database 23) and the information processing device 10 via the LAN 21. The ROM (Read Only Memory) 32 is realized by a non-volatile memory or the like, and stores various programs or the like. The RAM (Random Access Memory) 33 is realized by a volatile memory or the like, and temporarily stores various information. The storage unit 34 is realized by, for example, an HDD (Hard Disk Drive) or the like, and stores various information. The operation unit 35 is realized by, for example, a keyboard or a mouse, and the user can input various information via the operation unit 35. The display unit 36 is, for example, a display. The operation unit 35 and the display unit 36 have a function as a graphical user interface (GUI) under the control of the control unit 50.

The control unit 50 is realized by, for example, a CPU (Central Processing Unit), and controls the processing in the information processing apparatus 10 in an integrated manner. The control unit 50 includes a diagnosis target image acquisition unit 51, a candidate image acquisition unit 52, an imaging date / time acquisition unit 53, a condition setting unit 54, a selection unit 55, an image processing unit 56, and a display control unit 57.

The diagnosis target image acquisition unit 51 acquires a medical image of a subject as a diagnosis target image from the medical image database 23 via the communication IF 31 and LAN 21 according to the user's operation input by the operation unit 35. Then, the diagnosis target image acquisition unit 51 outputs the acquired diagnosis target image to the image processing unit 56 together with the incidental information.

The candidate image acquisition unit 52 uses the communication IF 31 and LAN 21 as medical images (candidate images) that are candidates for comparison images with respect to the image to be diagnosed acquired by the image acquisition unit 51 to obtain medical images of the same subject in the past. A plurality of images are acquired from the medical image database 23 via the above. Then, the candidate image acquisition unit 52 outputs the incidental information of the acquired plurality of candidate images to the imaging date / time acquisition unit 53, and outputs the plurality of candidate images to the selection unit 55.

The imaging date / time acquisition unit 53 acquires the imaging date / time of a plurality of candidate images. Since the incidental information given to the plurality of candidate images includes the imaging date and time, the imaging date / time acquisition unit 53 reads the incidental information attached to the plurality of candidate images to obtain the plurality of candidate images. It is possible to acquire the imaging date and time of.

The condition setting unit 54 sets conditions related to the imaging date and time in order to select a medical image (comparison image) to be compared from a plurality of candidate images. Specifically, the condition setting unit 54 sets the imaging date and time based on the imaging date and time of the image to be diagnosed.

FIG. 2 is an example showing conditions related to the imaging date and time by the condition setting unit 54. The imaging date and time includes the date and time when the subject was imaged, but here, for the sake of brevity, the date will be used. The condition setting unit 54 can set, for example, the latest imaging date and time (the past imaging date and time closest to the imaging date and time of the image to be diagnosed) as a condition related to the imaging date and time. According to this, it is possible to make a comparison with the latest examination, which is the basis of comparative interpretation. Under this condition, the selection unit 55 selects the candidate image corresponding to the latest imaging date and time as the comparison image. In the example of FIG. 2, the imaging date and time of the image to be diagnosed is April 15, 2019. Among the plurality of candidate images 1 to 5, the imaging date and time of the candidate image 5 is 2018/10/20, which is the newest candidate image of the imaging date and time, so the candidate image 5 is selected by the selection unit 55. The selection unit 55 outputs the selected candidate image 5 as a comparison image to the image processing unit 56. Further, the oldest imaging date and time (the past imaging date and time farthest from the imaging date and time of the image to be diagnosed) can be set as a condition regarding the imaging date and time. According to this, the image in which the most change occurs from the image to be diagnosed can be selected as the comparison image. Under this condition, the selection unit 55 selects the candidate image corresponding to the oldest imaging date and time as the comparison image.

Further, the condition setting unit 54 can set the imaging date and time within a predetermined period as the condition related to the imaging date and time based on the imaging date and time of the image to be diagnosed. For example, the lower limit of the period can be set as a predetermined period. For example, it can be set on condition that the examination is separated from the imaging date and time of the image to be diagnosed by a predetermined period (for example, one year) or more. According to this, when it is known that it takes a certain amount of time for the lesion of interest to occur, a candidate image of a test performed at an appropriate period can be selected as a comparative image. Further, it is possible to set the latest imaging date and time as an additional condition regarding the imaging date and time within a predetermined period. According to this, it is possible to select as a comparison image a candidate image for an examination that is closest to the image to be diagnosed and has no extra change while satisfying the condition of the period.

On the contrary, the upper limit of the period can be set as a predetermined period. For example, as shown in FIG. 2A, the condition setting unit 54 can set a period of, for example, within 2 years based on the imaging date / time 2019/4/15 of the image to be diagnosed. According to this, it is possible to reduce the increase in noise caused by the fact that even changes that have occurred in the past appear in the difference image because the period is too far from the comparison image. Alternatively, as a predetermined period, a period having an upper limit and a lower limit such as one year or more and less than two years can be set. When a predetermined period is set as a condition, the selection unit 55 selects a candidate image corresponding to the imaging date and time within the predetermined period as a comparison image. In the example of FIG. 2A, since the imaging date and time of the candidate image 4 is 2018/9/5 and the imaging date and time of the candidate image 5 is 2018/10/20 among the plurality of candidate images, the selection unit 55 Selects the candidate image 4 and the candidate image 5. The selection unit 55 outputs the selected candidate image 4 and the candidate image 5 to the image processing unit 56 as comparison images. Further, it is possible to set the earliest imaging date and time as an additional condition regarding the imaging date and time within a predetermined period. According to this, it is possible to select the test that changes the most among the conditions of the period. In this case, in the example of FIG. 2A, the candidate image 4 is selected as the comparison image.

The condition setting unit 54 may be able to set a plurality of conditions having different priorities as conditions relating to the imaging date and time. For example, when there is no candidate image satisfying the condition of the first priority, a plurality of stages of conditions can be set such that a candidate image satisfying the condition of the second priority is selected. For example, the first priority condition can be that the imaging date and time is 1 year or more and less than 2 years, and the second priority condition can be 2 years or more and less than 3 years. Further, an arbitrary number of three or more conditions may be set with priority, such as setting 3 years or more and less than 4 years as the condition of the third priority. According to this, even when a comparison image with ideal conditions does not exist, a comparison image satisfying the next best condition can be selected, so that there is an effect that cases where the images cannot be compared can be reduced.

Further, the condition setting unit 54 can set an imaging date and time as a condition relating to the imaging date and time so that a predetermined treatment such as surgery is not inserted between the imaging date and time of the image to be diagnosed. Under this condition, the selection unit 55 selects as a comparison image a candidate image corresponding to the imaging date and time that does not sandwich a predetermined treatment such as surgery between the imaging date and time of the image to be diagnosed.

When treatment such as surgery is performed between the imaging date and time of the candidate image and the imaging date and time of the diagnosis target image, the pixel values around the treatment site in the candidate image and the diagnosis target image are significantly different, and the candidate image is not suitable for comparison. FIG. 2B shows that the operation was performed at the imaging date and time of the candidate image 4 and the imaging date and time of the candidate image 5. Since the imaging date and time of the candidate image 5 among the plurality of candidate images is a candidate image of the imaging date and time that does not sandwich treatment such as surgery between the imaging dates and times of the diagnosis target image, the candidate image 5 is selected by the selection unit 55. The selection unit 55 outputs the selected candidate image 5 as a comparison image to the image processing unit 56. It should be noted that the condition regarding the imaging date and time may be that the imaging date and time is as old as possible without interposing a predetermined treatment such as surgery. According to this, the image in which the most change occurs from the image to be diagnosed can be selected as the comparison image within the range where no major change such as surgery occurs.

Further, the condition setting unit 54 can set as new an imaging date and time as possible as a condition regarding the imaging date and time before starting a predetermined treatment. In the case of this condition, the selection unit 55 selects a candidate image of an earlier and newest imaging date and time as a comparison image based on the specified predetermined treatment date and time. According to this, an appropriate comparative image can be selected when it is desired to observe the change due to the effect of the treatment.

Further, the condition setting unit 54 can also set an imaging date and time in which the modality model has not been changed between the imaging date and time of the image to be diagnosed as a condition related to the imaging date and time.

Under this condition, the selection unit 55 selects as a comparison image a candidate image corresponding to the imaging date and time for which the modality model has not been changed between the imaging date and time of the image to be diagnosed.

This is because if the modality model is changed between the imaging date and time of the candidate image and the imaging date and time of the diagnosis target image, the image quality of the candidate image and the diagnosis target image is significantly different, and the candidate image is not suitable for comparison. FIG. 2C shows that the modality model was changed at the imaging date and time of the candidate image 3 and the imaging date and time of the candidate image 4. Among the plurality of candidate images, the imaging date and time of the candidate image 4 and the imaging date and time of the candidate image 5 are candidate images of the imaging date and time without interposing the model change of the modality between the imaging date and time of the image to be diagnosed. Image 4 or candidate image 5 is selected. The selection unit 55 outputs the selected candidate image 4 or the candidate image 5 to the image processing unit 56 as a comparison image. It should be noted that the condition regarding the imaging date and time may be that the modality has not been changed and the imaging date and time is as old as possible. According to this, the image in which the most change occurs from the image to be diagnosed can be selected as the comparison image within the range in which a large change does not occur due to the model change of the modality. In addition, although the modality model change is described here as an example, if it is an event that affects the image quality, such as a change in the imaging protocol or a version upgrade of the reconstruction algorithm, the date and time of any event. May be used as a condition.

The conditions relating to the imaging date and time may be a combination of the above conditions.

Here, in general, since a plurality of images are captured in the inspection of the same imaging date and time, one comparative image may not be specified from the candidate images only by the condition of the imaging date and time. Further, even when there are a plurality of tests that satisfy the conditions of the imaging date and time, one comparison image cannot be identified from the candidate images only by the conditions of the imaging date and time. In such a case, the selection unit 55 may be configured to further narrow down the comparison image by setting conditions other than the imaging date and time. In this case, the condition setting unit 54 can select a comparison image from the candidate images satisfying the conditions of the imaging date and time under conditions other than the imaging date and time (imaging range, imaging site, imaging conditions, etc.). If it is an imaging site, the lung field, abdomen, head, and the like can be set. For example, the condition setting unit 54 can set conditions for selecting a candidate image of the same imaging region as the imaging region of the image to be diagnosed. For example, if the image to be diagnosed is an image of the abdomen, the condition setting unit 54 sets the imaging site on the condition that the abdomen is the abdomen, and the selection unit 55 selects the candidate image of the abdomen as the comparison image. Similarly, if the image to be diagnosed is an image of the head, the condition setting unit 54 sets the imaging site on the condition that the image is the head, and the selection unit 55 selects the candidate image of the head as the comparison image. To do. Similarly, the selection condition may be that the test purpose, the name of the disease to be tested, and the like match the image to be diagnosed.

Further, if it is an imaging condition, a reconstruction condition, a slice thickness, a contrast condition (presence or absence of a contrast agent), and the like can be set as selection conditions for a comparative image. For example, the condition setting unit 54 can set that the image has imaging conditions suitable for the difference processing as a condition for selecting the comparison image. For example, the slice thickness can be set as 5 mm or less, the reconstruction function can be set as a mediastinal condition, non-contrast, a range of tube voltage and tube current, and the like. Further, the condition setting unit 54 can set that the image is captured under the same imaging conditions as the imaging condition of the image to be diagnosed as a condition for selecting the comparison image. For example, if the slice thickness of the image to be diagnosed is 2.5 mm, the condition setting unit 54 sets that the slice thickness is 2.5 mm as a condition for selecting a comparison image from the candidate images. At this time, the selection unit 55 selects a candidate image having a slice thickness of 2.5 mm as a comparison image from the plurality of candidate images. Similarly, it is possible to set that the image to be diagnosed and the reconstruction function, the tube voltage, and the tube current match, or that the difference is within a predetermined range, as a condition for selecting the comparison image. .. Moreover, you may set the condition which combined a plurality of items. In this case, for example, a comprehensive evaluation value based on the product or sum of each item can be calculated based on the degree of matching of each item, and a candidate image giving the best evaluation value can be selected as a comparison image. Alternatively, all candidate images whose evaluation values exceed a predetermined threshold value can be selected as comparison images. In the above example, a case where a comparison image is selected from candidate images satisfying the shooting date and time according to conditions other than the shooting date and time has been described, but the order in which the conditions are applied is limited to this. is not it. For example, the candidate images may be narrowed down according to conditions other than the shooting date and time, and then the comparison image may be selected based on the shooting date and time conditions. In particular, when the conditions of the imaging date and time are "oldest" or "newest", it is desirable to narrow down by conditions other than the shooting date and time first. For example, it is possible to set conditions such as selecting the candidate image having the oldest imaging date and time or selecting the candidate image having the latest imaging date and time from the candidate images satisfying conditions other than the shooting date and time. According to this, from among the candidate images that satisfy conditions other than the imaging date and time, according to the purpose (for example, want to see the changes that have occurred in the patient so far, or want to see the changes from the previous same-purpose examination). You can make a comparison.

Then, the image processing unit 56 performs comparison processing (difference processing) between the diagnosis target image and the comparison image (selected candidate image), and outputs the comparison result of the comparison processing to the display control unit 57. Specifically, the image processing unit 56 generates a difference image that depicts the difference between the diagnosis target image and the comparison image. The display processing unit 57 causes the display unit 36 to display the comparison result (difference image).

At least a part of each unit included in the control unit 50 may be realized as an independent device. In addition, each may be realized as software that realizes a function. In this case, the software that realizes the function may operate on a server via a network such as the cloud. In this embodiment, it is assumed that each part is realized by software in the local environment.

Further, the configuration of the information processing apparatus 10 shown in FIG. 1 is merely an example. For example, the storage unit 34 of the information processing device 10 may have the function of the medical image database 23, and the storage unit 34 may hold a plurality of medical images related to a plurality of subjects and their incidental information.

Next, the operation by the information processing apparatus 10 in this embodiment will be described in detail with reference to FIG. In the following, a case where a CT image is used as a medical image will be described as an example, but the implementation of the present invention is not limited to this. Further, the image to be diagnosed and the candidate image do not necessarily have to be images of the same subject and the same modality.

(Step S1010) <Acquisition of image to be diagnosed>
In this step, the diagnosis target image acquisition unit 51 executes a process of acquiring the diagnosis target image (first image) to be processed from the medical image database 23. This process is executed by accepting an operation from the user by the GUI provided by the operation unit 35 and the display unit 36. By this process, the diagnosis target image acquisition unit 51 reads the diagnosis target image from the medical image database 23, and the information processing device 10 holds the diagnosis target image using the ROM 32, the RAM 33, the storage unit 34, and the like. In this embodiment, a case where a three-dimensional CT image composed of a plurality of tomographic images is acquired as a diagnosis target image will be described as an example. However, the implementation of the present invention is not limited to this, and may be a two-dimensional CT image, a simple X-ray image, an MRI image, a camera image of a lesion portion of a subject, or the like. The image to be diagnosed is given additional information such as a subject ID (patient ID), an imaging date and time, an imaging site, and imaging parameters.

(Step S1020) <Acquisition of a plurality of candidate images>
In this step, the candidate image acquisition unit 52 executes a process of acquiring a plurality of medical images (candidate images) as candidates for comparison with the diagnosis target image acquired in step S1010 from the medical image database 23. As an example of this process, the candidate image acquisition unit 52 acquires a medical image of the same subject as the diagnosis target image as a candidate image from a plurality of medical images recorded in the medical image database 23. Specifically, the candidate image acquisition unit 52 uses the search function provided in the medical image database 23 to obtain a medical image having the same subject ID as the subject ID, which is the incidental information of the image to be diagnosed, as the incidental information. get.

The method of acquiring the candidate image is not limited to the above method. For example, the candidate image acquisition unit 52 may refer to the imaging date and time of the image to be diagnosed and acquire only the older (past) medical image. In this embodiment, it is a medical image of the same subject imaged at a time earlier than the time when the image to be diagnosed is captured by the above method, and has the same modality as the image to be diagnosed (CT in the specific example of this example). Candidate images shall be acquired only for medical images captured by the device).

(Step S1030) <Acquisition of imaging dates and times of a plurality of candidate images>
In this step, the imaging date / time acquisition unit 53 acquires the imaging date / time of a plurality of candidate images acquired by the candidate image acquisition unit 52. The imaging date and time recorded together with the candidate image is recorded as incidental information in the header area of each candidate image. The imaging date / time acquisition unit 53 acquires the imaging date / time of the plurality of candidate images by reading the incidental information of the plurality of candidate images. Here, the imaging date / time acquisition unit 53 may acquire the date (inspection date) of the inspection in which the candidate image was acquired and use it as the imaging date / time of each candidate image acquired in the inspection. Further, information that correlates with the imaging date and time, such as the date when the candidate image is reconstructed or the date when the image is first read, may be substituted as the imaging date and time.

(Step S1035) <Set conditions related to imaging date and time>
In this step, the condition setting unit 54 sets conditions related to the imaging date and time for selecting a comparison image from a plurality of candidate images. For example, the conditions are set by having the user select the conditions from the variations of the conditions related to the imaging date and time described above via a GUI (not shown). Alternatively, the configuration may be such that the conditions described in advance in the setting file are read. It is desirable that the condition setting unit 54 also sets conditions other than the imaging date and time in this step.

(Step S1040) <Select a comparison image>
In this step, the selection unit 55 selects a comparison image from a plurality of candidate images based on the conditions regarding the imaging date and time set by the condition setting unit 54 and the imaging date and time of the plurality of candidate images.

In the process by the selection unit 55, it is not always necessary to narrow down the comparison images to one, and a plurality of comparison images may be selected. In this case, the user may select the comparison image to be output to the image processing unit 56 from the plurality of comparison images selected by the selection unit 55 via a GUI (not shown). Further, the configuration may be such that all of the selected plurality of comparative images are output to the image processing unit 56.

(Step S1050) <Comparison process>
In this step, the image processing unit 56 executes a comparison process between the image to be diagnosed and the comparison image. Specifically, the image processing unit 56 performs difference processing between the images to be diagnosed and the comparison image, and calculates the difference image as the comparison result. Here, when a plurality of comparison images are selected by the selection unit 55, the image processing unit 56 generates a difference image between the diagnosis target image and the comparison image for each comparison image. That is, the image processing unit 56 generates a difference image between the first image and the second image by comparing the first image which is the diagnosis target image and the second image which is the comparison image. The difference processing between the image to be diagnosed and the comparison image corrects the difference in the image that is unnecessary for image diagnosis, such as the difference in the position and shape of the subject drawn in the image to be diagnosed and the comparison image, and the difference in image quality between the images. It is desirable to execute after The correction of the difference between the images may be performed by any known method, and detailed description thereof will be omitted here.

The process of calculating the comparison result is not limited to the above difference process. For example, the image processing unit 56 may generate a superposed image for the diagnosis target image and the comparison image. In this case, different color channels can be assigned to the diagnosis target image and the comparison image, and a superimposed image can be generated as a color image in which they are mixed. Further, a deformed image obtained by deforming the comparative image so that the image to be diagnosed and the shape of the subject match may be generated.

(Step S1060) <Display of comparison result>
In this step, the display control unit 57 executes a process of displaying the comparison result obtained by the process of step S1050 on the display unit 36. As a specific example, the display control unit 57 volume-renders each of the diagnosis target image, the comparison image, and the difference image, and displays each of the images created by the volume rendering (hereinafter, volume-rendered image) on the display unit 36. Display side by side.

The method of displaying the comparison result is not limited to the above method, and the display control unit 57 may display the tomographic image at an arbitrary position in each image on the display unit 36 and change the tomographic image to be displayed by the user's operation. The display control unit 57 can specify a corresponding position in each of the diagnosis target image and the comparison image, and perform processing such as displaying a tomographic image of the position (displaying the corresponding cross section). That is, when the tomographic image of the diagnosis target image displayed by the user is changed, the display control unit 57 displays the tomographic image at the position of the comparative image corresponding to the position of the tomographic image on the display unit 36. When the diagnosis target image and the comparison image are aligned in step S1050, the display control unit 57 interlocks the tomographic image to be displayed between the diagnosis target image and the difference image based on the alignment result. It is desirable to perform processing such as switching.

As described above, according to the present invention, the information processing apparatus 10 having the selection unit 55 for selecting the comparison image (second image) to be compared with the diagnosis target image (first image) from a plurality of candidate images. The image pickup date and time acquisition unit 53 for acquiring the imaging date and time for capturing a plurality of candidate images and the condition setting unit 54 for setting the conditions related to the imaging date and time are provided, and the selection unit 55 is set by the condition setting unit 54. At least one candidate image is selected as a comparison image (second image) from the plurality of candidate images based on the conditions regarding the imaging date and time and the imaging date and time of the plurality of candidate images. Therefore, the selection unit 55 can select a candidate image (comparison image) to be compared from a plurality of candidate images depending on the conditions related to the imaging date and time, and the display unit 36 can quickly display the comparison result by the comparison image.

As described above, according to this embodiment, there is an effect that comparison images that can be suitably compared with the images to be diagnosed can be selected and the result of the comparison processing of those images can be provided to the user without requiring the user to perform troublesome operations.

In the above embodiment, the result of the comparison processing between the selected comparison image and the image to be diagnosed is displayed, but the display of the result is not always necessary. For example, the configuration may be such that only the process of associating the result of the comparison process and the information for identifying the comparison image with the image to be diagnosed and storing it in the medical image database 23 may be performed. In this case, when observing the image to be diagnosed using another image viewer, the result of the associated comparison process and each of the comparison images can be read and displayed. Moreover, it is not always necessary to carry out the comparison process. For example, the configuration may be such that only the process of associating the information for identifying the selected comparative image with the image to be diagnosed and storing it in the medical image database 23 may be performed. In this case, when observing the image to be diagnosed using another image viewer, the linked comparison image is read and displayed in a comparable form, or a superimposed image between the image to be diagnosed and the comparison image is displayed. You can generate a difference image.

Next, the information processing apparatus 10 of the second embodiment will be described with reference to FIGS. 4 to 9. The difference from the first embodiment is that a combination of a plurality of candidate images is evaluated and the combination is selected. FIG. 4 is a diagram showing the overall configuration of the medical information processing system of the present invention and the configuration of the information processing device 10. Here, a configuration different from that of FIG. 1 will be mainly described.

The candidate image acquisition unit 52 acquires a plurality of medical images (candidate images) that are candidates for comparison images with respect to the diagnosis target image acquired by the diagnosis target image acquisition unit 51 from the medical image database 23 via the communication IF 31 and LAN 21. Then, the candidate image acquisition unit 52 outputs the acquired plurality of candidate images to the combination generation unit 60 and the selection unit 55.

The combination generation unit 60 generates at least one combination of candidate images acquired by the candidate image acquisition unit 52. Here, the combination of candidate images is a combination of one or a plurality of candidate images. Further, it is assumed that at least one combination of a plurality of (that is, two or more) candidate images is included in the combination of the candidate images generated by the combination generation unit 60. Then, the combination generation unit 60 outputs the combination of the generated candidate images to the evaluation unit 61.

The evaluation unit 61 calculates an evaluation value regarding the appropriateness of comparison with the image to be diagnosed for each combination of the candidate images generated by the combination generation unit 60. Then, the evaluation unit 61 outputs each calculated evaluation value to the selection unit 55.

The selection unit 55 selects a combination of candidate images to be compared with the diagnosis target image from the combinations of candidate images based on each evaluation value. The combination of the selected candidate images is composed of one or more candidate images. Hereinafter, the combination of the selected candidate images is referred to as a set of comparison images, and the candidate images constituting the set of comparison images are referred to as comparison images. Then, the selection unit 55 outputs the set of comparison images to the image processing unit 56.

The image processing unit 56 performs comparison processing between the image to be diagnosed and the set of comparison images, and outputs the comparison result of the comparison processing to the display control unit 57. The display control unit 57 causes the display unit 36 to display the comparison result of the image processing unit 56.

Next, with reference to FIG. 5, the entire processing procedure by the information processing apparatus 10 in this embodiment will be described in detail. Steps S1010, S1020, S1050, and S1060 are substantially the same steps as in FIG.

Here, the candidate image is expressed as I_r, i (1 ≦ i ≦ N). N represents the number of candidate images acquired in step S1020. In this embodiment, the following description will be given with the case of N = 3 as a specific example.

When the number N of the candidate images acquired in this step is 1, the processing of steps S2000 to S2020 described later is omitted, and the acquired candidate images are regarded as a set of comparative images to be described in detail later. The processing after step S1050 may be executed.

(Step S2000) <Generate a combination of candidate images>
In this step, the combination generation unit 60 generates a plurality of combinations of one or more candidate images including at least one combination of two or more candidate images from the N candidate images acquired in step S1020. That is, the combination generation unit 60 corresponds to a setting means for setting a plurality of combinations including two or more candidate images among the plurality of candidate images. In this embodiment, the combination of the candidate images shows an example of being generated by the combination of two different candidate images. In the case of a specific example of N = 3, the following three combinations <I_r, 1, I_r, 2>, <I_r, 1, I_r, 3>, <I_r, 2, I_r, 3> are generated. In this embodiment, the combination of candidate images is expressed as P_j (1 ≦ j ≦ M). Here, M is the total number of combinations. In this embodiment, the following description will be given by taking the case of M = 3 as an example.

(Step S2010) <Calculation of evaluation value>
In this step, the evaluation unit 61 executes a process of calculating an evaluation value for comparison with the diagnosis target image for each combination of candidate images generated in step S2000. In this embodiment, a case where the evaluation value is calculated based on the overlapping range (common imaging range) between the imaging range of the combination of the candidate images to be evaluated and the imaging range of the image to be diagnosed will be described as a specific example.

Here, the process of this step will be described in detail with reference to FIGS. 6 to 9. 6 to 9 are diagrams showing the relationship between the imaging range of the image to be diagnosed and the imaging range of the candidate image. In FIG. 6, 400 is a subject in this example. Reference numeral 410 denotes a range in the body axis direction of the subject 400 in the imaging range of the image to be diagnosed acquired in step S1010. Here, the case where the imaging range of the image to be diagnosed includes the range from the chest to the abdomen of the subject 400 is shown. Further, in FIG. 6, 420, 430, and 440 show the imaging ranges (in the body axis direction) of the candidate images I_r, 1, I_r, 2, and I_r, 3 acquired in step S1020, respectively. Here, I_r and 1 are candidate images of the head of the subject, I_r and 2 are the abdomen, and I_r and 3 are the chest images.

In FIG. 7, 450 indicates the overlapping range between the imaging range (the range of the sum of 420 and 430) of the candidate image combination P_1 (combination of candidate images I_r, 1, I_r, 2) and the imaging range 410 of the image to be diagnosed. Shown. That is, 450 represents a range in which comparison processing between images with the image to be diagnosed can be executed using the combination P_1 of candidate images. FIG. 7 shows that the diagnostic target image and the candidate image I_r, 1 do not overlap in the imaging range, and the diagnostic target image and the candidate image I_r, 2 show that a part of the imaging range overlaps. Therefore, the overlap of the imaging range between the diagnosis target image and the candidate images I_r, 2 is the overlapping range of the combination P_1 of the diagnosis target image and the candidate image.

In FIG. 8, 460 captures the image to be diagnosed with the same expression as in FIG. 7 with respect to the imaging range (the range of the sum of 420 and 440) of the combination P_2 of the candidate images (combination of candidate images I_r, 1, I_r, 3). The overlapping range with the range 410 is shown. FIG. 8 shows that the diagnostic target image and the candidate image I_r, 1 do not overlap in the imaging range, and the diagnostic target image and the candidate image I_r, 3 show that a part of the imaging range overlaps. Therefore, the overlap of the imaging range between the diagnosis target image and the candidate images I_r, 3 is the overlapping range of the combination P_2 of the diagnosis target image and the candidate image.

In FIG. 9, 470 captures the image to be diagnosed with the same expression as in FIG. 7 with respect to the imaging range (the range of the sum of 430 and 440) of the candidate image combination P_3 (combination of candidate images I_r, 2, I_r, 3). The overlapping range with the range 410 is shown. FIG. 9 shows that the imaging range 410 of the image to be diagnosed and a part of the imaging ranges 430 and 440 of the candidate images I_r, 2 and I_r, 3 overlap. In this step, the evaluation unit 61 calculates the size of each range (overlapping range) of 450 in FIG. 7, 460 in FIG. 8, and 470 in FIG. 9, and combines candidate images based on the size of the overlapping range. The evaluation value E_j (1 ≦ j ≦ M) is calculated for each of the above. The evaluation value E_j may be the length of the subject 400 in the overlapping range in the body axis direction as shown in FIGS. 7, 8 and 9, or may be the volume of the overlapping range. Further, it may be the ratio of the overlapping range to the entire imaging range of the image to be diagnosed. That is, the evaluation unit 61 calculates the evaluation value for each of the plurality of combinations based on the overlap range calculation means for calculating the overlap range with the first image for each of the plurality of combinations and the overlap range. It corresponds to an example of the evaluation value calculation means. In particular, the evaluation unit 61 calculates, as the overlapping range, the overlapping range between the sum range of the imaging ranges of two or more candidate images included in the combination and the imaging range of the first image. It corresponds to an example of the calculation means.

The above description is based on the premise that the relative positional relationship between the imaging range of the image to be diagnosed and the candidate image is clear. If the relative positional relationship of the imaging range of each image is not clear, the information processing device 10 can calculate the relative positional relationship by executing the alignment between the images in this step. This alignment process may be performed by any well-known method, and detailed description thereof will be omitted here.

Further, the calculation of the relative positional relationship of the imaging range of each image is not limited to the method of alignment between images, and may be calculated based on the incidental information of each image. For example, when the diagnosis target image and the candidate image are accompanied by information on the “imaging site”, the positional relationship between the images can be calculated based on the information. For example, the incidental information regarding the imaging site of the image to be diagnosed is "chest, abdomen", the incidental information regarding the imaging site of the candidate image I_r, 1 is the "head", and the incidental information regarding the imaging site of the candidate image I_r, 2. Is the "abdomen", and the incidental information regarding the imaging site of the candidate images I_r, 3 is the "chest". In this case, it is estimated that the imaging range of the candidate image combination P_1 (combination of I_r, 1 and I_r, 2) is "head and abdomen", and the overlapping range with the image to be diagnosed is "abdomen". .. Further, it is estimated that the imaging range of the candidate image combination P_2 (combination of I_r, 1 and I_r, 3) is "head and chest", and the overlapping range with the image to be diagnosed is "chest". Further, it is estimated that the imaging range of the candidate image combination P_3 (combination of I_r, 2 and I_r, 3) is "chest and abdomen", and the overlapping range with the image to be diagnosed is "chest and abdomen". .. In this case, the evaluation unit 61 can use the number of sites included in the overlapping range (1 when the overlapping range is "chest" or "abdomen", 2 when the overlapping range is "chest and abdomen") as the evaluation value. .. Further, a predetermined coefficient may be set in advance for each part of the human body such as "chest" and "abdomen", and the evaluation value may be calculated by multiplying each of the above overlapping ranges by the coefficient. As a more specific example, with respect to the "chest", "abdomen" and the like, the size of the relevant part in a standard human body is set as a coefficient. According to this, the size of the overlapping range can be calculated by a simpler method and used as an evaluation value.

By the method described above, the evaluation unit 61 calculates the evaluation value E_j (1 ≦ j ≦ M) for each of the combination P_j of the candidate images generated in step S2010.

(Step S2020) <Select a set of comparative images>
In this step, the selection unit 55 executes a process of selecting a combination of candidate images suitable for comparison with the image to be diagnosed from among the combinations of M candidate images based on each evaluation value calculated in step S2010. To do. Specifically, the selection unit 55 selects the combination having the highest evaluation value among the evaluation values E_j (1 ≦ j ≦ M) for the combination of M candidate images. The combination of the selected candidate images becomes a set of comparison images, and the candidate images constituting the set of comparison images become a comparison image. That is, the selection unit 55 corresponds to an example of selection means for selecting a plurality of second images to be compared with the first image from a plurality of candidate images. In particular, the selection unit 55 corresponds to an example of selection means characterized in that any one of a plurality of combinations is selected as a plurality of second images based on the evaluation value.

(Step S1050) <Comparison process>
In this step, the image processing unit 56 executes a comparison process between the image to be diagnosed and the set of the comparison image, and calculates the comparison result. Specifically, for example, the image processing unit 56 performs difference processing between the images of the image to be diagnosed and the set of the comparison image, and calculates the difference image as the comparison result. That is, the image processing unit 56 corresponds to an example of a generation means for generating a difference image between the first image and the second image by comparing the first image and the second image. In this embodiment, the set of comparative images is composed of a plurality of comparative images. As an example of the processing method in this step, a method in which the image processing unit 56 performs difference processing between each of the plurality of comparative images included in the set of comparative images and the image to be diagnosed can be considered. In this case, a difference image between each of the plurality of comparison images and the image to be diagnosed is calculated, and a set of these difference images can be used as the processing result of this step. The difference processing between the image to be diagnosed and the plurality of comparison images is an image such as the difference in the position and shape of the subject drawn in each of the image to be diagnosed and the plurality of comparison images, and the difference in image quality between the images. It is desirable to correct the difference in images that is unnecessary for diagnosis. Further, in the comparison processing, only the overlapping range between the imaging range of each of the plurality of comparative images and the imaging range of the image to be diagnosed can be processed. Since the overlapping range can be easily obtained from the processing results of steps S2000 and S2010, detailed description thereof will be omitted. It should be noted that a plurality of comparison images constituting a set of comparison images may be combined into one comparison image by stitching processing, and then the comparison processing between the combined comparison image and the image to be diagnosed may be executed. At this time, with respect to the region where the plurality of comparison images overlap, any comparison image including the overlapping region can be selected and combined. In addition, a comparison image whose imaging target image is close to the diagnosis target image may be selected, a comparison image whose imaging condition is close to the diagnosis target image may be selected, or a comparison image having better image quality than others may be selected. You can do it. Further, in addition to selecting and synthesizing any of the comparative images, a method of synthesizing by smoothly weighted averaging between overlapping images can also be an embodiment of the present invention.

The process of calculating the comparison result is not limited to the above difference process. For example, the image processing unit 56 may generate superimposed images for each of the diagnosis target image and the plurality of comparison images. In this case, different color channels can be assigned to the diagnosis target image and the comparison image, and a superimposed image can be generated as a color image in which they are mixed.

(Step S1060) <Display of comparison result>
In this step, the display control unit 57 executes a process of displaying the comparison result obtained by the process of step S1050 on the display unit 36. As a specific example, the display control unit 57 volume-renders each of the diagnosis target image, the comparison image set, and the difference image set as the comparison result, and the image created by the volume rendering (hereinafter, the volume rendering image). ) Can be displayed side by side on the display unit 36. At that time, with respect to the set of comparison images and the set of difference images, the display control unit 57 volume-renders each of the plurality of comparison images and the set of difference images constituting the set of comparison images and the set of difference images, and a plurality of sets. Volume rendered images may be displayed side by side. In addition to this, the display control unit 57 executes volume rendering after synthesizing a plurality of images constituting each set into one three-dimensional image by stitching, for example, based on the result of step S1050 or the like. May be good.

The method of displaying the comparison result is not limited to the above method, and the display control unit 57 may display the tomographic image at an arbitrary position in each image on the display unit 36 and change the tomographic image to be displayed by the user's operation. In this case, the display control unit 57 identifies the corresponding position in each of the diagnosis target image and the comparison image set based on the positional relationship between the diagnosis target image and the comparison image set, and displays the tomographic image at that position. It is desirable to perform processing such as (display the corresponding cross section). That is, when the tomographic image of the diagnosis target image displayed by the user is changed, the display control unit 57 displays the tomographic image of the position of the set of comparative images corresponding to the position of the tomographic image on the display unit 36. Further, when the alignment of the diagnosis target image and the set of the comparison image is performed in step S1050, the display control unit 57 displays the tomographic image in the diagnosis target image and the difference image based on the alignment result. It is desirable to perform processing such as switching in conjunction with.

Further, the information processing apparatus 10 calculates the overlap range between the combination generation unit 60 that sets a plurality of combinations of a plurality of candidate images and the candidate image included in the plurality of combinations and the diagnosis target image (first image). It has an overlap range calculating means (evaluation unit 61) and a selection unit 55 that selects at least one combination from a plurality of combinations based on the overlap range, and the selection unit 55 is further included in the selected combination. The candidate image to be selected is selected as the comparison image (second image).

As described above, according to this embodiment, there is an effect that a plurality of comparison images that can be suitably compared with the image to be diagnosed can be selected and the result of the comparison processing of those images can be provided to the user without requiring the user to perform troublesome operations. is there.

(Modification example 2-1: Combination of candidate images <Evaluation based on number / number of candidate images>)
In the process of step S2000 of this embodiment, a case where two candidate images are combined by combining candidate images has been described as an example, but this embodiment is not limited to this. For example, three or more candidate images may be combined. In this case, in the process of step S2010, the evaluation unit 61 calculates the evaluation value by comparing the imaging range of the combination of three or more candidate images with the imaging range of the image to be diagnosed. Further, the number of candidate images included in each combination of candidate images may be a predetermined constant or may be different from each other. Further, in this case, the combination of the candidate images is not necessarily limited to the case of combining a plurality of candidate images, and one candidate image may be treated as one of the combinations of the candidate images. For example, when the total number N of the candidate images is 3, the combination generation unit 60 includes three types of combinations containing only one candidate image, three types of combinations containing two, and one combination containing three candidate images. Generate each type. Then, the subsequent processing may be executed with the total number of combinations M = 7. In this case, the larger the number of candidate images to be combined, the wider the overlapping range tends to be. Therefore, it is desirable that the evaluation value calculated in step S2010 is calculated based on the number of candidate images included in the combination of the candidate images to be evaluated in addition to the information on the overlapping range. For example, when there is a combination of two candidate images whose evaluation values due to the overlapping range are almost the same (the difference between the evaluation values is equal to or less than a predetermined threshold value), the evaluation unit 61 is a candidate composed of a smaller number of candidate images. A method such as increasing the evaluation value of the combination of images can be considered. For example, among the combinations of candidate images in which the entire range of the images to be diagnosed overlaps, the selection unit 55 selects a combination of candidate images composed of a smaller number of candidate images as a set of comparison images. It may be. Alternatively, a value obtained by multiplying the evaluation value based on the overlapping range by a predetermined correction coefficient (a value that becomes smaller as the number of combined images increases) according to the number of candidate images to be combined is used as the corrected evaluation value. It may be. According to this, since the comparison processing in step S1050 can be executed for a smaller number of comparison images, the processing in step S1050 can be made more efficient and a higher quality comparison result can be obtained. is there.

(Modification 2-2: Evaluation value calculation method <Image quality / imaging conditions>)
The process of step S2010 in this embodiment will be described as an example of calculating an evaluation value based on an evaluation based on an overlap range between the imaging range of the image to be diagnosed and the imaging range of the combination of candidate images (hereinafter, overlapping range evaluation). However, the implementation of the present invention is not limited to this.

For example, apart from the evaluation of the overlapping range, the evaluation unit 61 performs an evaluation based on the image quality of each candidate image included in the combination of the candidate images (hereinafter, image quality evaluation), and integrates the evaluation based on the overlapping range evaluation and the image quality evaluation. The evaluation value may be calculated. That is, the evaluation unit 61 corresponds to an example of an image quality evaluation means for evaluating the image quality of each candidate image included in a plurality of combinations. Specifically, the evaluation unit 61 estimates the noise level of each candidate image included in each combination of candidate images, and lowers the evaluation value for the combination including the candidate images having a high noise level. For example, a value obtained by multiplying the evaluation value by the overlap range evaluation by a predetermined correction coefficient (a value that becomes smaller as the noise level is higher) according to the noise level of the candidate image may be used as the corrected evaluation value. .. At this time, the evaluation unit 61 calculates the size of the overlapping range between each candidate image and the image to be diagnosed, and calculates the evaluation value with more emphasis on the noise level of the candidate image having a large overlapping range. Is desirable. Further, the evaluation unit 61 may calculate the evaluation value by emphasizing the noise level of the candidate image having the highest noise level among the candidate images that at least partially overlap the imaging range of the image to be diagnosed. In addition to this, for example, the evaluation unit 61 may lower the evaluation value of the combination when the image quality of the plurality of candidate images included in the same combination of candidate images is different. For example, a value obtained by multiplying the evaluation value based on the overlapping range by a correction coefficient (a value that becomes smaller as the evaluation value of the image quality is lower) according to the image quality of the candidate image may be used as the evaluation value after correction. ..

Further, not limited to the above method, the evaluation unit 61 evaluates the candidate image based on, for example, reconstruction conditions which are incidental information of the candidate image, and imaging conditions such as tube voltage and tube current of the X-ray radiating device. You may. Further, the evaluation unit 61 may evaluate the candidate image based on the resolution of the candidate image (pixel size of the two-dimensional slice, slice interval, etc.). For example, the evaluation unit 61 may correct the evaluation value of the combination so that the evaluation value of the candidate image having a narrow slice interval becomes high. Further, the evaluation unit 61 may lower the evaluation value of the combination when the imaging conditions of the plurality of candidate images included in the same combination of candidate images are different.

Further, in the above description, the method of evaluation based on the image quality of the candidate image has been described as an example, but other than this, the evaluation may be performed based on the image quality of the image to be diagnosed. For example, the evaluation unit 61 evaluates the image quality of the diagnosis target image by the same method as described above, and evaluates the evaluation value based on both the evaluation of the image quality of the candidate image and the evaluation of the image quality of the diagnosis target image. Can be calculated. More specifically, the evaluation value of the combination of candidate images including the candidate image having a lower image quality than the image quality of the diagnosis target image may be lowered. In addition to this, the evaluation value of the combination of candidate images including the candidate image having an image quality close to that of the image to be diagnosed may be increased.

Further, the evaluation unit 61 acquires incidental information such as the inspection purpose of each candidate image included in the combination of the candidate images, the name of the organ to be inspected, the name of the disease to be inspected as the inspection information from the medical image database, and the inspection information. The candidate image may be evaluated based on. For example, the evaluation value may be corrected so that the evaluation value of the combination of the candidate images including the candidate image having the inspection information close to the inspection information of the diagnosis target image as incidental information becomes high. That is, the evaluation unit 61 corresponds to an example of the inspection information acquisition means for acquiring the inspection information of each candidate image included in the plurality of combinations. Further, a value obtained by multiplying the evaluation value by a correction coefficient (a value that becomes smaller as the degree of matching decreases) determined according to the degree of coincidence between the image to be diagnosed and the incidental information of the candidate image may be used as the corrected evaluation value. ..

(Modification 2-3: Selection method)
In step S2020 in this embodiment, a case where the selection unit 55 selects one combination of candidate images having the highest evaluation value calculated in step S2010 has been described as an example, but the present invention is not limited to this. For example, in step S2020, the selection unit 55 may select a plurality of combinations of candidate images having an evaluation value higher than a predetermined value as a set of comparison images. In this case, the image processing unit 56 may perform the same comparison processing on each of the plurality of sets of comparison images, and calculate the same number of comparison results as the number of sets of comparison images. According to the above method, there is an effect that the user can arbitrarily select and observe a desired comparison result from the comparison results for a plurality of comparison image sets that clear the predetermined conditions.

(Modification 2-4: Comparison method)
In the process of step S1050 in the present embodiment, the case where the image processing unit 56 generates a difference image or a superimposed image as a comparison process between the image to be diagnosed and the set of the comparison image has been described as an example, but the embodiment of the present invention has been described. Not limited to this. For example, the image processing unit 56 makes the anatomical structure drawn in each comparative image and the anatomical structure drawn in the image to be diagnosed substantially match each of the comparative images constituting the set of comparative images. The deformation alignment process may be performed, and an image obtained by transforming each comparison image based on the result of the deformation alignment process may be used as the result of the comparison process. According to this, there is an effect that the user can easily grasp the position in each comparison image constituting the set of comparison images corresponding to the portion of interest of the user in the diagnosis target image.

The implementation of the present invention is not limited to this, and in step S1060, the display control unit 57 may be configured to display a set of the diagnosis target image and the comparison image side by side on the display unit 36. There is an effect that a set of comparative images selected as an image suitable for comparison with the diagnosis target image from a plurality of candidate images in the same subject can be displayed in a form that can be easily compared with the diagnosis target image.

Further, the information processing apparatus 10 may be configured to perform only a process of associating the information for identifying the selected set of comparative images with the image to be diagnosed and storing the information in the medical image database 23. In this case, when observing the image to be diagnosed using another image viewer, the linked comparison image set is read and displayed in a comparable form, or between the diagnosis target image and the comparison image set. It is possible to generate a superimposed image or a difference image of.

(Modification 2-5: Save in association)
In the present embodiment, as the process of step S1060, the case where the display control unit 57 displays the comparison processing result of the diagnosis target image and the set of the comparison image has been described as an example, but the implementation of the present invention is not limited to this. .. For example, the comparison result calculated by the image processing unit 56 in step S1050 may be stored in the medical image database 23 and acquired by the control unit 37 by an operation or the like from the user. Then, the display control unit 57 may provide a mechanism for displaying the comparison result acquired from the medical image database 23 by the same processing as in step S1060. In this case, it is desirable that the control unit 37 saves the comparison processing result in association with the diagnosis target image acquired in step S1010 and the comparison image included in the set of comparison images selected in step S2020.

The information processing apparatus 10 according to the present embodiment performs a comparison process between a diagnosis target image and a comparison image of a subject for which the user intends to perform medical treatment, and provides the user with the result of the comparison process. As a specific example, a case where there are a plurality of images (hereinafter, candidate images) in which the same subject is imaged on a day different from the imaging date and time of the image to be diagnosed will be described. The information processing apparatus 10 according to the present embodiment uses an image suitable for comparison with the diagnosis target image from a plurality of candidate images as a comparison image based on the information regarding the imaging date and time of the diagnosis target image and the acquisition date and time of the candidate image. select.

The overall configuration of the medical information processing system according to the present embodiment is the same as that of FIG. 4 used in the description of the second embodiment. However, only the details of the processing performed by the evaluation unit 61 are different from those of the second embodiment. Further, the processing procedure executed by the information processing apparatus 10 according to the present embodiment is the same as that of FIG. 5 used in the description of the second embodiment. From step S1010 to step S2000 in FIG. 5, the information processing apparatus 10 executes the same processing as in the second embodiment. A detailed description will be omitted here.

(Step S2010)
In this step, the evaluation unit 61 executes a process of calculating an evaluation value for comparison with the diagnosis target image for each combination of candidate images generated in step S2000.

In this embodiment, the evaluation unit 61 also calculates the following evaluation values in addition to the evaluation values (evaluation values based on the overlapping range) calculated by the same method as in step S2010 of the second embodiment. That is, the evaluation unit 61 also calculates the imaging date and time (imaging date) of each candidate image included in the combination of the candidate images to be evaluated and the evaluation value (evaluation value based on the imaging date and time) based on the imaging date and time of the diagnosis target image. To do. Then, the evaluation unit 61 calculates an evaluation value obtained by integrating (for example, addition or multiplication) of the above two evaluation values.

As a specific example of calculating the evaluation value based on the imaging date and time, the evaluation value is calculated based on the difference between the imaging date and time of the image to be diagnosed and the imaging date and time (imaging date) of each candidate image included in the combination of the candidate images. A method is conceivable. More specifically, the period (elapsed period) between the imaging date and time of the image to be diagnosed and the representative value of the imaging date and time of the candidate image included in the combination of the candidate images (the representative imaging date and time of the combination of the candidate images) is long. In this case, a high value can be calculated as the evaluation value. Here, the representative value is an average of the imaging dates and times of the candidate images included in the combination of the candidate images, the oldest or latest imaging date and time, and the like. According to this, in step S2020 described later, the combination of the candidate images having a long elapsed time is preferentially selected based on the imaging date and time of the image to be diagnosed. In addition to this, the conditions based on the imaging date and time of the diagnosis target image and the representative imaging date and time of the combination of the candidate images include the imaging date and time of the diagnosis target image and the imaging date and time of the candidate image described in Example 1. Various conditions based on can be used. Here, when it is a condition that a predetermined treatment such as surgery is not inserted between the image to be diagnosed and the image to be diagnosed, the oldest candidate image included in the combination of candidate images is set as the representative imaging date and time of the combination of candidate images. It is desirable to set the imaging date and time.

The method of calculating the evaluation value by the evaluation unit 61 is not limited to the method exemplified above. For example, the evaluation unit 61 has a low evaluation value when the variation in the imaging date and time (imaging date) of the candidate images included in the combination of the candidate images is large (for example, the interval between the oldest imaging date and time and the newest imaging date and time). May be calculated. In this case, the evaluation value does not depend on the imaging date and time of the image to be diagnosed. Further, the evaluation unit 61 determines that the ratio between the period between the imaging date and time of the diagnosis target image and the imaging date and time of the oldest candidate image and the period between the imaging date and time of the diagnosis target image and the imaging date and time of the newest candidate image is large. A low value may be calculated as the evaluation value. According to this, the combination composed of the candidate images having a small difference in the imaging date and time is preferentially selected. According to this, a combination of candidate images is preferentially selected so that the difference between the candidate images is as small as possible.

The conditions relating to the imaging date and time may be a combination of the above conditions.

It should be noted that the condition under which the evaluation unit 61 calculates the evaluation value regarding the imaging date and time may be a fixed condition in advance, or the user may provide a condition setting unit (not shown) as in the first embodiment. It may be possible to set it according to the purpose.

From step S2020 to step S1060, the information processing apparatus 10 executes the same processing as in the second embodiment. A detailed description will be omitted here.

As described above, in this embodiment, the information processing device 10 has a selection unit 55 for selecting a comparison image (second image) to be compared with the diagnosis target image (first image) from a plurality of candidate images. An imaging date / time acquisition unit 53 that acquires an imaging date / time when a plurality of candidate images are captured, a combination generation unit 60 that sets a plurality of combinations of a plurality of candidate images, and a condition setting unit 54 that sets conditions related to the imaging date / time. The selection unit 55 further includes a selection unit 55 that selects at least one combination from the plurality of combinations based on the conditions regarding the imaging date and time set by the condition setting unit 54 and the imaging date and time of the candidate image. The candidate image included in the selected combination is selected as the comparison image (second image). Further, the evaluation unit 61 acquires the evaluation values of the imaging dates and times of the plurality of candidate images for the conditions relating to the imaging date and time, and the selection unit 55 selects at least one candidate image from the plurality of candidate images based on the evaluation values. You may.

According to the procedure described above, the processing of the information processing apparatus 10 in this embodiment is executed. In this embodiment, there is an effect that a plurality of comparative images that can be compared more preferably can be selected based on the imaging date and time of the diagnosis target image and the candidate image, and the result of the comparison processing of those images can be provided to the user.

(Modification 3-1)
In the present embodiment, the case where the imaging date and time of the image to be diagnosed and the imaging date and time of the candidate image are used in the evaluation value calculation process executed in step S2010 has been described as an example, but the implementation of the present invention is not limited to this. For example, when generating a combination of candidate images in step 2000, a condition is set for the length of the period between the imaging date and time of the diagnosis target image and the imaging date and time of the candidate image, and the combination of candidate images satisfying the condition is generated. You may try to do it. Alternatively, conditions may be set for variations in the imaging date and time between the candidate images, and a combination of candidate images satisfying the conditions may be generated. In addition to that, conditions regarding whether or not surgery or treatment is performed on the subject during the period between the imaging date and time of the image to be diagnosed and the imaging date and time of the candidate image are set, and a combination of candidate images satisfying the conditions is generated. You may try to do it. For example, when generating a combination of candidate images, the imaging date and time of a plurality of comparative images constituting the combination can be set within a predetermined range (for example, within one year). According to this, it is possible to omit the process of generating a combination of candidate images that does not satisfy a predetermined condition and the process of calculating an evaluation value, and it is expected that the processing efficiency will be improved.

(Modification example 3-2)
The evaluation unit 61 can calculate the evaluation value based on the contrast conditions of the image to be diagnosed and the candidate image. Specifically, a high evaluation value can be calculated for a combination of candidate images composed of candidate images having the same contrast conditions as the contrast conditions of the image to be diagnosed. In addition to this, for example, the evaluation value can be calculated based on the variation in the contrast conditions of the plurality of candidate images included in the combination of the candidate images. For example, when treating the presence or absence of contrast as a contrast condition, a combination of candidate images composed only of images with contrast (or the same time phase of contrast) or a combination of candidate images composed only of images without contrast. The evaluation value of can be increased. Further, the evaluation value can be calculated based on both the evaluation based on the difference between the contrast condition of the image to be diagnosed and the contrast condition of the candidate image and the variation of the contrast condition of a plurality of candidate images constituting the combination of the candidate images. .. For example, a higher evaluation value can be calculated for a combination of candidate images composed only of images having the same contrast conditions as the image to be diagnosed. When the diagnosis target image and the candidate image are MRI images, the evaluation value is calculated by the same method as described as an example of the contrast condition based on the type of image sequence (for example, T1 or T2). You may do so. According to these methods, a high evaluation value is calculated for the combination of the candidate image composed of the image to be diagnosed and the candidate image having the same image characteristics, and as a result, a suitable comparison processing result can be provided to the user. is there.

(Modification example 3-3)
In step S2010, the evaluation unit 61 may calculate the evaluation value based on the imaging date and time of each image, not based on the overlapping range of the imaging of the candidate image and the image to be diagnosed. Here, the method of calculating the evaluation value based on the imaging date and time of each image can be performed by the method described above. According to this, the evaluation value based on the overlapping range is not important, such as when it is known that the overlapping range with the image to be diagnosed is almost the same among the combinations of the plurality of candidate images generated in step S2000. In some cases, there is an effect that the processing can be performed more efficiently.

<Other Examples>
It is also possible to combine at least two of the plurality of modifications described above.

Further, the disclosed technology can take an embodiment as a system, an apparatus, a method, a program, a recording medium (storage medium), or the like. Specifically, it may be applied to a system composed of a plurality of devices (for example, a host computer, an interface device, an imaging device, a web application, etc.), or it may be applied to a device composed of one device. Good.

Needless to say, the object of the present invention is achieved by doing the following. That is, a recording medium (or storage medium) in which a software program code (computer program) that realizes the functions of the above-described embodiment is recorded is supplied to the system or device. Needless to say, the storage medium is a computer-readable storage medium. Then, the computer (or CPU or MPU) of the system or device reads and executes the program code stored in the recording medium. In this case, the program code itself read from the recording medium realizes the function of the above-described embodiment, and the recording medium on which the program code is recorded constitutes the present invention.

10 Information processing device 21 LAN
23 Medical image database 31 Communication IF
32 ROM
33 RAM
34 Storage unit 35 Operation unit 36 Display unit 50 Control unit 51 Diagnosis target image acquisition unit 52 Comparison candidate image acquisition unit 53 Shooting date and time acquisition unit 54 Condition setting unit 55 Selection unit 56 Image processing unit 57 Display control unit

Claims (18)

An information processing device having a selection means for selecting a second image to be compared with the first image from a plurality of candidate images.
An acquisition means for acquiring the imaging date and time when the plurality of candidate images were captured, and
It has a condition setting means for setting conditions related to the imaging date and time.
The selection means selects at least one candidate image from the plurality of candidate images as the second image based on the conditions regarding the imaging date and time set by the condition setting means and the imaging date and time of the plurality of candidate images. An information processing device characterized by this. The condition setting means sets that the imaging date and time is the closest to the first image as a condition regarding the imaging date and time, and the selection means sets the first image and the imaging date and time from the plurality of candidate images. The information processing apparatus according to claim 1, wherein a candidate image satisfying the condition of being closest is selected as the second image. The condition setting means sets the newest imaging date and time as a condition regarding the imaging date and time, and the selection means selects a candidate image satisfying the condition that it is the newest imaging date and time from the plurality of candidate images. The information processing apparatus according to claim 1, wherein the image is selected as a second image. The condition setting means sets as a condition regarding the imaging date and time that the imaging date and time is within a predetermined period based on the imaging date and time of the first image, and the selection means is selected from the plurality of candidate images. The information processing apparatus according to claim 1, wherein a candidate image satisfying the condition that the imaging date and time is within the predetermined period is selected as the second image. The condition setting means sets as a condition regarding the imaging date and time that a predetermined treatment is not sandwiched between the imaging date and time of the first image, and the selection means is selected from the plurality of candidate images. The second image is described in claim 1, wherein a candidate image satisfying the condition that the imaging date and time does not sandwich the predetermined treatment between the imaging date and time of the first image is selected as the second image. Information processing equipment. The condition setting means sets as a condition relating to the imaging date and time that the imaging date and time does not include a model change of the modality between the imaging date and time of the first image, and the selection means sets the plurality of candidate images. Therefore, claim 1 is characterized in that a candidate image satisfying the condition that the date and time of imaging is such that the model of the modality is not changed between the date and time of imaging of the first image is selected as the second image. The information processing device described. The information processing apparatus according to claim 1, wherein the selection means selects at least one candidate image from the plurality of candidate images based on incidental information other than the imaging date and time. The information processing apparatus according to claim 7, wherein the incidental information other than the imaging date and time includes at least one of an imaging range, an imaging site, an imaging condition, a reconstruction condition, a slice thickness, and a contrast condition. The evaluation means for acquiring the evaluation values of the imaging dates and times of the plurality of candidate images with respect to the conditions relating to the imaging date and time is further provided, and the selection means is at least one candidate image from the plurality of candidate images based on the evaluation values. The information processing apparatus according to claim 1, wherein the information processing apparatus is selected. The information processing apparatus according to any one of claims 1 to 9, further comprising a generation means for generating a difference image from the first image and the second image. An information processing device having a selection means for selecting a second image to be compared with the first image from a plurality of candidate images.
An acquisition means for acquiring the imaging date and time when the plurality of candidate images were captured, and
A setting means for setting a plurality of combinations of the plurality of candidate images, and
Condition setting means for setting conditions related to the imaging date and time, and
It has a combination selection means for selecting at least one combination from the plurality of combinations based on the conditions regarding the imaging date and time set by the condition setting means and the imaging date and time of the candidate image.
The information processing apparatus is characterized in that the selection means selects the candidate image included in the combination selected by the combination selection means as the second image. An information processing device having a selection means for selecting a second image to be compared with the first image from a plurality of candidate images.
A setting means for setting a plurality of combinations of the plurality of candidate images, and
Overlapping range calculating means for calculating the overlapping range between the candidate image and the first image included in the plurality of combinations, and
It has a combination selection means for selecting at least one combination from the plurality of combinations based on the overlap range.
The information processing apparatus is characterized in that the selection means selects the candidate image included in the combination selected by the combination selection means as the second image. An information processing method for selecting a second image to be compared with the first image from a plurality of candidate images.
The step of acquiring the imaging date and time when the plurality of candidate images were captured, and
Steps to set conditions related to imaging date and time,
An information processing method comprising: selecting at least one candidate image from the plurality of candidate images as the second image based on the set conditions and the imaging date and time of the plurality of candidate images. An information processing method for selecting a second image to be compared with the first image from a plurality of candidate images.
The step of acquiring the imaging date and time when the plurality of candidate images were captured, and
The step of setting a plurality of combinations of the plurality of candidate images and
Steps to set conditions related to imaging date and time,
A step of selecting at least one combination from the plurality of combinations based on the conditions relating to the imaging date and time and the imaging date and time of the candidate image.
An information processing method comprising a step of selecting the candidate image included in the selected combination as the second image. An information processing method for selecting a second image to be compared with the first image from a plurality of candidate images.
The step of setting a plurality of combinations of the plurality of candidate images and
A step of calculating the overlap range between the candidate image and the first image included in the plurality of combinations, and
A step of selecting at least one combination from the plurality of combinations based on the overlap range, and
An information processing method comprising a step of selecting the candidate image included in the selected combination as the second image. A program for causing a computer to execute the information processing method according to any one of claims 13 to 15. An information processing device having a selection means for selecting a second image to be compared with the first image from a plurality of candidate images.
It has an acquisition means for acquiring the imaging date and time when each of the candidate images is imaged.
The information processing apparatus is characterized in that the selection means selects the second image from the candidate images based on the context of the date and time of treatment or surgery on the subject and the imaging date and time of each of the candidate images. .. An information processing device having a selection means for selecting a second image to be compared with the first image from a plurality of candidate images.
An acquisition means for acquiring the imaging date and time when each of the candidate images was imaged, and
A setting means for setting a plurality of combinations of the candidate images, and
It has a combination selection means for selecting any of the combinations of the candidate images based on the interval of the imaging date and time between the candidate images included in the combination of the candidate images.
The information processing apparatus is characterized in that the selection means selects a candidate image included in the combination of the candidate images selected by the combination selection means as the second image.

Images