JP2006204641A - Image diagnostic support system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve a precision in a diagnosis by eliminating individual variations in an image diagnosis performed by comparing tomographic images of a subject and a healthy subject. <P>SOLUTION: A system 1 supporting the diagnosis by comparing tomographic images of the healthy subject and the subject is equipped with a target region data storage part 17 storing target data by disorders which shows target regions set for each disorder in advance, a characteristic region extracting part 15 which compares with the tomographic image of the same area of the healthy subject to extract characteristic region with the characteristic different from the healthy subject from the tomographic image of the subject, a spreading calculating part 18 which acquires the target data on one disorder from the storage part 17 and calculates a first evaluation value showing a ratio of the extracted characteristic region in the target region set based on the acquired target data, and a display device 3 for displaying the first evaluation value. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a technique for supporting diagnosis of a disease by comparing an image of a subject with an image of a healthy person, and in particular, assists diagnosis by extracting a region having a characteristic different from that of a healthy person from the image of the subject. Regarding technology.

  In recent years, imaging diagnostic machines (X-ray CT (Computed Tomography), MRI (Magnetic Resonance Imaging), ultrasonic diagnostic equipment) for capturing accurate images and capturing the inside of the body as images without imposing a heavy burden on subjects. , Radiation diagnostic machines, etc.) are indispensable in the current medical field.

  In the clinical field of nuclear medicine, single photon emission computed tomography (SPECT) and positron emission tomography using a radioisotope in the body of a subject and using gamma rays emitted from the radioisotope. Photography (Positron Emission Tomography, PET) is used respectively. As a result, it has become clear that blood flow in a specific part of the brain increases or decreases for each disease by taking and analyzing a tomographic image of the brain. Therefore, it is possible to extract the presence or absence of an abnormal site by comparing SPECT and PET images of the subject and a healthy person.

Statistical analysis techniques such as SPM (Statistical Parametric Mapping) and 3D-SSP (Three-dimensional Stereotactic Surface Projections) are known as methods for extracting abnormal sites from SPECT and PET images. Thereby, the abnormal part of a subject can be extracted objectively and it is useful for the diagnosis of a disease.
Image diagnosis in clinical psychiatry, volume S10, Nakayama Shoten

  However, in the conventional method, since humans visually discriminate based on the obtained subject images, artificial differences due to differences in experience and knowledge occur, and diagnosis support based on objective grounds is possible. There was no problem.

  Accordingly, an object of the present invention is to eliminate variations in judgments made by individuals in image diagnosis performed by comparing tomographic images of subjects and persons belonging to a specific group, and to assist in improving diagnosis accuracy.

  An image diagnosis support system according to an embodiment of the present invention is a system that supports diagnosis by comparing tomographic images of subjects and persons belonging to a specific group. Then, in a tomographic image of a predetermined part of the human body, storage means for storing target data for each disease indicating a target area set in advance for each disease, and a tomographic image of the predetermined part of a person belonging to a specific group; In comparison, extracting means for extracting feature regions having different characteristics from those belonging to the specific group from the tomographic image of the predetermined part of the subject, and acquiring target data for one disease from the storage means, the acquisition Calculation means for calculating an evaluation value indicating a distribution state of the feature area extracted by the extraction means for a target area determined based on the target data, and means for outputting the evaluation value calculated by the calculation means. .

  In a preferred embodiment, the calculation means may calculate a first evaluation value indicating a ratio of the feature region in the target region.

  In a preferred embodiment, the calculating means may calculate a second index value indicating a ratio of a portion belonging to the target area in the characteristic area.

  In a preferred embodiment, the target area set for each disease may be further set for each severity. And the target data memorize | stored in the said memory | storage means may be classified according to disease severity.

  In a preferred embodiment, the display unit may further include an image in which the feature area is displayed in a display mode different from other areas in the tomographic image of the subject and a calculation result by the calculation means.

  In a preferred embodiment, the extracting means calculates a Z value based on a tomographic image of a predetermined part of a person belonging to the specific group and a tomographic image of the predetermined part of the subject, and based on the calculated Z value. The feature region may be extracted. The image diagnosis support system further includes an average calculation unit that calculates an average value of the Z values of the feature regions extracted by the extraction unit, and the output unit includes the feature region calculated by the average calculation unit. You may make it output the average value of Z value.

  In a preferred embodiment, the tomographic images of persons and subjects belonging to the specific group may both be normalized SPECT (Single Photon Emission Computed Tomography) tomographic images of the brain. In the SPECT tomographic image of the subject's brain, the feature region has a blood flow rate of the subject's brain that is increased or decreased by a predetermined amount or more than the blood flow rate based on the SPECT tomographic image of the person belonging to the specific group. It may be a region.

  Hereinafter, an image diagnosis support system according to an embodiment of the present invention will be described with reference to the drawings. The diagnostic imaging support system according to the present embodiment supports diagnosis of a subject's disease by comparing SPECT tomographic images (hereinafter also simply referred to as tomographic images) of the brains of healthy subjects and subjects.

  FIG. 1 shows an overall configuration diagram of an image diagnosis support system 1 according to the present embodiment.

  An image diagnosis support system 1 according to the present embodiment includes an image diagnosis support system main body 10, an input device 2 connected to the image diagnosis support system main body 10, and a display device 3. The diagnostic imaging support system main body 10 is configured by, for example, a general-purpose computer system, and individual components or functions in the diagnostic imaging support system main body 10 described below are realized by, for example, executing a computer program. .

  The image diagnosis support system main body 10 includes a subject data storage unit 11 that stores image data of a subject to be diagnosed, and a healthy person data storage that stores image data of normalized healthy persons collected in advance. Unit 12, normalization processing unit 13 that normalizes subject data, Z value calculation unit 14 that calculates Z values, and feature region extraction unit 15 that extracts feature regions that show different features from healthy subjects from subject data A threshold value table 16 that stores threshold values for feature region extraction, a target region data storage unit 17 that stores data indicating target regions to be noted for each disease, and a spread calculation unit 18 that quantifies the spread of the feature region, The average processing unit 19 is provided.

  The subject data storage unit 11 stores voxel value data (hereinafter, referred to as subject data) 111 of the subject's SPECT tomographic image captured by the SPECT tomographic image capturing apparatus 5.

  In the healthy person data storage unit 12, an average value (hereinafter, referred to as healthy person data) 121 of the tomographic images of the brains of a large number of healthy persons collected in advance, and the brains of a large number of healthy persons are stored. Standard deviation data 122 of voxel value data of a tomographic image is stored. Since the shape and size of the human brain vary from person to person, the healthy person data 121 calculates an average value using tomographic image data of a normal person's standard brain normalized to a predetermined shape and size. It is a thing. Similarly, the standard deviation data 122 is obtained by using standardized tomographic image data of a normal brain of a healthy person.

  FIG. 2 is a diagram illustrating an example of the data structure of the healthy person data 121 and the standard deviation data 122. The healthy person data 121 in FIG. 11A is obtained from image data of N XY cross sections in the Z-axis direction, where the left-right direction of the head is the X-axis, the front-rear direction is the Y-axis, and the vertical direction is the Z-axis. Become. The voxel value included in each image data corresponds to the pixel value of each image. The standard deviation data 122 in FIG. 5B has the same structure as that of the healthy person data 121, and the standard deviation obtained at the same time when the healthy person data 121, which is an average value of a large number of healthy person data, is calculated. Stored.

  Referring to FIG. 1 again, the normalization processing unit 13 normalizes the subject data (voxel value data) stored in the subject data storage unit 11. Here, since the normalized subject data is compared with the healthy person data, the normalization processing unit 13 normalizes the same structure as the healthy person data 121.

The Z value calculation unit 14 compares the corresponding images (images of the same part) of the subject data and the healthy person data to calculate the Z value. That is, the Z value calculation unit 14 uses the subject data normalized by the normalization processing unit 13 and the healthy person data 121 and the standard deviation data 122 stored in the healthy person data storage unit 12 to calculate the Z value. calculate. The Z value calculation unit 14 calculates the Z value for all pixels (that is, all voxel values) of all the tomographic images of the normalized subject data using the following formula.
Z value = {(Voxel value of healthy subject data) − (Voxel value of subject data)} / standard deviation

  The threshold value table 16 stores Z value threshold values to be extracted as feature regions by the feature region extraction unit 15. For example, the threshold value may be set to a different value for each disease stage severity.

  Based on the Z value calculated by the Z value calculation unit 14, the feature region extraction unit 15 extracts a region showing a feature different from that of a healthy person from the tomographic image of the subject as a feature region. For example, the feature region extraction unit 15 extracts a region whose Z value is greater than or less than a predetermined threshold as a feature region. Here, in the case of the SPECT tomographic image used in the present embodiment, when the Z value is positive (that is, the healthy person data has a higher voxel value than the subject data), the blood flow of the subject is the blood of the healthy person. On the contrary, when the Z value is negative (that is, the healthy person data has a lower voxel value than the subject data), the subject's blood flow is greater than the healthy person's blood flow. Is shown. Therefore, in this embodiment, in the subject data, a portion where the blood flow volume is increased or decreased as compared with the healthy person is extracted as the feature region. This is because a specific disease is known to have an increase or decrease in blood flow at a specific location. In particular, in the present embodiment, as will be described below, a target area to be focused on for each disease is set in advance, and diagnosis support using this target area is performed.

  It should be noted that the threshold value of the Z value for feature region extraction may be determined in advance, may be determined with reference to the threshold value table 16, or may be designated by the user from the input device 2 each time.

  The target area data storage unit 17 stores target data indicating target areas set in advance for each disease severity (stage and progression). Here, the target region is a region to be noted when diagnosing a disease in a SPECT tomographic image of the brain. That is, the disease-specific target region is a region where a change in blood flow occurs in each disease. Therefore, different regions of different tomographic images are set for each target region for each disease and for each disease stage severity. A plurality of target areas may be set for one tomographic image. Examples of brain diseases include Alzheimer's dementia, Parkinson's disease, progressive supranuclear palsy, Machado-Joseph disease, and Olive Bridge cerebellar atrophy. The target area is determined based on predetermined diagnostic criteria such as NINCDS-ADRDA and various examinations. For example, a disease group (a large number of patient data for a certain disease) and a healthy group (a large number of healthy data) are compared between groups, and the threshold is determined by a test statistic (t-test etc). Can do.

  FIG. 3 is a diagram illustrating an example of target data stored in the target area data storage unit 17. That is, the target data is set according to disease-specific stage severity, such as “Alzheimer severity 1”, “Alzheimer severity 2”, and so on. In each target data, for each tomographic image, “1” is set for the pixel that is the target area, and “0” is set for the other areas.

  Referring again to FIG. 1, the spread calculation unit 18 calculates an evaluation value indicating the distribution state of the feature region extracted by the feature region extraction unit 15. In the present embodiment, using the target data stored in the target area data storage unit 17, two evaluation values are calculated for each disease severity. That is, the first evaluation value is an evaluation value indicating the proportion of the feature region in all target regions (hereinafter referred to as EXTENT1). The second evaluation value is an evaluation value indicating the ratio of the portion belonging to the target area among all the characteristic areas (hereinafter referred to as EXTENT2).

  FIG. 4 is a schematic diagram for explaining EXTENT1 and EXTENT2. FIG. 4A is an XY tomographic image (Z = m) 100 of the subject's brain. The tomographic image 100 displays feature regions 51 to 54 extracted by the feature region extraction unit 15 (the areas are S1 to S4, respectively). FIG. 5B shows target data 110 indicating target areas 61 and 62 (areas are A1 and A2 respectively) of a certain disease in the XY plane where Z = m. Here, the area may be the number of pixels respectively belonging to the feature region and the target region.

FIG. 3C shows the tomographic image 100 and the target data 110 displayed in an overlapping manner. From this figure, it can be seen that the feature region 52 belongs to the target region 61 and the feature region 54 belongs to the target region 62. When EXTENT1 and EXTENT2 are obtained in FIG.
EXTENT1 = (S2 + S4) / (A1 + A2)
EXTENT2 = (S2 + S4) / (S1 + S2 + S3 + S4)

  EXTENT1 quantifies the extent of the feature region (S2 + S4) in the target region (A1 + A2). Thus, it is considered that the larger EXTENT1, the more characteristic regions occupy the target region, and the higher the probability of each disease (and severity).

  EXTENT2 is obtained by quantifying the extent of expansion to the target area among all the characteristic areas (S1 + S2 + S3 + S4). As a result, the larger the EXTENT2, the more concentrated the feature region is in the target region, and thus it is considered that the probability of each disease (and severity) is high.

  The average processing unit 19 calculates the average value of the Z values of the feature regions extracted by the feature region extraction unit 15.

  The EXTENT 1 and 2 calculated by the spread calculation unit 18 and the average value of the Z value calculated by the average processing unit 19 are displayed on the display device 3. At this time, the average value of the EXTENT1, 2 and the Z value may be displayed together with the respective tomographic images of each subject. Further, the tomographic image may be displayed in a display mode (for example, colored) such that the frame and the feature region indicating the target region can be distinguished from the other regions as shown in FIG. 4C, for example. Good.

  Note that the average values of the EXTENT1, 2 and Z values may be output from a printer (not shown), or may be output to a predetermined recording medium or another computer, in addition to being displayed on the display device 3.

  Next, the processing procedure of this system will be described according to the flowchart shown in FIG.

  First, this system acquires subject data from the SPECT tomographic imaging apparatus 5 and stores it in the subject data storage unit 11 (S11).

  Next, the normalization processing unit 13 normalizes the subject data stored in the subject data storage unit 11 (S12).

  Then, the Z value calculation unit 14 calculates a Z value using the normalized subject data and the healthy person data stored in advance in the healthy person data storage unit 12 (S13).

  When the Z value is calculated for all images of the subject data, the feature region extraction unit 15 extracts a feature region whose Z value is equal to or greater than a predetermined threshold (S14).

  The average processing unit 19 calculates the average value of the Z values of the feature regions extracted by the feature region extraction unit 15 (S15).

  The spread calculation unit 18 acquires target data from the target area data storage unit 17 and calculates EXTENT1 and EXTENT2 (S16). Here, the spread calculation unit 18 obtains target data or all target data of the specified disease (and severity) from the target area data storage unit 17, and calculates EXTENT1 and EXTENT2 based on the target data.

  Finally, the average value of the Z values calculated in step S15 and the EXTENT1 and EXTENT2 calculated in step S16 are displayed on the display device 3 (S17).

  Thereby, the quantitative evaluation value regarding subject data can be shown using a SPECT tomographic image of a subject and a healthy person. By referring to this evaluation value, the doctor can more accurately diagnose the disease. In particular, since a quantitative evaluation value of the subject data is presented, it is possible to suppress a variation in judgment by a doctor and perform a more accurate diagnosis.

  The above-described embodiments of the present invention are examples for explaining the present invention, and are not intended to limit the scope of the present invention only to those embodiments. Those skilled in the art can implement the present invention in various other modes without departing from the gist of the present invention.

  For example, in this embodiment, feature regions are extracted using tomographic images by SPECT, and diagnosis support is performed based on the feature regions, but in addition to SPECT, tomographic images by PET, X-ray CT, and MRI may be used. it can. Here, in the case of SPECT and PET images, in addition to the voxel value indicating blood flow, for example, an integrated value indicating increase / decrease in accumulation on the receptor may be used. Further, CT values may be used for CT images, and T1 values and T2 values (relaxation times) may be used for MRI images.

  Further, when extracting a feature region, the Z value is used in the above-described embodiment, but the method of extracting the feature region is not necessarily limited to this, and subject data shows a feature different from that of healthy subject data by some method. It suffices if the region can be extracted. For example, t value or F value can be used.

  Furthermore, in the above-described embodiment, the data to be compared with the subject data is healthy person data. In addition, for example, the average value of a large number of patient data of a specific disease and the subject data are compared. May be. For example, when the patient data of the initial Alzheimer patient is used as the comparison target data, a characteristic region for the initial Alzheimer patient is extracted from the subject data. Then, if the spread of the feature region is evaluated by using the disease-specific divided region data of late-stage Alzheimer, the progress of Alzheimer can be determined. Furthermore, a feature region is extracted by comparing an average value of a large number of patient data of a certain disease (for example, frontal lobe Alzheimer) and subject data of the disease, and divided region data of another disease (for example, depression) is used. When the spread of the characteristic region is evaluated, it is possible to distinguish a subject who has another disease from a subject who does not.

1 is an overall configuration diagram of an image diagnosis support system 1 according to an embodiment of the present invention. An example of the data structure of healthy subject data and standard deviation data is shown. An example of the target data memorize | stored in the target area data storage part is shown. It is a schematic diagram for demonstrating EXTENT1 and EXTENT2. It is a flowchart which shows the process sequence in this system.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image diagnosis assistance system 2 Input device 3 Display apparatus 5 Tomographic imaging device 10 Image diagnosis assistance system main body 11 Subject data storage part 12 Healthy person data storage part 13 Normalization process part 14 Z value calculation part 15 Feature area extraction part 16 Threshold value Table 17 Target area data storage unit 18 Spread calculation unit 19 Average processing unit

Claims (9)

A system that supports diagnosis by comparing tomographic images of subjects and persons belonging to a specific group,
In a tomographic image of a predetermined part of the human body, storage means for storing disease-specific target data indicating a target region set in advance for each disease;
An extraction means for extracting a feature region having a characteristic different from that of the person belonging to the specific group from the tomographic image of the predetermined part of the subject compared to the tomographic image of the predetermined part of the person belonging to the specific group;
Calculating means for acquiring target data for one disease from the storage means, and calculating an evaluation value indicating a distribution state of the feature area extracted by the extracting means for a target area determined based on the acquired target data; ,
Means for outputting an evaluation value calculated by the calculating means.   The image diagnosis support system according to claim 1, wherein the calculation unit calculates a first evaluation value indicating a ratio of the feature region in the target region.   The image diagnosis support system according to claim 1, wherein the calculation unit calculates a second index value indicating a ratio of a portion belonging to the target region in the feature region. The target area set for each disease is further set by severity,
2. The diagnostic imaging support system according to claim 1, wherein the target data stored in the storage means is classified according to disease severity.   The image diagnosis support according to claim 1, further comprising display means for displaying an image in which the feature area is displayed in a display mode different from other areas in the tomographic image of the subject, and an evaluation value calculated by the calculation means. system. The extraction means calculates a Z value based on a tomographic image of a predetermined part of a person belonging to the specific group and a tomographic image of the predetermined part of the subject, and extracts the feature region based on the calculated Z value. ,
The image diagnosis support system further includes an average calculation unit that calculates an average value of the Z values of the feature regions extracted by the extraction unit,
The image diagnosis support system according to claim 1, wherein the output unit outputs an average value of the Z values of the feature regions calculated by the average calculation unit. The tomographic images of the persons belonging to the specific group and the subject are both normalized SPECT (Single Photon Emission Computed Tomography) tomographic images of the brain,
The feature region is a region in the SPECT tomographic image of the subject's brain in which the blood flow in the subject's brain is increased or decreased by a predetermined amount or more than the blood flow based on the SPECT tomographic image of a person belonging to the specific group. The diagnostic imaging support system according to any one of claims 1 to 6. A method for assisting diagnosis by comparing tomographic images of subjects and persons belonging to a specific group,
In a tomographic image of a predetermined part of a human body, storing target data for each disease indicating a target area set in advance for each disease in a storage unit;
Extracting a feature region having characteristics different from those belonging to the specific group from the tomographic image of the predetermined part of the subject compared to the tomographic image of the predetermined part of the person belonging to the specific group;
Obtaining target data for one disease from the storage means, and calculating an evaluation value indicating a distribution state of the extracted feature region with respect to a target region determined based on the acquired target data;
Outputting the calculated evaluation value; and a method for supporting image diagnosis. When executed on a computer,
In a tomographic image of a predetermined part of a human body, storing target data for each disease indicating a target area set in advance for each disease in a storage unit;
Extracting a feature region having characteristics different from those belonging to the specific group from the tomographic image of the predetermined part of the subject compared to the tomographic image of the predetermined part of the person belonging to the specific group;
Obtaining target data for one disease from the storage means, and calculating an evaluation value indicating a distribution state of the extracted feature region with respect to a target region determined based on the acquired target data;
And a step of outputting the calculated evaluation value. A computer program in which processing for supporting image diagnosis is performed.

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