Apparatus, program, and method for examining biological tissue

Abstract

To achieve an automatic inspection of a biological tissue capable of highly accurate determination.SOLUTION: In the inspection image Tim, a feature amount is associated with a combination of the first coordinates, the second coordinates, and the specific physical quantity. The first coordinate and the second coordinate specify a position on the focal plane when the biological tissue is imaged. The specific physical quantity includes a physical quantity related to light from a substance of a biological tissue present at a position specified by the first coordinates and the second coordinates. The feature quantity includes the intensity of light having a specific physical quantity. The inference model Md extracts the information on the abnormality from the inspection image Tim by performing a three dimensional convolution operation on the feature amount with the first coordinate, the second coordinate, and the specific physical quantity as a first dimension, a second dimension, and a third dimension, respectively, on the inspection image.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present disclosure relates to devices, programs, and methods for examining biological tissue. [Background technology]

[0002] Conventionally, configurations for examining biological tissues are known. For example, JP 2023-551913 A (Patent Document 1) discloses a method and system for predicting a subject's diagnostic status regarding a biological disease or disorder. According to the method and system, the subject's diagnostic status regarding the disease or disorder can be predicted by acquiring a Raman image of the sample, analyzing the Raman spectrum spatially across the sample, generating a temporal Raman profile, and processing the data using a trained model. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2023-551913 [Non-patent literature]

[0004] [Non-Patent Document 1] Qingqing Hong, Xinyi Zhong, Weitong Chen, Zhenghua Zhang, Bin Li, "Hyperspectral Image Classification Network Based on 3D Octave Convolution and Multiscale Depthwise Separable Convolution", ISPRS Int. J. Geo-Inf. 2023, 12(12), 505; https: / / doi.org / 10.3390 / ijgi12120505. [Non-patent document 2] Siqi Wei, Yafei Liu, Mengshan Li, Haijun Huang, Xin Zheng, Lixin Guan, "DCCaps-UNet: A U-Shaped Hyperspectral Semantic Segmentation Model Based on the Depthwise Separable and Conditional Convolution Capsule Network", Remote Sens. 2023, 15(12), 3177; https: / / doi.org / 10.3390 / rs15123177. [Non-patent document 3] Si-Jie Hao, Yuan Wan, Yi-Qiu Xia, Xin Zou, Si-Yang Zheng, "Size-based separation methods of circulating tumor cells", in Advanced Drug Delivery Reviews 125 (2018), pp. 3-20. Summary of the Invention [Problem to be solved by the invention]

[0005] Cancer cells are an example of a biological cause of disease. In the initial diagnosis of cancer, a cytological examination (a biopsy) is typically performed in which cell tissue (specimen) collected from the patient is stained with hematoxylin and eosin (HE), and the stained specimen is observed under a microscope to determine the presence or absence of abnormal cells. Depending on the results of the cytological examination, tissue is subsequently collected and a histological examination (needle biopsy) is performed to enable a more accurate diagnosis. In recent years, in order to obtain rapid and accurate results, many cases have been performed without a cytological examination. In cytological / histological examinations, a laboratory technician prepares a specimen by staining with HE for the specimen collected by the attending physician, and a pathologist then observes the specimen to determine the presence or absence of abnormal cells.

[0006] In recent years, the shortage of pathologists has become a problem. Under these circumstances, if pathologists are always required to make the final decision on cytology / histology, the burden on them may become excessive. Furthermore, it is difficult to obtain cytology / histology results on the same day; it usually takes about one to two weeks for the results to be obtained. The fact that it takes a certain amount of time to obtain cytology results can increase the burden on hospital staff and can also increase the physical and mental burden on patients.

[0007] In this regard, it is possible to shorten the time required to obtain cytology results by introducing automatic analysis processing etc. to images of biological tissues. However, as an alternative to the judgments made by pathologists, automatic examination of biological tissues requires high accuracy.

[0008] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to realize automatic examination of biological tissue that allows highly accurate determination. [Means for solving the problem]

[0009] An apparatus according to one aspect of the present disclosure includes a memory unit and an inference unit. The memory unit stores an examination image of biological tissue. The inference unit uses an inference model to determine whether the examination image contains an abnormality. In the examination image, a feature is associated with a combination of a first coordinate, a second coordinate, and a specific physical quantity. The first coordinate and the second coordinate identify a position on a focal plane when the biological tissue is imaged. The specific physical quantity includes a physical quantity related to light from a substance of the biological tissue present at a position identified by the first coordinate and the second coordinate. The feature includes the intensity of light having the specific physical quantity. The inference model extracts information related to the abnormality from the examination image by performing a three-dimensional convolution operation on the feature, with the first coordinate, the second coordinate, and the specific physical quantity being the first dimension, the second dimension, and the third dimension, respectively.

[0010] An apparatus according to another aspect of the present disclosure includes a storage unit and a learning unit. The storage unit stores training images labeled with abnormalities in biological tissue. The learning unit performs machine learning on an inference model using the training images. In the training images, a feature is associated with a combination of a first coordinate, a second coordinate, and a specific physical quantity. The first coordinate and the second coordinate identify a position on a focal plane when the biological tissue is imaged. The specific physical quantity includes a physical quantity related to light from a substance of the biological tissue present at a position identified by the first coordinate and the second coordinate. The feature includes the intensity of light having the specific physical quantity. The learning unit constructs an inference model that determines information related to an abnormality from the training images by performing a three-dimensional convolution operation on the feature, with the first coordinate, the second coordinate, and the specific physical quantity being the first dimension, the second dimension, and the third dimension, respectively, on the training images.

[0011] A program according to another aspect of the present disclosure, when executed by a processor, causes the processor to acquire an inspection image of biological tissue and determine whether the inspection image contains an abnormality using an inference model. In the inspection image, a feature is associated with a combination of a first coordinate, a second coordinate, and a specific physical quantity. The first coordinate and the second coordinate identify a position on the focal plane when the biological tissue is imaged. The specific physical quantity includes a physical quantity related to light from a substance of the biological tissue present at the position identified by the first coordinate and the second coordinate. The feature includes the intensity of light having the specific physical quantity. The inference model extracts information related to the abnormality from the inspection image by performing a three-dimensional convolution operation on the feature, with the first coordinate, the second coordinate, and the specific physical quantity being the first dimension, the second dimension, and the third dimension, respectively.

[0012] A method according to another aspect of the present disclosure includes the steps of acquiring an inspection image of biological tissue and using an inference model to determine whether the inspection image contains an abnormality. In the inspection image, a feature is associated with a combination of a first coordinate, a second coordinate, and a specific physical quantity. The first coordinate and the second coordinate identify a position on a focal plane when the biological tissue is imaged. The specific physical quantity includes a physical quantity related to light from a substance of the biological tissue present at a position identified by the first coordinate and the second coordinate. The feature includes the intensity of light having the specific physical quantity. The inference model extracts information related to the abnormality from the inspection image by performing a three-dimensional convolution operation on the feature, with the first coordinate, the second coordinate, and the specific physical quantity being the first dimension, the second dimension, and the third dimension, respectively. [Effects of the Invention]

[0013] According to the device, program, and method disclosed herein, an inference model performs a 3D convolution operation on the test image to extract information about abnormalities from the test image, thereby enabling automatic testing of biological tissue with high accuracy. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a block diagram showing an example of a configuration of an information processing device 10 according to an embodiment. [Figure 2] FIG. 1 is a diagram showing an example of Raman spectra obtained by performing Raman spectroscopy on abnormal biological tissue containing cancer cells and normal biological tissue containing no cancer cells. [Figure 3] FIG. 1 is a three-dimensional representation of an inspection image, which is an example of an image acquired by Raman imaging. [Figure 4] 4A to 4C are diagrams showing examples of two-dimensional images for each wave number included in the inspection image of FIG. 3. [Figure 5] FIG. 10 is a diagram illustrating an outline of a convolution operation according to a comparative example. [Figure 6] FIG. 1 is a diagram illustrating an overview of a three-dimensional convolution operation according to an embodiment. [Figure 7]FIG. 7 is a diagram comparing indices relating to cancer determination for each block when the block size of FIGS. 5 and 6 is changed between the embodiment and the comparative example. [Figure 8] FIG. 7 is a diagram comparing indices relating to cancer determination of examination images when the block sizes of FIGS. 5 and 6 are changed between the embodiment and a comparative example. [Figure 9] 10 is a flowchart showing an example of the flow of cancer determination processing executed by a processor that executes the image analysis program of FIG. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference numerals, and their description will not be repeated in principle.

[0016] 1 is a block diagram showing an example of the configuration of an information processing device 10 according to an embodiment. The information processing device 10 is an example of a device for examining biological tissue.

[0017] The information processing device 10 includes a processor 11 (inference unit), a memory 12 (storage unit), an input unit 13, an output unit 14, and a bus 15. The processor 11, the memory 12, the input unit 13, and the output unit 14 are connected to each other via the bus 15 so as to be able to communicate with each other. The information processing device 10 includes, for example, a PC (Personal Computer) or a workstation.

[0018] The processor 11 includes, for example, a CPU (Central Processing Unit). The processor 11 may also include a GPU (Graphics Processing Unit). The processor 11 executes programs stored in the memory 12 to realize each function of the information processing device 10.

[0019] The memory 12 includes a non-volatile memory (e.g., a hard disk) and a volatile memory (e.g., a random access memory (RAM)). The memory 12 stores an inference model Md, an image analysis program Pg1, a machine learning program Pg2, an inspection image Tim, and a training image Lim. Although not shown in FIG. 1, the memory 12 may also store an operating system (OS), data required to execute each program, and the processing results of each program.

[0020] The test image Tim and the learning image Lim are acquired by Raman imaging of a sample containing a patient's biological tissue (e.g., breast tissue). In Raman imaging, a Raman spectrum based on Raman spectroscopy is acquired for each coordinate (pixel) on a focal plane (a plane normal to the optical axis and including the focal point) on which the sample is placed. In Raman spectroscopy, monochromatic light (incident light) from a laser or the like is irradiated onto the focal plane, and Raman scattered light scattered by a substance present at the irradiated position of the incident light is acquired.

[0021] In a Raman spectrum, the intensity (feature quantity) of the Raman scattered light is associated with a wavenumber (specific physical quantity), which is the reciprocal of the wavelength of the Raman scattered light. Raman scattered light is defined as the change in wavelength of the scattered light relative to the incident light in terms of energy, which is a value obtained by multiplying Planck's constant by the wavenumber. Because the wavelength of Raman scattered light scattered by a substance depends on the molecular vibration of the substance, a Raman spectrum often exhibits a peak at a wavenumber specific to the substance. In other words, a Raman spectrum contains information that can identify the substance. Note that the specific physical quantity is not limited to the wavenumber of the Raman scattered light, and may be any physical quantity related to light from a substance in biological tissue present at specific coordinates (for example, light emitted from the substance).

[0022] The training images Lim are images labeled with abnormalities in biological tissue. The labels include, for example, information regarding the presence or absence of cancer cells determined by a pathologist who analyzed the biological tissue. In each of the test images Tim and training images Lim, the intensity of Raman scattered light is associated with a combination of an X coordinate (first coordinate), a Y coordinate (second coordinate), and a wavenumber. That is, each coordinate in each of the test images Tim and training images Lim is associated with the Raman spectrum of the substance located at that coordinate. Each of the test images Tim and training images Lim is an image obtained by superimposing (merging) two-dimensional images parallel to the XY plane, which are composed of the Raman spectrum intensity for each wavenumber within the wavenumber range of Raman spectroscopy.

[0023] The inference model Md extracts information about abnormalities (e.g., cancer cells) contained in the test image Tim from the test image Tim by performing a three-dimensional convolution operation (see Non-Patent Documents 1 and 2) on the intensity of the test image Tim, where the X coordinate (first coordinate), Y coordinate (second coordinate), and wavenumber on the focal plane are the first, second, and third dimensions, respectively. That is, the inference model Md includes a 3D-CNN (convolutional neural network). The three-dimensional convolution operation considers not only information about the substance located at each coordinate but also information about what substances are present around the substance to determine whether or not an abnormality exists at a certain coordinate in the test image Tim.

[0024] The processor 11 (inference unit) executing the image analysis program Pg1 uses the inference model Md to determine whether or not an abnormality is contained in the inspection image Tim. The processor 11 (learning unit) executing the machine learning program Pg2 performs machine learning (e.g., supervised learning) on ​​the inference model Md using the training image Lim. The processor 11 executing the machine learning program Pg2 constructs the inference model Md that determines information about an abnormality from the training image Lim by performing a three-dimensional convolution operation on the training image Lim. Note that machine learning includes deep learning.

[0025] The input unit 13 receives input from the user (for example, GUI (Graphical User Interface) operations, gestures, CUI (Character User Interface) input, command input, or voice input). The input unit 13 includes a mouse, a keyboard, a touch panel, a camera, and a microphone. The output unit 14 outputs the processing results of the processor 11 to the user. The output unit 14 includes, for example, a touch panel, a display, and a speaker.

[0026] FIG. 2 shows an example of Raman spectra obtained by Raman spectroscopy on abnormal tissue containing cancer cells and normal tissue containing no cancer cells. The solid line shows the Raman spectrum of the abnormal tissue, and the dotted line shows the Raman spectrum of the normal tissue. As shown in FIG. 2, the wavenumber range of the Raman spectra is 600 cm -1 ~3050cm -1 Since the Raman spectrum of abnormal biological tissue and the Raman spectrum of normal biological tissue are different from each other, it is possible to distinguish between abnormal biological tissue and normal biological tissue by using this information.

[0027] FIG. 3 is a three-dimensional representation of an inspection image Tim1, which is an example of an image acquired by Raman imaging. As shown in FIG. 3, the X-axis, Y-axis, and wavenumber axis are perpendicular to one another. The XY plane corresponds to the focal plane. The wavenumber axis extends from the origin of the XY plane as an axis perpendicular to the XY plane. Data included in inspection image Tim1 is, in three-dimensional space, in the range of 0 μm to 150 μm on the X-axis, the range of 0 μm to 45 μm on the Y-axis, and the range of 600 cm on the wavenumber axis. -1 ~3050cm -1 It exists in the range of , and forms a rectangular parallelepiped area.

[0028] 4 is a diagram showing examples of two-dimensional images for each wave number included in the inspection image Tim1 in FIG. 3. As shown in FIG. 4, two-dimensional images Tim11, Tim12, Tim13, Tim14, and Tim15 are two-dimensional images superimposed on the inspection image Tim1, each of which is at a wave number of 750 cm. -1,1246cm -1 ,1520cm -1 ,1655cm -1 ,2855cm -1 Based on the principle of Raman spectroscopy, in which the wavelength of Raman scattered light from a substance depends on the molecular vibration of that substance, 2D images Tim11, Tim12, Tim13, Tim14, and Tim15 primarily capture mitochondria, collagen, carotenoids, proteins, and lipids, respectively.

[0029] In the following, a comparison will be made between the configuration according to the embodiment and a comparative example in which one-dimensional convolution calculation is performed on the Raman spectrum, in terms of accuracy in determining whether or not cancer cells are included in the test image Tim (cancer determination).

[0030] FIG. 5 is a diagram illustrating an overview of a convolution operation according to a comparative example. As shown in FIG. 5, an inspection image Tim is divided into multiple blocks Bc. The length of each block Bc in the X-axis direction (box size), the length of each block Bc in the Y-axis direction (box size), and the length of each block Bc in the wavenumber direction are B, B, and R, respectively. A surface of each block Bc on the XY plane includes multiple coordinates. In the comparative example, an average Raman spectrum Sav is derived, in which the average value of the intensities of multiple Raman spectra associated with each of the multiple coordinates is associated with each wavenumber. The average Raman spectrum Sav is input to a 1D-CNN, and a one-dimensional convolution operation is performed on the average Raman spectrum Sav. Information about abnormalities contained in the blocks Bc is extracted from the average Raman spectrum Sav by the 1D-CNN, and a cancer node value (cancer index) and a normal node value are calculated in the output layer of the 1D-CNN. The cancer index is an example of an abnormal value indicating the degree of abnormality of the block Bc.

[0031] If the value of the cancer node is greater than the value of the normal node, the block Bc is determined to contain cancer cells. If the average value of the cancer indexes of multiple blocks Bc exceeds a threshold, the test image Tim is determined to contain cancer cells. The value compared with the threshold in cancer determination may be any statistical value of the cancer indexes of multiple blocks Bc, such as the median, maximum, or minimum value. The threshold can be appropriately determined through experiments or simulations.

[0032] The average Raman spectrum Sav does not include position information of the Raman spectrum for each of the multiple coordinates included in the block Bc. Therefore, cancer diagnosis based on the average Raman spectrum Sav does not take into account information about what substances are present around the substances located at each coordinate.

[0033] FIG. 6 is a diagram illustrating an overview of a three-dimensional convolution operation according to an embodiment. The test image Tim is the same as the test image Tim shown in FIG. 5. As shown in FIG. 6, in the embodiment, the block Bc is input directly to the 3D-CNN, and a three-dimensional convolution operation is performed on the block Bc. Information about abnormalities contained in the block Bc is extracted by the 3D-CNN, and values ​​of cancer nodes and normal nodes in the output layer of the 3D-CNN are calculated. In the embodiment, too, if the average value of the cancer index exceeds a threshold, it is determined that the test image Tim contains cancer cells. In the cancer determination according to the embodiment, information about what substances are present around the substance located at each coordinate is taken into consideration.

[0034] FIG. 7 is a diagram comparing indices related to cancer detection for each block Bc when the block size B in FIGS. 5 and 6 is changed between the embodiment and the comparative example. In FIG. 7, for each of the indices, accuracy, sensitivity, and specificity, which are examples of the indices, a broken line is shown, along with an error band surrounding the broken line. The error band represents the distribution of the indices when cancer detection is performed multiple times for a certain block size B. That is, the lower end of the error band represents the minimum value of the indices, and the upper end of the error band represents the maximum value of the indices, with a dot representing the median value between the lower and upper ends. The plotted data is based on the results of cancer detection for 1.5 μm, 3 μm, 4.5 μm, 6 μm, 7.5 μm, 9 μm, 12 μm, 15 μm, 18 μm, 22.5 μm, and 30 μm. The broken lines labeled 1D and 3D are based on cancer detection by the comparative example and the embodiment, respectively. The same applies to FIG. 8, which will be described next to FIG. 7.

[0035] As shown in FIG. 7, the accuracy of cancer detection according to the embodiment exceeds 0.8 for all block sizes B, and is also higher than the accuracy of cancer detection according to the comparative example. The accuracy of cancer detection according to the embodiment increases in the block size B range of 1.5 μm to 9 μm, remains almost constant in the block size B range of 9 μm to 15 μm, and decreases above 15 μm. The range of 9 μm to 15 μm corresponds to the size of cells contained in the test image Tim (see Non-Patent Document 3). By adjusting the block size B to the size of cells contained in the test image Tim, the accuracy of cancer detection for each block size B can be improved.

[0036] Fig. 8 is a diagram comparing indices related to cancer detection for the test image Tim between the embodiment and the comparative example when the block size B in Figs. 5 and 6 is changed. As shown in Fig. 8, the accuracy of cancer detection by the embodiment exceeds the accuracy of cancer detection by the comparative example for all block sizes B. The accuracy of cancer detection by the embodiment exceeds 0.9 in the range of block sizes B from 3 μm to 22.5 μm.

[0037] Fig. 9 is a flowchart showing an example of the flow of the cancer determination process executed by the processor 11 that executes the image analysis program Pg1 in Fig. 1. In the following, steps will be simply abbreviated as S.

[0038] 9, in S101, the processor 11 acquires an inspection image Tim and proceeds to S102. In S102, the processor 11 calculates cancer indices for multiple blocks Bc included in the inspection image Tim using the inference model Md and proceeds to S103. In S103, the processor 11 calculates an average value of the cancer indices and proceeds to S104. In S104, the processor 11 determines whether the average value of the cancer indices is greater than a threshold value.

[0039] If the average value of the cancer index is greater than the threshold value (YES in S104), the processor 11 determines in S105 that the test image Tim contains cancer cells and proceeds to S107. If the average value of the cancer index is equal to or less than the threshold value (NO in S104), the processor 11 determines in S106 that the test image Tim does not contain cancer cells and proceeds to S107. In S107, the processor 11 outputs the result of the cancer determination to the output unit 14 and ends the process.

[0040] As described above, the device, program, and method according to the embodiment can realize automatic examination of biological tissue, which allows highly accurate determination.

[0041] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present disclosure is defined by the claims, not by the above description, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0042] 10 Information processing device, 11 Processor, 12 Memory, 13 Input unit, 14 Output unit, 15 Bus, B Block size, Bc Block, Lim Learning image, Md Inference model, Pg1 Image analysis program, Pg2 Machine learning program, Sav Average Raman spectrum, Tim, Tim1 Inspection image, Tim11 to Tim15 2D images.

Claims

1. a storage unit for storing the examination image of the biological tissue; an inference unit that determines whether the inspection image contains an abnormality using an inference model; In the inspection image, a feature is associated with a combination of a first coordinate, a second coordinate, and a specific physical quantity; the first and second coordinates identify a position in a focal plane when the biological tissue is imaged; the specific physical quantity includes a physical quantity related to light from a substance of the biological tissue present at a position specified by the first coordinates and the second coordinates, the feature amount includes the intensity of the light having the specific physical amount, The inference model extracts information about the abnormality from the inspection image by performing a three-dimensional convolution operation on the feature quantities, with the first coordinate, the second coordinate, and the specific physical quantity being the first dimension, the second dimension, and the third dimension, respectively.

2. The information regarding the abnormality is calculated by calculating a plurality of abnormality values ​​each indicating the degree of abnormality of a plurality of blocks included in the inspection image; The apparatus according to claim 1 , wherein the inference unit determines whether the inspection image contains an abnormality based on statistics of the plurality of abnormal values.

3. The apparatus according to claim 2 , wherein the size of each of the plurality of blocks corresponds to the size of a cell contained in the biological tissue.

4. the inspection image is obtained by Raman imaging; the light includes Raman scattered light, The apparatus according to any one of claims 1 to 3, wherein the specific physical quantity includes a physical quantity related to the wavelength of the Raman scattered light.

5. a storage unit for storing learning images labeled with abnormalities in biological tissue; a learning unit that performs machine learning on an inference model using the learning image; In the learning image, a feature is associated with a combination of a first coordinate, a second coordinate, and a specific physical quantity; the first and second coordinates identify a position in a focal plane when the biological tissue is imaged; the specific physical quantity includes a physical quantity related to light from a substance of the biological tissue present at a position specified by the first coordinates and the second coordinates, the feature amount includes the intensity of the light having the specific physical amount, The learning unit constructs the inference model that determines information about the anomaly from the training image by performing a three-dimensional convolution operation on the feature quantities of the training image, with the first coordinate, the second coordinate, and the specific physical quantity being the first dimension, the second dimension, and the third dimension, respectively.

6. When executed by a processor, the processor: obtaining an examination image of the biological tissue; A program for determining whether or not an abnormality is included in the inspection image using an inference model, In the inspection image, a feature is associated with a combination of a first coordinate, a second coordinate, and a specific physical quantity; the first and second coordinates identify a position in a focal plane when the biological tissue is imaged; the specific physical quantity includes a physical quantity related to light from a substance of the biological tissue present at a position specified by the first coordinates and the second coordinates, the feature amount includes the intensity of the light having the specific physical amount, The inference model is a program that extracts information about the abnormality from the inspection image by performing a three-dimensional convolution operation on the feature quantities, with the first coordinate, the second coordinate, and the specific physical quantity being the first dimension, the second dimension, and the third dimension, respectively.

7. acquiring an examination image of the biological tissue; and determining whether the inspection image contains an anomaly using an inference model; In the inspection image, a feature is associated with a combination of a first coordinate, a second coordinate, and a specific physical quantity; the first and second coordinates identify a position in a focal plane when the biological tissue is imaged; the specific physical quantity includes a physical quantity related to light from a substance of the biological tissue present at a position specified by the first coordinates and the second coordinates, the feature amount includes the intensity of the light having the specific physical amount, A method in which the inference model extracts information about the anomaly from the inspection image by performing a three-dimensional convolution operation on the feature quantities, with the first coordinate, the second coordinate, and the specific physical quantity being the first dimension, the second dimension, and the third dimension, respectively.

Images