Patent Description:
In diagnostic imaging for diagnosing cases using a computed tomography (CT) image, it is said to be difficult to diagnose cases in which lesions are distributed in a plurality of regions over a wide range, such as diffuse pulmonary disease. When diagnosing such cases, doctors narrow down candidates for disease names by referring to similar cases for which a definite diagnose has been made in the past.

However, searching for past similar cases takes time, which is a large burden on the doctor. Therefore, a similar case image search technique has been proposed which automatically searches for CT images of similar cases from the past cases and presents a search result to support a diagnosis task of the doctor (for example, refer to <CIT> and "<NPL>). The pixel values of the CT image may be referred to as CT values.

In this similar case image search technique, an organ region extracted from a medical image is divided into a plurality of regions, and the number of pixels indicating a lesion in each of the plurality of regions is counted. With reference to a storage unit that stores the number of pixels indicating the lesion for each region, a similar case image corresponding to a similarity of the number of pixels indicating the lesion is specified.

In relation to CT images, classification of solid organs and hollow organs is also known (for example, refer to "<NPL>). The CT value of each organ is also known (for example, refer to "<NPL>>). A diagnostic image generation apparatus that generates a three-dimensional projection image from medical volume data by rendering, and a lung field region extraction method that extracts the entire lung field region including a lost lung field region from the CT image are also known (for example, refer to <CIT> and <CIT>). Further prior art is:.

An object of the present disclosure is to distinguish a plurality of parts included in an image of a body.

The scope of the present invention is defined in the appended claims.

According to the similar case image search technique in the related art, feature amounts are extracted from a CT image of each of a plurality of slices of a lung field, a result obtained by combining the extracted feature amounts is quantified as a three-dimensional distribution of a lesion, and thereby cases having similar distributions may be searched for. At this time, in order to take into consideration individual differences in the size of the lung field, the positions of the upper end and the lower end of the lung field are calculated, and feature amount extraction is performed on a predetermined number of CT images which are extracted from the plurality of CT images from the upper end to the lower end.

Therefore, in a case where the calculation results of the upper end and the lower end of the lung field largely deviate from the original correct positions, it is difficult to accurately calculate the three-dimensional distribution of the lesion. Therefore, the calculation accuracy of the upper end and the lower end of the lung field greatly affects the calculation accuracy of the three-dimensional distribution of the lesion in the lung field.

For example, the positions of the upper end and the lower end of the lung field may be calculated by generating a coronal image indicating a sectional shape of the lung field from the CT images of the plurality of slices of the body. However, in a case where a lung field region and a region of an organ other than the lung field are mixed in the CT image, it is difficult to generate a coronal image indicating an accurate sectional shape of the lung field. In this case, it is desirable to generate a coronal image by extracting only the lung field region from the CT image.

Such a problem occurs in a case where a plurality of different body parts are mixed in the CT image as well as in the case of generating a coronal image indicating the sectional shape of the lung field. Such a problem occurs in various images including body parts of a human or animal as well as in the CT image of a human body.

Hereinafter, embodiments of a technique for distinguishing a plurality of parts included in an image of a body will be described in detail with reference to the drawings.

In the CT image of the slice of the lung field, there may be a region corresponding to a hollow portion of an organ other than the lung field, such as a stomach or a large intestine in addition to the lung field region. As described in "<NPL>>, the lung field is a solid organ formed by closely binding cells having a function, and the stomach and the large intestine are tubular hollow organs through which substances such as ingested food pass.

As described in "<NPL>>, the CT value represents a percentage of attenuation of X-rays when the X-rays pass through a human body, and the CT value of air is defined as -<NUM> HU. Since the content of the hollow organ such as the stomach or the large intestine is a cavity (air), the CT value of the hollow organ is approximately -<NUM> HU. On the other hand, since the lung field is a solid organ and the content thereof is not completely hollow, the CT value of the lung field is a value larger than -<NUM> HU. The CT values of other parts such as bones, muscles, blood vessels, and fat are values much larger than the CT value of the lung field.

In the CT image of the slice of the lung field, the coordinate in the left-right direction of the body is defined as an x coordinate, and the coordinate in the front-rear direction of the body is defined as a y coordinate. In a case where a CT image does not satisfy any of the following conditions a and b, or in a case where a CT image satisfies only the condition a, an accurate coronal image of the lung field may be generated from the CT image of each of a plurality of slices by using the features of the CT values of the lung field.

Condition a: a lung field region and a region W corresponding to a hollow portion of a hollow organ are present in the CT image, and the range of the x coordinate of the region W overlaps the range of the x coordinate of the lung field region.

Condition b: when CT images are sequentially selected from the upper lung field toward the lower lung field, the region W is continuously included even in the CT image which no longer includes the lung field region.

<FIG> are diagrams illustrating an example of a CT image from which an accurate coronal image may be generated. <FIG> illustrates an example of a CT image <NUM> that satisfies neither condition a nor condition b. The x coordinate represents a coordinate in the left-right direction of the body, and the y coordinate represents a coordinate in the front-rear direction of the body. A circular region <NUM> represents the field of view (FOV) of the CT apparatus that generates the CT image. In the circular region <NUM>, broken lines parallel to the y axis represent a plurality of pixels having the same x coordinate.

In the CT image <NUM> of the slice of the lower lung field, a lung field region <NUM> of the left lung and a lung field region <NUM> of the right lung are included, and a region of a hollow portion of a hollow organ is not included. In this case, a computer obtains a minimum value <NUM> of the CT values of the plurality of pixels having the same x coordinate at each position on the x axis indicated by the broken line, and plots the minimum value <NUM> at the corresponding position on the plane of the coronal image. Since the region of the hollow portion of the hollow organ is not included, in the range of the x coordinates of the lung field region <NUM> or the lung field region <NUM>, the minimum value <NUM> represents the CT value of the lung field region <NUM> or the lung field region <NUM>.

The computer repeats the same processing for the CT image of each of the plurality of slices to generate a coronal image including a lung field region <NUM> of the left lung and a lung field region <NUM> of the right lung. The computer analyzes the coronal image to calculate the position of a lower end <NUM> of the lung field.

<FIG> illustrates an example of a CT image <NUM> that satisfies the condition a and does not satisfy the condition b. In the CT image <NUM> of the slice of the lower lung field, a region <NUM> of the hollow portion of the hollow organ is included in addition to a lung field region <NUM> of the left lung and a lung field region <NUM> of the right lung, and the range of the x coordinate of the region <NUM> overlaps the range of the x coordinate of the lung field region <NUM>. However, when CT images are sequentially selected from the upper lung field toward the lower lung field, the region of the hollow portion of the hollow organ is also not included in the CT image which no longer includes the lung field region.

In this case, the computer repeats the same processing as in the case of <FIG> to generate a coronal image including a lung field region <NUM> of the left lung and a lung field region <NUM> of the right lung. In the range of the x coordinate of the region <NUM>, the minimum value of the CT values represents the CT value of the region <NUM>, not the lung field region <NUM>. However, since the CT value of the region <NUM> are similar to the CT value of the lung field region <NUM>, the shape of the lung field region <NUM> included in the coronal image is not greatly affected. The computer analyzes the coronal image to calculate the position of a lower end <NUM> of the lung field.

<FIG> illustrate an example of a CT image from which it is difficult to generate an accurate coronal image because both of the condition a and the condition b are satisfied. <FIG> illustrates an example of a CT image <NUM> including a lung field region and a region corresponding to a hollow portion of a hollow organ. In the CT image <NUM> of the slice of the lower lung field, a region <NUM> of the hollow portion of the hollow organ is included in addition to a lung field region <NUM> of the left lung and a lung field region <NUM> of the right lung, and the range of the x coordinate of the region <NUM> overlaps the range of the x coordinate of the lung field region <NUM>. In this case, in the range of the x coordinate of the region <NUM>, the minimum value of the CT values represents the CT value of the region <NUM>, not the lung field region <NUM>.

<FIG> illustrates an example of a CT image <NUM> which includes a region corresponding to a hollow portion of a hollow organ and does not include a lung field region. The CT image <NUM> is a CT image of a slice further below the CT image <NUM>. In the CT image <NUM>, a region <NUM> of the hollow portion of the hollow organ is included, and a lung field region is not included. In this case, in the range of the x coordinate of the region <NUM>, the minimum value of the CT values represents the CT value of the region <NUM>.

The computer repeats the same processing as in the case of <FIG> to generate a coronal image including a lung field region <NUM> of the left lung and a lung field region <NUM> of the right lung. However, when CT images are sequentially selected from the upper lung field toward the lower lung field, the region <NUM> of the hollow portion of the hollow organ is continuously included even in the CT image <NUM> which no longer includes the lung field region. Therefore, the shape of the lung field region <NUM> includes a projection <NUM> corresponding to the shape of the hollow organ, and the position of a lower end <NUM> of the lung field calculated from the coronal image is lower than the actual lower end.

<FIG> illustrates an example of a functional configuration of an image processing apparatus according to the embodiment. An image processing apparatus <NUM> in <FIG> includes a storage unit <NUM> and an identification unit <NUM>. The storage unit <NUM> stores an image <NUM> of the body. The identification unit <NUM> performs image processing on the image <NUM>.

<FIG> is a flowchart illustrating an example of image processing performed by the image processing apparatus <NUM> in <FIG>. First, the identification unit <NUM> extracts a plurality of consecutive pixels corresponding to a first part or a second part of the body from a pixel column in a predetermined direction of the image <NUM> (operation <NUM>), and obtains a statistical value of pixel values of the plurality of consecutive pixels (operation <NUM>). The identification unit <NUM> identifies a part corresponding to the plurality of consecutive pixels, among the first part or the second part based on the statistical value (operation <NUM>).

With the image processing apparatus <NUM> in <FIG>, a plurality of parts included in the image of the body may be distinguished.

<FIG> illustrates a specific example of the image processing apparatus <NUM> in <FIG>. An image processing apparatus <NUM> in <FIG> includes a storage unit <NUM>, an acquisition unit <NUM>, an identification unit <NUM>, a generation unit <NUM>, a learning unit <NUM>, and a determination unit <NUM>. The storage unit <NUM> and the identification unit <NUM> correspond to the storage unit <NUM> and the identification unit <NUM> in <FIG>, respectively.

A CT apparatus <NUM> is installed in a medical institution, and captures a CT image of a patient. The image processing apparatus <NUM> and the CT apparatus <NUM> may communicate with each other via a communication network.

<FIG> illustrates an example of image processing performed by the image processing apparatus <NUM> in <FIG>. The image processing apparatus <NUM> has two operation modes which are a learning mode and an operation mode. In the learning mode, the image processing apparatus <NUM> generates an extraction model <NUM> for extracting a lung field region from a coronal image in the following procedures, for example.

P1: The acquisition unit <NUM> acquires a learning CT image <NUM> of each of a plurality of patients from the CT apparatus <NUM>, and stores the learning CT image in the storage unit <NUM>. The learning CT image <NUM> is a CT image of a slice of a body including a lung field, and a plurality of learning CT images <NUM> corresponding to a plurality of slices are acquired for each patient. The identification unit <NUM> identifies a lung field region corresponding to the lung field in each learning CT image <NUM>. The generation unit <NUM> generates a learning coronal image <NUM> indicating the sectional shape of the lung field, from the lung field region of each of the plurality of learning CT images <NUM> of each patient, and stores the learning coronal image in the storage unit <NUM>.

P2: The user adds an annotation <NUM> indicating the lung field region, to the learning coronal image <NUM> of each patient.

P3: The generation unit <NUM> generates ground truth region information <NUM> indicating the lung field region in the learning coronal image <NUM>, based on the annotation <NUM> added to the learning coronal image <NUM>, and stores the ground truth region information in the storage unit <NUM>.

P4: The learning unit <NUM> causes the learning model to learn the learning coronal images <NUM> and the ground truth region information <NUM> of the plurality of patients to generate the extraction model <NUM> that is a trained model, and stores the extraction model in the storage unit <NUM>.

In the operation mode, the image processing apparatus <NUM> determines the upper end and the lower end of the lung field in the following procedures, for example.

P11: The acquisition unit <NUM> acquires a plurality of CT images <NUM> of a patient as a diagnosis target, from the CT apparatus <NUM>, and stores the CT images in the storage unit <NUM>. The CT image <NUM> is an example of the image <NUM> in <FIG>. The identification unit <NUM> identifies a lung field region in each CT image <NUM>, and the generation unit <NUM> generates a coronal image <NUM> of the lung field from the lung field region of each of the plurality of CT images <NUM> and stores the coronal image in the storage unit <NUM>.

P12: The determination unit <NUM> extracts a lung field region from the coronal image <NUM> by using the extraction model <NUM>, generates region information <NUM> indicating the extracted lung field region, and stores the region information in the storage unit <NUM>.

P13: The determination unit <NUM> determines an upper end <NUM> and a lower end <NUM> of the lung field by analyzing the image of the lung field region indicated by the region information <NUM>, generates position information <NUM> indicating the upper end <NUM> and the lower end <NUM>, and stores the position information in the storage unit <NUM>.

Next, the processing of identifying the lung field region in the CT image <NUM> in procedure P11 will be described. The processing of identifying the lung field region in the learning CT image <NUM> in procedure P1 is also similar to procedure P11.

The identification unit <NUM> distinguishes the lung field region from the region W by using the difference between the CT value of the lung field region and the CT value of the region W corresponding to the hollow portion of the hollow organ. The lung field is an example of the first part, and the hollow organ is an example of the second part. The following medical findings are obtained from the description of "<NPL>> and the like.

CT value of lung field region: about -<NUM> HU to about -<NUM> HU.

According to the above-described medical findings, it is assumed that the CT value of the lung field region is greater than the CT value of the region W. However, in a case where the CT values of individual pixels are compared, there is also a possibility that the CT value of the lung field region is smaller than the CT value of the region W incidentally. Therefore, the identification unit <NUM> obtains the statistical value of the CT values of the plurality of pixels included in a determination target region, and identifies whether the determination target region is the lung field region or the region W based on the statistical value.

In procedure P11, the image processing apparatus <NUM> identifies a lung field region in the CT image <NUM>, and generates the coronal image <NUM> of the lung field, from the lung field region of each of the plurality of CT images <NUM>, for example, in the following procedures P21 to P28 (not illustrated).

P21: The identification unit <NUM> performs binarization processing on the CT image <NUM> to extract a contour of each region. As a threshold T1 for the binarization processing, for example, a CT value in a range of -<NUM> HU to <NUM> HU is used. The identification unit <NUM> generates a processing target CT image by converting the CT values of pixels belonging to regions other than the region having the largest internal area into predetermined values. As the predetermined value, for example, a CT value equal to or greater than the threshold T1 is used.

<FIG> illustrates an example of the processing target CT image. A CT image <NUM> before the conversion includes a lung field region <NUM> of the left lung, a lung field region <NUM> of the right lung, and a region <NUM> of the hollow portion of the hollow organ. By performing the binarization processing on the CT image <NUM>, in addition to contours of the lung field region <NUM>, the lung field region <NUM>, and the region <NUM>, a contour of a body region <NUM> including these regions is also extracted. Since the body region <NUM> corresponds to a region having the largest internal area, CT values of pixels belonging to a region outside the body region <NUM> are converted into predetermined values, and thereby a processing target CT image <NUM> is generated.

P22: The identification unit <NUM> selects one x coordinate of the processing target CT image, and identifies a pixel column in a direction of a straight line parallel to the y axis at the position of the selected x coordinate. The direction of the straight line parallel to the y axis is an example of the predetermined direction.

P23: The identification unit <NUM> extracts a partial pixel column corresponding to the lung field region or the region W from the identified pixel column. For example, the identification unit <NUM> compares the CT value of each pixel included in the pixel column with a threshold T2. The identification unit <NUM> extracts a plurality of consecutive pixels each of which has a CT value equal to or smaller than the threshold T2, as the partial pixel column corresponding to the lung field region or the region W.

The threshold T2 may be determined based on the above-described medical findings, or may be determined by performing statistical processing on CT values of actual CT images. In the case of using the statistical processing, the threshold T2 that separates a group of CT values of the lung field region and the region W from a group of CT values of the other parts may be determined from a distribution of CT values obtained by collecting the CT values of the lung field region, the region W, and the other parts. As the threshold T2, for example, a CT value in a range of -<NUM> HU to <NUM> HU is used. The threshold T2 is an example of a second threshold.

By using such a threshold T2, a partial pixel column corresponding to the lung field region or the region W may be accurately extracted.

P24: For each extracted partial pixel column, the identification unit <NUM> obtains a statistical value V of the CT values of the plurality of pixels included in the partial pixel column. As the statistical value V, an average value, a median value, a mode value, or the like may be used.

P25: The identification unit <NUM> compares the statistical value V with a threshold T3. The threshold T3 is a CT value smaller than the threshold T2. The identification unit <NUM> determines that the partial pixel column corresponds to the lung field region in a case where the statistical value V is greater than the threshold T3, and determines that the partial pixel column corresponds to the region W in a case where the statistical value V is equal to or smaller than the threshold T3. The identification unit <NUM> identifies the partial pixel column determined to correspond to the lung field region, as a part of the lung field region.

The threshold T3 may be determined based on the above-described medical findings, or may be determined by performing statistical processing on CT values of actual CT images. In the case of using the statistical processing, the threshold T3 that separates a group of CT values of the lung field region from a group of CT values of the region W may be determined from a distribution of CT values obtained by collecting the CT values of the lung field region and the region W. As the threshold T3, for example, a CT value in a range of -<NUM> HU to -<NUM> HU is used. The threshold T3 is an example of a first threshold.

By comparing the statistical value V of the CT values of the partial pixel column with the threshold T3, it may be accurately determined whether the partial pixel column corresponds to the lung field region or the region W.

P26: In a case where one or more partial pixel columns corresponding to the lung field region are extracted from the pixel column, the generation unit <NUM> selects these partial pixel columns and obtains the minimum value of the CT values of the plurality of pixels included in the selected partial pixel column. The generation unit <NUM> plots the minimum value at a position corresponding to the slice of the CT image <NUM> and the x coordinate of the pixel column, in the plane of the coronal image <NUM>.

P27: The image processing apparatus <NUM> repeats the processing of procedures P22 to P26 for all of the x coordinates of the processing target CT image.

P28: The image processing apparatus <NUM> repeats the processing of procedures P21 to P27 for the CT images <NUM> corresponding to all of the slices of the patient as the diagnosis target. Thus, the coronal image <NUM> of the patient as the diagnosis target is generated.

With such image processing, the pixels of the lung field region and the pixels of the region W in the CT image <NUM> are distinguished from each other, and the coronal image <NUM> is generated using only the CT values of the lung field region. Thus, since the CT values of the region W are computationally ignored, the accurate coronal image <NUM> may be generated by excluding the influence of the region W. By using the accurate coronal image <NUM>, the accurate positions of the upper end and the lower end of the lung field may be obtained.

In the case of simply distinguishing the lung field region from the region W in the CT image <NUM> without generating the coronal image <NUM>, any direction in the CT image <NUM> may be used as the predetermined direction in procedure P22. In this case, the predetermined direction may be a direction parallel to the x axis, or may be a direction intersecting the x axis at a predetermined angle.

<FIG> is a flowchart illustrating an example of image processing in a learning mode performed by the image processing apparatus <NUM> in <FIG>. First, the image processing apparatus <NUM> sets <NUM> for a control variable r indicating any of q (q is an integer of <NUM> or more) patients. The acquisition unit <NUM> acquires n (n is an integer of <NUM> or more) learning CT images <NUM> of the r-th patient, from the CT apparatus <NUM> (operation <NUM>).

Next, the image processing apparatus <NUM> generates the learning coronal image <NUM> of the lung field, from the lung field region of each of the n learning CT images <NUM> (operation <NUM>). The user adds an annotation to the learning coronal image <NUM>, and the generation unit <NUM> generates ground truth region information <NUM> indicating the lung field region in the learning coronal image <NUM> based on the added annotation (operation <NUM>).

Next, the image processing apparatus <NUM> increments r by <NUM> (operation <NUM>), and in a case where r < q, the image processing apparatus <NUM> repeats the processing of operation <NUM> to operation <NUM>.

In a case where r reaches q in operation <NUM>, the learning unit <NUM> causes the learning model to learn the learning coronal image <NUM> and the ground truth region information <NUM> of the <NUM>-th to q-<NUM>-th patients to generate the extraction model <NUM> (operation <NUM>).

<FIG> is a flowchart illustrating an example of image processing in an operation mode performed by the image processing apparatus <NUM> in <FIG>. First, the acquisition unit <NUM> acquires n CT images <NUM> of the patient as the diagnosis target, from the CT apparatus <NUM> (operation <NUM>). The image processing apparatus <NUM> generates the coronal image <NUM> of the lung field, from the lung field region of each of the n CT images <NUM> (operation <NUM>).

Next, the determination unit <NUM> extracts the lung field region from the coronal image <NUM> by using the extraction model <NUM>, and generates the region information <NUM> indicating the extracted lung field region (operation <NUM>). The determination unit <NUM> determines the upper end and the lower end of the lung field by using the region information <NUM>, and generates the position information <NUM> indicating the upper end and the lower end (operation <NUM>).

<FIG> is a flowchart illustrating an example of coronal image generation processing in operation <NUM> in <FIG>. The coronal image generation processing in operation <NUM> in <FIG> is the same as the coronal image generation processing in <FIG>.

First, the image processing apparatus <NUM> sets <NUM> for a control variable i indicating any of the n CT images <NUM>. The identification unit <NUM> selects the i-th CT image <NUM> (operation <NUM>), performs binarization processing on the selected CT image <NUM>, and generates the i-th processing target CT image (operation <NUM>).

The position (x,y) of the pixel in the processing target CT image is described by using xmax (xmax is an integer of <NUM> or more) x coordinates and ymax (ymax is an integer of <NUM> or more) y coordinates. In this case, xmax represents the width of the processing target CT image, and ymax represents the height of the processing target CT image.

On the other hand, the position (x,y) of the pixel in the coronal image <NUM> is described by using xmax x coordinates and n y coordinates. As the x coordinate of the coronal image <NUM>, the same x coordinate as that of the processing target CT image is used. The y coordinate of the coronal image <NUM> corresponds to any slice of the n CT images <NUM>. In this case, xmax represents the width of the coronal image <NUM>, and n represents the height of the coronal image <NUM>.

Next, the image processing apparatus <NUM> sets <NUM> for a control variable j indicating an x coordinate of the coronal image <NUM>. The generation unit <NUM> obtains a pixel value p[j][i] of the pixel at the position (j,i) in the coronal image <NUM>, and plots p[j][i] on the plane of the coronal image <NUM> (operation <NUM>).

Next, the image processing apparatus <NUM> increments j by <NUM> (operation <NUM>), and in a case where j < xmax, the image processing apparatus <NUM> repeats the processing of operation <NUM> and operation <NUM>. In a case where j reaches xmax in operation <NUM>, the image processing apparatus <NUM> increments i by <NUM> (operation <NUM>). In a case where i < n, the image processing apparatus <NUM> repeats the processing of operation <NUM> to operation <NUM>. In a case where i reaches n in operation <NUM>, the image processing apparatus <NUM> ends the processing.

<FIG> is a flowchart illustrating an example of pixel value calculation processing in operation <NUM> in <FIG>. In the pixel value calculation processing in <FIG>, an array buf and an array num are used. The array buf is a variable-length array that stores CT values of one partial pixel column corresponding to the lung field region or the region W. The array num is a variable-length array that stores the CT values of one or a plurality of partial pixel columns corresponding to the lung field region. The initial values of the lengths of the array buf and the array num are <NUM>.

First, the identification unit <NUM> sets <NUM> for a control variable k indicating the y coordinate of the processing target CT image, and compares the CT value c[j][k] of the pixel at the position (j,k) in the processing target CT image, with the threshold T2 (operation <NUM>).

In a case where c [j][k] > T2 (operation <NUM>, YES), the identification unit <NUM> checks the length length(buf) of the array buf (operation <NUM>). In a case where length(buf) = <NUM> (operation <NUM>, YES), the identification unit <NUM> increments k by <NUM> (operation <NUM>), and in a case where k < ymax, the processing in operation <NUM> and subsequent operations are repeated.

In a case where c[j][k] ≤ T2 (operation <NUM>, NO), the identification unit <NUM> adds c[j][k] to the array buf (operation <NUM>). Thus, the CT value of the pixel corresponding to the lung field region or the region W is added to the array buf, and length(buf) is incremented by <NUM>. The identification unit <NUM> performs the processing in operation <NUM> and subsequent operations.

In a case where length(buf) > <NUM> (operation <NUM>, NO), the identification unit <NUM> obtains the statistical value V of one or more CT values included in the array buf, and compares the statistical value V with the threshold T3 (operation <NUM>). In a case where V > T3 (operation <NUM>, YES), the identification unit <NUM> adds one or more CT values included in the array buf to the array num (operation <NUM>). Thus, the CT values of the partial pixel column corresponding to the lung field region are added to the array num.

Next, the identification unit <NUM> initializes the array buf (operation <NUM>). Thus, length(buf) = <NUM>. The identification unit <NUM> performs the processing in operation <NUM> and subsequent operations. In a case where V ≤ T3 (operation <NUM>, NO), the identification unit <NUM> skips the processing of operation <NUM>, and performs the processing in operation <NUM> and subsequent operations. Thus, the CT values of the partial pixel column corresponding to the region W are ignored.

In a case where k reaches ymax in operation <NUM>, the generation unit <NUM> obtains the minimum value of one or more CT values included in the array num, and sets the minimum value for p[j][i] (operation <NUM>). In a case where a CT value is not stored in the array num, the generation unit <NUM> sets a predetermined value equal to or greater than the threshold T2 for p[j][i].

The image processing apparatus <NUM> may generate a coronal image of the hollow organ instead of the coronal image <NUM> of the lung field, by using the coronal image generation processing in <FIG>. In a case where a coronal image of the hollow organ is generated, the determination condition "V > T3?" in operation <NUM> in <FIG> is changed to "V < T3?".

Thus, in a case where V < T3, the identification unit <NUM> adds one or more CT values included in the array buf to the array num (operation <NUM>), and in a case where V ≥ T3, the identification unit <NUM> skips the processing of operation <NUM>. Thus, the CT values of one or a plurality of partial pixel columns corresponding to the region W are stored in the array num.

The image processing apparatus <NUM> may also identify a part corresponding to a region in the medical image by performing the same image processing on the medical image other than the CT image. A magnetic resonance imaging (MRI) image, an ultrasonic image, or the like is used as the medical image other than the CT image.

The configurations of the image processing apparatus <NUM> in <FIG> and the image processing apparatus <NUM> in <FIG> are merely examples, and some components may be omitted or changed according to the use or conditions of the image processing apparatus. For example, in the image processing apparatus <NUM> in <FIG>, the acquisition unit <NUM> may be omitted in a case where the learning CT image <NUM> and the CT image <NUM> are stored in advance in the storage unit <NUM>. In a case where the extraction model <NUM> is generated by another image processing apparatus and is stored in the storage unit <NUM>, the learning unit <NUM> may be omitted. In a case where it is not necessary to determine the upper end and the lower end of the lung field, the determination unit <NUM> may be omitted.

The flowcharts in <FIG> and <FIG> are merely examples, and a part of the processing may be omitted or changed according to the configuration or conditions of the image processing apparatus. For example, in the image processing in <FIG>, the processing in operation <NUM> may be omitted in a case where the learning CT image <NUM> is stored in advance in the storage unit <NUM>. In a case where the extraction model <NUM> is generated by another image processing apparatus and is stored in the storage unit <NUM>, the image processing in <FIG> may be omitted.

In the image processing in <FIG>, in a case where the CT image <NUM> is stored in advance in the storage unit <NUM>, the processing of operation <NUM> may be omitted. In a case where the upper end and the lower end of the lung field are determined from the coronal image <NUM> without using the extraction model <NUM>, the processing in operation <NUM> may be omitted.

The CT images illustrated in <FIG>, <FIG>, and <FIG> are merely examples, and the CT image varies depending on the patient and the body part. The coronal images illustrated in <FIG>, <FIG>, and <FIG> are merely examples, and the coronal image varies depending on the CT images.

<FIG> illustrates a hardware configuration example of an information processing apparatus (computer) used as the image processing apparatus <NUM> in <FIG> and the image processing apparatus <NUM> in <FIG>. The information processing apparatus in <FIG> includes a central processing unit (CPU) <NUM>, a memory <NUM>, an input device <NUM>, an output device <NUM>, an auxiliary storage device <NUM>, a medium driving device <NUM>, and a network coupling device <NUM>. These components are pieces of hardware, and are coupled to each other by a bus <NUM>. The CT apparatus <NUM> in <FIG> may be coupled to the network coupling device <NUM>.

The memory <NUM> is, for example, a semiconductor memory such as a read-only memory (ROM), a random-access memory (RAM), or a flash memory, and stores a program and data to be used in processing. The memory <NUM> may be used as the storage unit <NUM> in <FIG> or the storage unit <NUM> in <FIG>.

The CPU <NUM> (processor) executes a program using, for example, the memory <NUM> to operate as the identification unit <NUM> in <FIG>. The CPU <NUM> executes a program using the memory <NUM> to also operate as acquisition unit <NUM>, the identification unit <NUM>, the generation unit <NUM>, the learning unit <NUM>, and the determination unit <NUM> in <FIG>.

The input device <NUM> is, for example, a keyboard, a pointing device, or the like, and is used for an operator or a user to input instructions or information. The output device <NUM> is, for example, a display device, a printer, a speaker, or the like, and is used for outputting inquiries or instructions to an operator or a user and outputting a processing result. The processing result may be the region information <NUM> or the position information <NUM>.

The auxiliary storage device <NUM> is, for example, a magnetic disk device, an optical disk device, a magneto-optical disk device, a tape device, or the like. The auxiliary storage device <NUM> may be a hard disk drive or a flash memory. The information processing apparatus stores a program and data in the auxiliary storage device <NUM>, and may use the program and data by loading them into the memory <NUM>. The auxiliary storage device <NUM> may be used as the storage unit <NUM> in <FIG> or the storage unit <NUM> in <FIG>.

The medium driving device <NUM> drives a portable recording medium <NUM>, and accesses the contents recorded therein. The portable recording medium <NUM> is a memory device, a flexible disk, an optical disk, a magneto-optical disk, or the like. The portable recording medium <NUM> may be a compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a Universal Serial Bus (USB) memory, or the like. The operator or the user may store a program and data in the portable recording medium <NUM>, and may use the program and data by loading them into the memory <NUM>.

A computer-readable recording medium in which a program and data to be used in processing are stored as described above is a physical (non-transitory) recording medium such as the memory <NUM>, the auxiliary storage device <NUM>, or the portable recording medium <NUM>.

The network coupling device <NUM> is a communication interface circuit that is coupled to a communication network such as a local area network (LAN) or a wide area network (WAN) and that performs data conversion involved in communication. The information processing apparatus may receive a program and data from external devices via the network coupling device <NUM>, and may use the program and data by loading them into the memory <NUM>.

The information processing apparatus may also receive the CT image <NUM> and a processing request from a user terminal via the network coupling device <NUM>, and transmit the region information <NUM> or the position information <NUM> to the user terminal.

The information processing apparatus does not necessarily include all of the components in <FIG>, and part of the components may be omitted depending on the use or conditions. For example, in a case where the information processing apparatus receives a processing request from a user terminal, the input device <NUM> and the output device <NUM> may be omitted. In a case where the portable recording medium <NUM> or the communication network is not used, the medium driving device <NUM> or the network coupling device <NUM> may be omitted.

Claim 1:
An image processing program that causes a computer to execute a process, the process comprising:
extracting a plurality of consecutive pixels corresponding to one of a first part or a second part of a body, from a pixel column in a predetermined direction of an image of the body, wherein each pixel of the plurality of consecutive pixels has an associated computerized tomography, CT, value, and the image of the body is a CT image;
obtaining a statistical value of pixel values of the plurality of consecutive pixels, wherein the statistical value is an average value, a median value, or a mode value of the CT values of the plurality of pixels; and
identifying a part corresponding to the plurality of consecutive pixels, among the first part or the second part, based on the statistical value, wherein the identifying of the part includes identifying the part corresponding to the plurality of consecutive pixels, based on a comparison result between the statistical value and a first threshold,
wherein the extracting of the plurality of consecutive pixels includes extracting the plurality of consecutive pixels based on a comparison result between a second threshold and a pixel value of each of a plurality of pixels included in the pixel column in the predetermined direction,
wherein the first part is a lung field and the second part is a part other than the lung field, and
wherein the process further includes:
by using a computed tomography image of each of a plurality of slices of the body as the image of the body and using each of a plurality of pixel columns in the computed tomography image as the pixel column in the predetermined direction, identifying a position of a pixel column including pixels corresponding to the lung field among the plurality of pixel columns by identifying the part corresponding to the plurality of consecutive pixels,
generating a coronal image of the lung field, using only the CT values of the pixels corresponding to the lung field and based on the position of the pixel column including the pixels corresponding to the lung field, the position being identified in the computed tomography image of each of the plurality of slices, and
identifying an upper end and a lower end of the lung field by using the coronal image.