JP2019088672A - Image processing device, method for operating image processing device and image processing program - Google Patents

Abstract

To provide an image processing device that can confirm a change in state of hepatic fibrosis, and to provide a method for operating an image processing device and an image processing program.SOLUTION: An image processing device performs non-rigid body alignment processing between hepatic areas of first volume data and second volume data that are obtained by imaging subject's liver in two states where morphological change occurs, and outputs an index indicating hardness of liver obtained by analyzing a deformation vector representing a deformed direction and a deformed distance of a corresponding position within two hepatic areas.SELECTED DRAWING: Figure 8

Description

  The present invention relates to image processing of medical images, and more particularly, to an image processing apparatus related to analysis of an image obtained by imaging a liver, an operation method of the image processing apparatus, and an image processing apparatus program.

  Fibrotic diseases are diseases that occur in all organs throughout the body, and cause organ failure in various organs. The term “fibrosis” refers to a state in which the protein “fiber (collagen)” that can be repaired when damaged by inflammation etc. is increased and spread, but when the fibrosis is excessive, the tissue can not be stretched, and the organ Will not function properly. Cirrhosis is one of the fibrotic diseases.

  Conventionally, liver biopsy has been performed to confirm the state of cirrhosis. Alternatively, as liver cirrhosis progresses, the surface of the liver becomes uneven, so using the image obtained by abdominal CT (Computed Tomography), the degree of unevenness of the surface of the liver and / or the balance between the right and left lobes of the liver, etc. Diagnosis of liver cirrhosis has been made by examining Further, in Patent Document 1, images of the liver before and after the morphological change (such as intake and exhalation) are taken using a magnetic resonance imaging (MRI) apparatus, and two cross-sectional images are obtained from each image. The edge of the liver appearing in the two cross-sectional images is extracted, and information on the hardness of the liver is obtained from the index representing the change in the edge.

Unexamined-Japanese-Patent No. 6-311978

  Actually, a liver biopsy is the most accurate way to know the degree of cirrhosis, but a liver biopsy is not suitable for observing changes over time because it is difficult to reproduce sampling at the same position. On the other hand, in the method of Patent Document 1, only analysis of a two-dimensional image of a cross-sectional image is performed, and it is considered that the result changes depending on how to select a cross-section. In addition, since only the marginal information is used, the information inside the organ may not be utilized and an accurate result may not have been obtained. Thus, no effective means for simply and objectively confirming changes in liver stiffness or liver fibrosis has been established.

  Therefore, in the present invention, in order to solve the above-mentioned problems, an image processing apparatus, an operation method of the image processing apparatus, and an image processing apparatus program capable of simply and objectively confirming the state of fibrosis of the liver. Intended to provide.

  An image processing apparatus according to the present invention includes an acquisition unit for acquiring first volume data and second volume data obtained by imaging the liver of a subject in two states in which a morphological change has occurred, and a first volume By performing non-rigid registration processing between the liver area of the data and the liver area of the second volume data, a deformation vector representing the direction and distance at which the corresponding position in the two liver areas is deformed is obtained by analysis. And an index output unit that outputs an index indicating the hardness of the liver.

  An operation method of an image processing apparatus according to the present invention is an operation method of an image processing apparatus including an acquisition unit and an index output unit, wherein the acquisition unit includes two states in which a morphological change occurs in a liver of a subject. The first volume data and the second volume data which were taken in step are acquired, and the index output unit performs non-rigid registration processing between the liver region of the first volume data and the liver region of the second volume data. By doing this, a deformation vector representing the direction and distance by which corresponding positions in the two liver regions are deformed is analyzed, and an index representing the hardness of the acquired liver is output.

  An image processing program according to the present invention comprises a computer, an acquisition unit for acquiring first volume data and second volume data obtained by imaging the liver of a subject in two states in which a morphological change has occurred, A non-rigid registration process is performed between the liver area of one volume data and the liver area of the second volume data to analyze a deformation vector representing the direction and distance at which corresponding positions in two liver areas are deformed Function as an index output unit that outputs an index representing the hardness of the liver acquired.

  Further, it is desirable that the index output unit output the deformation amount acquired based on the magnitude of the deformation vector as an index value representing the index.

  Also, the deformation amount may be an average value of the magnitudes of the deformation vectors.

  Further, the first volume data may be data captured when the subject is in the supine position, and the second volume data may be data captured when the subject is in the prone position. .

  The first volume data is data taken in a state in which the lung field of the subject is expanded, and the second volume data is that the lung field of the subject is from the lung field of the first volume data It may be data captured in a reduced state.

  In addition, the index output unit uses the difference value in the body axis direction of the upper end of the liver area of the first volume data and the upper end position of the liver area of the second volume data to differentiate the hardness level represented by the index It is desirable to perform adjustment to increase the level of hardness represented by the index as the value is larger, or to decrease the level of hardness represented by the index as the difference value decreases.

  "High hardness level" refers to the liver being stiffer than "low hardness level". "Adjustment to increase the level of hardness expressed by the index" adjusts the index to indicate that the liver is harder than the current index, and "Adjustment to lower the level of hardness expressed by the index" is the index Refers to making adjustments to point the liver to a softer state than the current index.

  The index output unit may output, as an index value representing the index, a value obtained by dividing the deformation amount acquired based on the magnitude of the deformation vector by the difference value.

  Also, the index output unit may adjust the index using a table prepared in advance.

  In addition, the index output unit may obtain the index for each of a plurality of liver areas constituting the liver area of the first volume data and the liver area of the second volume data.

  In addition, the index output unit may acquire the index by analyzing a deformation vector of the upper right liver lobe of the liver area of the first volume data and the liver area of the second volume data.

  In addition, the first volume data and the second volume data in which the index is the liver fibrosis level and the index output unit performs imaging in two states in which the liver fibrosis level and the liver of the patient have undergone a morphological change The first volume data and the second volume data of the subject are input to a discriminator which learns the relationship between the liver fibrosis level and the deformation vector using a teacher data set having You may get

  In addition, the index output unit acquires, as an index, the magnitude of the deformation vector of each position of the liver area of the first volume data and the liver area of the second volume data, and the liver area of the first volume data and the second Each position on the outer shape representing the outline of the liver area corresponding to each position in the liver area may be color-coded and displayed in a color corresponding to the index of each position in the liver area of the volume data.

  Further, the index output unit acquires, as an index, a deformation vector of each position of the liver area of the first volume data and the liver area of the second volume data, and the liver area of the first volume data and the second volume data The deformation vector of each position of the liver region may be displayed at each position on the outer shape representing the outline of the liver region corresponding to each position of the liver region.

  Also, the index output preferably performs rigid body registration prior to non-rigid body registration.

  In the rigid registration, it is desirable that the position of the center of gravity of the liver region of the first volume data be matched with the position of the center of gravity of the liver region of the second volume data.

  The rigid body alignment may be performed using the landmark of the liver area of the first volume data and the landmark of the liver area of the second volume data.

  The first volume data and the second volume data are preferably data obtained by imaging any of a CT apparatus, an MRI apparatus, and an ultrasound apparatus.

  Another image processing apparatus according to the present invention comprises a memory for storing instructions for causing a computer to execute, and a processor configured to execute the stored instructions, the processor being configured to morphologically identify a subject's liver. Non-rigid registration between the liver area of the first volume data and the liver area of the second volume data by acquiring the first volume data and the second volume data captured in two states where a change has occurred By performing processing, a deformation vector representing the direction and distance in which corresponding positions in two liver regions are deformed is acquired, the deformation vector is analyzed, an index representing liver stiffness is acquired, and an index is output. Execute the process

  According to the present invention, non-rigid registration processing is performed between liver areas of the first volume data and the second volume data obtained by imaging in two states in which the liver of the subject has undergone a morphological change. , By obtaining a deformation vector representing the direction and distance in which corresponding positions in two liver regions are deformed and outputting an index representing the hardness of the liver, the change in the state of fibrosis in the liver is simplified And it will be possible to confirm objectively.

Diagram showing the schematic configuration of a medical information system A diagram showing a schematic configuration of an image processing apparatus according to a first embodiment of the present invention Image of a normal case where the subject photographed the liver in the supine position and the prone position An image of an abnormal case in which the subject photographed the liver in the supine position and the prone position Image of a normal case in which the subject takes a picture of the liver in the state of exhalation and inhalation Image of an abnormal case in which the subject photographed the liver in the state of exhalation and inhalation Diagram to explain non-rigid registration An example of distribution of magnitudes of deformation vectors Diagram for explaining a plurality of areas constituting a liver region An example in which the deformation vector of each position in the liver region is displayed by an arrow An example in which the magnitudes of deformation vectors at a plurality of positions in the liver region are color-coded A flowchart showing the flow of processing of the image processing apparatus according to the first embodiment A diagram showing a schematic configuration of an image processing apparatus according to a second embodiment of the present invention A flowchart showing the flow of processing of the image processing apparatus according to the second embodiment

  Hereinafter, a medical information system provided with an image processing apparatus according to a first embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing a schematic configuration of a medical information system according to the present embodiment.

  In the medical information system of the present embodiment, as shown in FIG. 1, the image processing apparatus 1, the medical image storage server 2, and the imaging apparatus 3 (hereinafter referred to as modality) can communicate with each other via the network 4 Is connected and configured.

  The modality 3 is, for example, a CT apparatus, an MRI apparatus, and an ultrasonic apparatus, and the photographed three-dimensional volume data conforms to the storage format and communication standard in accordance with the DICOM (Digital Imaging and Communication in Medicine) standard. It is transmitted to and stored in the medical image storage server 2 via the network.

  The image processing apparatus 1 is a general-purpose computer, and has a central processing unit (CPU), a memory (main storage device), a storage (auxiliary storage device), an input / output interface, a communication interface, an input device, a display device, and a data bus Etc., and a known operation system etc. is installed. In addition, a liquid crystal display or the like is included as a display device, and a pointing device such as a keyboard and / or a mouse is included as an input device. The storage is configured by a hard disk or a solid state drive (SSD). Note that a GPU (Graphics Processing Unit) may be provided in the computer as necessary. The computer functions as the image processing apparatus 1 by installing the image processing program of the present embodiment in this computer. Further, the image processing apparatus 1 has a function of transmitting an image to the medical image storage server 2 and receiving an image from the medical image storage server 2, and is performed by executing a software program for each function. .

  The image processing program is distributed by being recorded on a recording medium such as a digital versatile disc (DVD) or a compact disc read only memory (CD-ROM), and installed in a computer from the recording medium. Alternatively, the image processing program is stored in an accessible state from the outside to a storage device or network storage of a server computer connected to a network, and installed after being downloaded to the computer in response to an external request. You may

  As shown in FIG. 2, the image processing apparatus 1 according to the first embodiment has an acquisition unit 10, a liver region extraction unit 11, a global registration unit 12, a local registration unit 13, an index acquisition unit 14, and an index output. The unit 15 is provided.

  The acquisition unit 10 acquires, from the medical image storage server 2, first volume data V1 and second volume data V2 obtained by imaging the liver of the subject in two states in which a morphological change has occurred. Volume data V1 and V2 in the present embodiment are three-dimensional image data captured by a CT apparatus, an MRI apparatus, an ultrasonic imaging apparatus, or the like. Volume data is composed of data representing physical quantities at voxel positions divided into three-dimensional spaces, and data is a value according to the amount of radiation or magnetism transmitted through organs or tissues present at each voxel position, or , An organ or tissue, etc. It is expressed by a value according to the ultrasonic wave reflected.

  The first volume data V1 and the second volume data V2 are images taken in two states before and after a morphological change of the liver occurs. Specifically, morphological changes in the liver are caused by changes in body position or the effects of respiration.

  For example, a morphological change occurs in the liver depending on whether the subject is supine (supine) or prone (prone). Therefore, an image captured when the subject is in the supine position is referred to as first volume data V1, and an image captured when the subject is in the prone position is referred to as second volume data V2. FIG. 3A is an image obtained by photographing a normal liver in a supine position and a prone position. The image under the left a is an axial image, a sagittal image, and a coronal image taken in the supine position. The image under b on the right is an axial image, a sagittal image, and a coronal image taken in the prone position. FIG. 3B is an image of the liver in which liver cirrhosis has progressed in a supine position (a on the left) and a prone position (b on the right). The shape of the upper part of the liver and the shape of the lower part (black line) in the image of the normal example in FIG. 3A are greatly changed, but the change in the shape of the upper part of the liver (black line) in the image of the nonnormal example of FIG. At least two non-normal cases have less change in shape than normal cases.

  Alternatively, a change in lung field size caused by breathing causes a morphological change in the liver. Therefore, an image captured in a state in which the lung field of the subject is expanded is taken as the first volume data V1, and an image captured in a state in which the lung field of the subject is reduced from the lung field of the first volume data V1. As the second volume data V2. In the first volume data V1 and the second volume data V2, it is preferable that the change in the size of the lung field is large, so that the maximum inspiratory state in which the size of the lung field is maximized and the size of the lung field are minimized. It is preferable to shoot in the state of minimum exhalation. FIG. 4A is an image of a normal liver taken with the subject inhaling and exhaling. The image under the left side a is an axial image, a sagittal image, and a coronal image taken in a state of inhalation. Images under b on the right are axial, sagittal and coronal images taken with exhalation. FIG. 4B is an image obtained by photographing a liver in which liver cirrhosis has progressed in a state of inhalation (a on the left side) and a state of exhalation (b on the right side) of a subject. Comparing the shape of the upper part of the liver (white arrow) in the image of the normal case of FIG. 4A with the shape of the upper part of the liver (white arrow) in the image of the nonnormal case of FIG. There is little change in shape.

  The first volume data V1 and the second volume data V2 are preferably captured in the state of maximum inspiration and minimum exhalation in which the size of the lung field greatly changes, but the sizes of the lung field may be different. For example, an image obtained by administering a contrast agent and performing imaging in a plurality of phases can be used. In general, when performing CT imaging using a contrast agent, imaging is performed in four phases according to the state in which the contrast agent diffuses. First, stop breathing in the breathed state, perform two-phase (early arterial phase, late arterial phase) imaging, and then take the remaining two-phase imaging in the exhaled state to obtain lungs. Volume data can be photographed with the size of the field changed. Among the four-phase volume data, volume data in which the size of the lung field is largely changed may be selected to be the first volume data V1 and the second volume data V2.

  Volume data V1 and V2 are stored in advance in the medical image storage server 2 together with patient identification information. The acquisition unit 10 uses the input device such as a keyboard to input the first volume data V1 and the second volume data V2 of the patient to be diagnosed based on the patient identification information input by the user. Are stored in a storage (not shown).

  The liver region extraction unit 11 extracts a liver region from each of the first volume data V1 and the second volume data V2. The liver region is extracted according to the pixel value and the like according to the captured modality, and various known methods can be used.

  The global alignment unit 12 performs global alignment of the liver by rigid body alignment. In rigid registration, registration is performed using only translation and rotation so as not to change the shape of the liver. Specifically, in order to match the position of the center of gravity of the liver area of the first volume data V1 and the position of the center of gravity of the liver area of the second volume data V2, in the respective axial directions of X, Y and Z Move in parallel. Furthermore, optimize the six parameters of parallel translation and rotational movement in the axial direction of each of X, Y, and Z of the rigid transformation model so that the overlapping volume of the two liver regions is maximized. Make it Alternatively, parallel movement and / or rotational movement is performed so that the landmark of the liver area of the second volume data V2 corresponding to the landmark of the liver area of the first volume data V1 approaches. As the landmark, a position representative of the liver may be used, and a position such as the upper end of the liver region, the boundary between the right lobe and the left lobe, the portal vein, or the hepatic artery may be used. One of these landmarks Using the above landmarks, alignment is performed so as to minimize the total distance between the two liver landmarks.

  Next, the local registration unit 13 performs detailed registration in the liver region by non-rigid registration with respect to the liver region on which rigid registration has been performed by the global registration unit 12. In non-rigid registration, registration is performed by changing the shape of at least one of the two livers to be registered. As non-rigid alignment for aligning between two different volume data, linear alignment such as affine transformation and nonlinear alignment using B-spline are known. Alignment of medical images is performed using such linear alignment and non-linear alignment.

Alignment of medical images, each point x _F in the image I _F of the first volume data V1 is to remain fixed, moving each point x _M in the image I _M of the second volume data V2 Then, it is a problem to obtain a function T of geometrical transformation for matching the position of each point x _M of the image I _{M to} the position of each point x _F of the anatomically corresponding image I _F. As shown in FIG. 5, this problem can be solved as an optimization problem that maximizes the degree of coincidence between one image I _F and the image obtained by modifying the other image I _M by T as an evaluation function. it can. There is no way to obtain a global optimum solution, so after setting an initial value, a solution is found by iteratively searching for a better solution using a gradient method or the like. Mutual information can be used for the degree of coincidence. The mutual information amount is used to align images captured with different modalities such as CT images and MRI images, but in the case of images captured with the same modalities, a cross correlation function may be used.

  Specifically, first, as a model of geometric transformation, grid points are provided in volume data using a B-spline transformation model, and rough alignment is performed by searching lattice points by moving the lattice points. Do. After that, detailed alignment on a pixel basis is performed with the Diffecomorphic (differential in-phase) conversion model. That is, first, an optimal solution of the B-spline transformation model is determined, and then an optimal solution of the Diffecomorphic transformation model is obtained with the result as an initial value. This makes it possible to find corresponding positions in the two liver regions between the two liver regions of the first and second volume data after rigid registration.

The index acquisition unit 14 acquires, from corresponding positions in the two liver regions obtained by the non-rigid registration of the local registration unit 13, a deformation vector representing the direction and distance in which the two liver regions are deformed. That is, a vector connecting each point x _M in the liver region of the image I _M corresponding to each point x _F in the liver region of the image I _F found by non-rigid registration is acquired as a deformation vector (note that the image I _F and Image I _M are images after rigid registration of the liver region). Furthermore, an index representing the stiffness of the liver is obtained from the deformation vector field. For example, the amount of deformation of the deformation vector is calculated as an index value.

  FIG. 6 shows an example of the distribution of magnitudes of deformation vectors. In FIG. 6, the horizontal axis indicates the magnitude of the deformation vector (deformation vector length), and the vertical axis indicates the relative frequency. Also, the solid line shows an example of the distribution of the deformation vector of the liver in a state where fibrosis is advanced enough to require transplantation, and the broken line shows the distribution of the deformation vector of the liver in a state of fibrosis but not serious. The dashed-dotted line shows an example of the distribution of deformation vectors of a healthy liver. An average value of the magnitudes of such deformation vectors can be obtained and used as an index value. The average value of the magnitudes of the deformation vectors indicates that the smaller the value, the more advanced the fibrosis. Also, the average value and the standard deviation of the magnitudes of the deformation vectors may be calculated as index values representing the hardness of the liver. Alternatively, the relationship between the distribution of deformation vector size and the level of liver fibrosis may be analyzed statistically from a large number of data, and the liver fibrosis level representing the fibrosis stage may be used as an index. Preferably, the liver fibrosis level corresponds to the fibrosis stage.

  In addition, the index may not be a numerical value, and may indicate the level of hardness of the liver in characters. For example, depending on the mean size of the deformation vector or the level of liver fibrosis, "A" for a healthy liver without evidence of liver cirrhosis, "B" for mild liver cirrhosis, or moderate liver cirrhosis In the case of “C” and in the case of severe cirrhosis “D”, an index indicating the level of liver stiffness may be used. Furthermore, the index is not limited to characters, and may be numbers, or a combination of characters and numbers. Furthermore, the index may be a sentence such as "moderate liver cirrhosis".

  Alternatively, the index acquiring unit 14 may acquire the index for each of a plurality of areas constituting the liver region. The liver is divided into left and right lobes bordering the interspinous mesentery, and is further divided into four sections, an outer area, an inner area, an anterior area, and a posterior area. These areas are further divided into two areas: the caudal lobe area of S1, the posterior outer area of S2 (outer upper area), the anterolateral area of S3 (lower outer area), the inner upper and lower areas of S4 (rectangular leaves) , S5 lower lower area, S6 lower lower area, S7 upper upper area, and S8 upper upper area (see FIG. 7). An index may be determined for each of these areas.

  In addition, since the upper right lobe of the liver has a thin and flat shape among the liver, the morphological change appears largely depending on the subject's body position and the state of respiration. Therefore, it is preferable to analyze the deformation vector of the upper right lobe of the liver region to acquire the index.

  Furthermore, the index acquisition unit 14 may acquire the liver fibrosis level as an index value using a discriminator. For example, data in which the liver fibrosis level is associated with the first volume data V1 and the second volume data V2 obtained by imaging the patient's liver in two states in which a morphological change has occurred are a teacher data set A large number of discriminators are prepared in advance, and machine learning of the relationship between liver fibrosis level and deformation vector is generated in advance, and the first volume data V1 and second The volume data V2 may be input to obtain the liver fibrosis level.

  The index output unit 15 outputs and displays an index value or a character representing an index on a display device such as a liquid crystal display. Specifically, numerical values and characters representing the hardness level are displayed on the screen displaying the volume data as shown in FIGS. 3A, 3B, 4A, and 4B. Alternatively, the index may be output to a printer and printed on paper or the like.

  Alternatively, the index acquisition unit 14 may acquire, as an index, deformation vectors per se of a plurality of positions in the liver region. In this case, the index output unit 15 may display the deformation vector of each position of the liver area at each position on the outer shape representing the outline of the liver area corresponding to each position of the liver area. FIG. 8 is an example in which the deformation vector of each position in the liver region in an axial cross section with the liver is displayed. Further, in FIG. 8, the displacement in the Z direction (body axis direction) is indicated by the black arrow in the positive direction, and the white arrow in the negative direction. In addition, although the displacement in the Z direction is represented by a black arrow and an open arrow in FIG. 8 for convenience, it may be displayed on the display in different colors such as red and blue.

  Furthermore, the index acquisition unit 14 may acquire, as an index, a size obtained from deformation vectors of a plurality of positions in the liver region. In this case, the index output unit 15 may color-code and display each position on the outer shape representing the outer shape of the liver region in a color corresponding to the index of each position in the liver region. FIG. 9 is an example in which the color classification is hatched for convenience. Different hatchings in FIG. 9 correspond to red and blue color coding, respectively. The location of red is that the magnitude of displacement vector is not less than a predetermined value, and the location of blue is that the magnitude of displacement vector is not more than a predetermined value May be shown.

  Next, the flow of processing of the image processing apparatus 1 according to the present embodiment will be described with reference to the flowchart shown in FIG.

  First, when patient identification information is input by the user, two volume data V1 and V2 are acquired by the acquisition unit 10 from the contrast CT image of the patient (ST10).

  Next, a liver region is extracted from the two volume data V1 and V2 acquired by the acquisition unit 10 using the liver region extraction unit 11 (ST11). The global alignment unit 12 translates in the respective axial directions of rigid transformation X, Y, and Z so that the center of gravity of the liver matches. Furthermore, with the parameters of translation to match the center of gravity of the liver as initial values, the six parameters of parallel translation and rotational movement in the axial direction of X, Y and Z are optimized for rigid body registration (ST12) .

  Furthermore, the local registration unit 13 performs non-rigid registration on the liver region for which rigid registration has been performed by the global registration unit 12 to perform detailed registration in the liver region, and the first volume The corresponding position in the liver area of the data V1 and the second volume data V2 is found (ST13).

  The index acquisition unit 14 acquires a deformation vector representing the direction and distance of deformation of the two liver regions of the first volume data V1 and the second volume data V2 based on the result of non-rigid registration (ST14). . Further, an index is acquired from the deformation vector (ST15). The index output unit 15 displays the index on the display (ST16).

  As described above in detail, since the liver deformation vector is acquired by non-rigid registration and the liver stiffness level is acquired, the state of liver fibrosis is simply and objectively confirmed It becomes possible.

  In the first embodiment described above, the rigid body alignment is performed before the non-rigid body alignment. However, for example, as in the case of photographing in the supine position and the prone position, the body of the liver is When imaging of two volume data is performed without largely changing the position in the axial direction, only non-rigid body alignment may be performed without performing rigid body alignment.

  Next, a second embodiment will be described. In the present embodiment, when the morphological change of the liver occurs according to the size of the lung field, a method of acquiring the hardness level more accurately will be described. About the same composition as a 1st embodiment, the same numerals as a 1st embodiment are attached, detailed explanation is omitted, and only different composition is explained in detail.

  The medical information system of the second embodiment is the same as that of the first embodiment, and thus the detailed description is omitted. The image processing apparatus 1a according to the second embodiment, as shown in FIG. 11, includes an acquisition unit 10, a liver region extraction unit 11, a global alignment unit 12, a local alignment unit 13, an index acquisition unit 14a, and an index output. The unit 15 is provided. Further, the index acquisition unit 14a of the present embodiment differs from the first embodiment in that the adjustment unit 16 that adjusts the index is provided.

  As in the first embodiment, the index acquiring unit 14a performs two operations of the first volume data V1 and the second volume data V2 based on the result of the non-rigid body alignment performed by the local alignment unit 13. A deformation vector representing the direction and distance of deformation of one liver region is obtained, and an index is determined based on the deformation vector.

  However, even if the same subject has different exhalation and inhalation states, the results will be different. For example, if the state of breathing when imaging two volume data V1 and V2 is maximum inspiration and minimum exhalation, the size of the lung field changes significantly when liver cirrhosis does not progress, so the amount of deformation of the liver region is Although the value is large, when the change in the size of the lung field when the two volume data V1 and V2 are photographed is small, the amount of deformation of the liver region becomes a relatively small value even if liver cirrhosis has not progressed. On the other hand, when liver cirrhosis progresses, the amount of deformation becomes a small value even if the size of the lung field is largely changed or even if the size of the lung field is not significantly changed.

  In exhalation and inspiration, the position of the diaphragm greatly changes, and the position of the liver moves up and down accordingly. When imaging two volume data V1 and V2 in which the state of breathing has been changed, there is no movement of the body position of the subject with respect to the imaging device, so changes in the size of the lung field It is possible to estimate from the change of

  Therefore, the adjustment unit 16 calculates the difference value in the body axis direction of the upper end position of the liver area of the first volume data V1 and the second volume data V2 of the liver area before alignment by the global alignment unit 12 is performed. Adjustment is performed to increase the hardness level represented by the index as the difference value increases, and the hardness level represented by the index decreases as the difference value decreases. . In other words, if the size of the lung field changes significantly, the size of the mutation vector is larger than if the size of the lung field does not change much, and the index indicates that the liver is soft (the hardness level is low). As you point, make adjustments to increase the hardness level. On the other hand, when the size of the lung field does not change much, the size of the mutation vector becomes smaller and the index is harder than the liver actually (the hardness level is higher than when the size of the lung field changes greatly Make adjustments so as to lower the hardness level, as it points to). As a result, it is possible to add a correction that absorbs differences in lung field size that change depending on the state of exhalation or inhalation.

  Specifically, a value obtained by dividing the deformation amount obtained based on the magnitude of the deformation vector obtained as the index value representing the index by the index acquisition unit 14a by the difference value is used as the index value. Alternatively, depending on the value obtained by dividing the deformation amount by the difference value, the hardness level may be represented by an index represented by letters "A" to "D".

  The adjustment unit 16 of the index acquisition unit 14a prepares a table representing an index according to the deformation amount of the deformation vector and the difference value (index value or index representing the hardness level in characters) in advance. The index may be adjusted. Alternatively, the index may be adjusted by preparing a table representing an index value corresponding to the index obtained from the deformation vector and the difference value.

  Next, the flow of processing of the image processing apparatus 1 according to the present embodiment will be described with reference to the flowchart shown in FIG.

  First, when the user inputs patient identification information, two volume data V1 and V2 are acquired by the acquisition unit 10 from the contrast-enhanced CT image of the patient (ST20).

  Next, a liver region is extracted from the two volume data V1 and V2 acquired by the acquisition unit 10 using the liver region extraction unit 11 (ST21). Here, the adjustment unit 16 acquires the difference value in the body axis direction of the upper end position of the liver region of the two volume data V1 and V2 (ST22).

  Next, the global alignment unit 12 translates in the respective axial directions of rigid body transformations X, Y, and Z so that the center of gravity of the liver matches. Furthermore, with the parameters of translation to match the center of gravity of the liver as initial values, the six parameters of parallel translation and rotational movement in the axial direction of X, Y, and Z are optimized for rigid body registration (ST23) .

  Furthermore, the local registration unit 13 performs non-rigid registration on the liver region for which rigid registration has been performed by the global registration unit 12 to perform detailed registration in the liver region, and the first volume The corresponding position in the liver area of the data V1 and the second volume data V2 is found (ST24).

  The index acquiring unit 14a acquires a deformation vector representing the direction and distance of deformation of the two liver regions of the first volume data V1 and the second volume data V2 based on the result of non-rigid registration (ST25). . Further, an index is acquired from the deformation vector (ST26). Here, the hardness level represented by the index is increased as the difference value increases, and the hardness level represented by the index decreases as the difference value decreases as the difference value increases with the adjustment unit 16. Make adjustment (ST27). The index output unit 15 displays the adjusted index on the display (ST28).

  As described above in detail, non-rigid registration acquires the deformation vector of the liver, obtains the hardness level of the liver, and takes into consideration that the degree of deformation of the liver varies depending on the state of respiration. By doing this, it becomes possible to more accurately obtain the fibrotic state of the liver.

  Although the case where one computer functions as an image processing apparatus has been described above, the functions may be distributed to a plurality of computers.

  In addition, although the case where the general-purpose computer functions as the image processing apparatus has been described above, it may be implemented by a dedicated computer. The dedicated computer may be firmware that executes a program stored in a non-volatile memory, such as a built-in ROM (Read Only Memory) or a flash memory. Furthermore, dedicated circuits such as application specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs) that permanently store programs for executing at least a part of the functions of the image processing apparatus. It may be provided. Alternatively, the program instruction stored in the dedicated circuit may be combined with the program instruction executed by the general-purpose CPU programmed to use the program of the dedicated circuit. As described above, the computer hardware configuration may be combined to execute program instructions.

1, 1a image processing apparatus 2 medical image storage server 3 imaging apparatus 4 network 10 acquisition unit 11 liver region extraction unit 12 global alignment unit 13 local alignment unit 14, 14 a index acquisition unit 15 index output unit 16 adjustment unit V1 first unit Volume data V2 second volume data

Claims (19)

An acquisition unit for acquiring first volume data and second volume data obtained by imaging the liver of the subject in two states in which a morphological change has occurred;
A deformation vector representing a direction and a distance at which corresponding positions in two liver regions are deformed by performing non-rigid registration processing between the liver region of the first volume data and the liver region of the second volume data An index output unit that outputs an index indicating the hardness of the liver acquired by analyzing
Image processing device equipped with   The image processing apparatus according to claim 1, wherein the index output unit outputs, as an index value representing the index, a deformation amount acquired based on a magnitude of the deformation vector.   The image processing apparatus according to claim 2, wherein the deformation amount is an average value of magnitudes of the deformation vectors. The first volume data is data captured when the subject is in a supine position,
The image processing apparatus according to any one of claims 1 to 3, wherein the second volume data is data captured when the subject is in a prone position. The first volume data is data captured in a state in which a lung field of the subject is expanded,
The image processing according to any one of claims 1 to 3, wherein the second volume data is data captured in a state in which a lung field of the subject is smaller than a lung field of the first volume data. apparatus.   The index output unit uses the difference value in the body axis direction of the upper end position of the liver area of the first volume data and the upper end position of the liver area of the second volume data to indicate the hardness indicated by the index. The image according to claim 5, wherein the level is adjusted to increase the level of hardness represented by the index as the difference value increases, or to adjust the level of hardness represented by the index to decrease as the difference value decreases. Processing unit.   The image processing apparatus according to claim 6, wherein the index output unit outputs, as an index value representing the index, a value obtained by dividing the deformation amount acquired based on the magnitude of the deformation vector by the difference value.   The image processing apparatus according to claim 6, wherein the index output unit adjusts the index using a table prepared in advance.   9. The index output unit according to any one of claims 1 to 8, wherein the index is acquired for each of a plurality of liver areas constituting the liver area of the first volume data and the liver area of the second volume data. Image processing apparatus as described.   9. The index output unit according to any one of claims 1 to 8, wherein the index is obtained by analyzing a liver area of the first volume data and a deformation vector of an upper part of a liver right lobe of a liver area of the second volume data. An image processing apparatus according to item 1. The indicator is liver fibrosis level,
Using the teacher data set having the first volume data and the second volume data in which the index output unit performs imaging in two states in which the liver fibrosis level and the liver of the patient are morphologically changed. The first volume data and the second volume data of the subject are input to a discriminator that has learned the relationship between the liver fibrosis level and the deformation vector, and the liver fibrosis level is acquired. An image processing apparatus according to claim 1.   The index output unit acquires, as the index, the size of the deformation vector at each position of the liver area of the first volume data and the liver area of the second volume data, and the liver of the first volume data Each position on the outline shape representing the outline of the liver area corresponding to each position of the liver area is displayed by color according to the index according to each index of the area and the liver area of the second volume data. The image processing apparatus according to 1).   The index output unit acquires, as the index, the deformation vector at each position of the liver area of the first volume data and the liver area of the second volume data, and the liver area of the first volume data and The image processing apparatus according to claim 1, wherein the deformation vector at each position of the liver region of the second volume data is displayed at each position on an outer shape representing an outer shape of the liver region corresponding to each position of the liver region.   The image processing apparatus according to any one of claims 1 to 13, wherein the index output unit performs rigid body registration before the non-rigid body registration.   The image processing apparatus according to claim 14, wherein the rigid registration aligns the position of the center of gravity of the liver area of the first volume data with the position of the center of gravity of the liver area of the second volume data.   The image processing apparatus according to claim 14, wherein the rigid body registration is performed using a landmark of a liver area of the first volume data and a landmark of a liver area of the second volume data.   The image according to any one of claims 1 to 16, wherein the first volume data and the second volume data are data obtained by imaging of any one of a CT apparatus, an MRI apparatus, and an ultrasound apparatus. Processing unit. An operation method of an image processing apparatus comprising an acquisition unit and an index output unit,
The acquisition unit acquires first volume data and second volume data obtained by imaging the liver of the subject in two states in which a morphological change has occurred,
A direction in which corresponding positions in two liver regions are deformed by the index output unit performing non-rigid registration processing between the liver region of the first volume data and the liver region of the second volume data And an operation method of the image processing apparatus which outputs an index representing the hardness of the liver acquired by analyzing a deformation vector representing the distance. Computer,
An acquisition unit for acquiring first volume data and second volume data obtained by imaging the liver of the subject in two states in which a morphological change has occurred;
A deformation vector representing a direction and a distance at which corresponding positions in two liver regions are deformed by performing non-rigid registration processing between the liver region of the first volume data and the liver region of the second volume data An image processing program that functions as an index output unit that outputs an index representing the hardness of the liver acquired by analyzing the.