WO2020070885A1 - Dispositif de détermination, programme de détermination et procédé de détermination - Google Patents

Dispositif de détermination, programme de détermination et procédé de détermination

Info

Publication number
WO2020070885A1
WO2020070885A1 PCT/JP2018/037410 JP2018037410W WO2020070885A1 WO 2020070885 A1 WO2020070885 A1 WO 2020070885A1 JP 2018037410 W JP2018037410 W JP 2018037410W WO 2020070885 A1 WO2020070885 A1 WO 2020070885A1
Authority
WO
WIPO (PCT)
Prior art keywords
distribution
feature amount
cell
stimulus
variance
Prior art date
Application number
PCT/JP2018/037410
Other languages
English (en)
Japanese (ja)
Inventor
伸一 古田
Original Assignee
株式会社ニコン
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 株式会社ニコン filed Critical 株式会社ニコン
Priority to PCT/JP2018/037410 priority Critical patent/WO2020070885A1/fr
Publication of WO2020070885A1 publication Critical patent/WO2020070885A1/fr

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M1/00—Apparatus for enzymology or microbiology
    • C12M1/34—Measuring or testing with condition measuring or sensing means, e.g. colony counters
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Definitions

  • the present invention relates to a determination device, a determination program, and a determination method.
  • a feature amount calculating unit that calculates a feature amount from a cell image obtained by imaging a cell, and a distribution of the feature amount calculated from the cell image before applying a stimulus to the cell.
  • the first distribution and the second distribution are compared with a distribution calculation unit that calculates a first distribution, a second distribution that is a distribution of a feature amount calculated from a cell image after a predetermined time has elapsed since the stimulation was applied, and compares the first distribution with the second distribution.
  • a determination device comprising: a comparison unit; and a reaction determination unit that determines a response of the cell to the stimulus based on a result of the comparison by the comparison unit.
  • a process of calculating a feature amount from a cell image obtained by imaging a cell a first distribution that is a distribution of a feature amount calculated from a cell image before applying a stimulus to the cell, A process of calculating a second distribution that is a distribution of a feature amount calculated from a cell image after a predetermined time has elapsed since the application of the stimulus, a process of comparing the first distribution with the second distribution, and a process of comparing And determining the response of the cell to the stimulus based on the result of the comparison.
  • calculating a feature amount from a cell image obtained by imaging a cell a first distribution which is a distribution of a feature amount calculated from a cell image before applying a stimulus to the cell,
  • a process of calculating a second distribution which is a distribution of a feature amount calculated from a cell image after a predetermined time has elapsed since the application of the stimulus, and comparing the first distribution with the second distribution; And determining the response of the cell to the stimulus based on the result of the comparison.
  • FIG. 7 is a diagram illustrating an example of a temporal change of an average value of a distribution of a feature amount according to the present embodiment.
  • FIG. 7 is a diagram illustrating an example of a temporal change in variance of a distribution of a feature amount according to the present embodiment.
  • 5 is a flowchart illustrating an example of a calculation procedure of a calculation unit according to the embodiment.
  • FIG. 8 is a diagram illustrating an example of a result of calculating a feature amount by a feature amount calculation unit according to the embodiment.
  • FIG. 6 is a flowchart showing an example of a detailed process of step 60 shown in FIG.
  • FIG. 4 is a diagram illustrating an example of a change in the distribution of the characteristic amounts of the cells over time according to the present embodiment. It is a figure showing an example of upper variance and lower variance of distribution of a characteristic quantity of this embodiment. It is a figure showing an example of change with time progress of variance of distribution of the feature quantity of the stimulated cell of this embodiment.
  • FIG. 6 is a diagram illustrating an example of a correlation of a lapse of time of variance of distribution of different feature amounts according to the present embodiment. It is a figure showing an example of change with the passage of time of two kinds of distribution of the characteristic quantity of a cell of this embodiment.
  • FIG. 1 is a diagram illustrating an example of a configuration of a microscope observation system 1 according to an embodiment of the present invention.
  • the microscope observation system 1 performs image processing on an image obtained by imaging a cell or the like. In the following description, an image obtained by imaging a cell or the like is also simply referred to as a cell image.
  • the microscope observation system 1 includes a determination device 10, a microscope device 20, and a display unit 30.
  • the microscope device 20 is a biological microscope, and includes a motorized stage 21 and an imaging unit 22.
  • the electric stage 21 can arbitrarily move the position of the imaging target in a predetermined direction (for example, a certain direction in a horizontal two-dimensional plane, or a vertical direction or an axial rotation direction).
  • the imaging unit 22 includes an imaging device such as a charge-coupled device (CCD) or a complementary MOS (CMOS), and captures an image of an object to be imaged on the electric stage 21. Note that the microscope apparatus 20 does not need to include the electric stage 21, and the stage may not operate in a predetermined direction.
  • the microscope device 20 includes, for example, a differential interference contrast microscope (DIC), a phase contrast microscope, a fluorescence microscope, a confocal microscope, a super-resolution microscope, a two-photon excitation fluorescence microscope, and a light sheet microscope. , A light field microscope, a holographic microscope, an optical coherence tomography (OCT), and the like.
  • the microscope device 20 captures an image of the culture vessel placed on the electric stage 21.
  • the culture vessel includes, for example, a well plate WP and a slide chamber.
  • the microscope apparatus 20 irradiates the cells cultured in the many wells W of the well plate WP with light, thereby capturing the transmitted light transmitted through the cells as an image of the cells.
  • the microscope device 20 can acquire images such as a transmitted DIC image of a cell, a phase contrast image, a dark field image, and a bright field image. Further, by irradiating the cells with excitation light for exciting the fluorescent substance, the microscope device 20 captures fluorescence emitted from the biological substance as an image of the cells. Further, the microscope device 20 captures light emission or phosphorescence from the light emitting substance in the cell as an image of the cell.
  • the cells are stained alive and time-lapse photography is performed to obtain a change image of the cells after the cell stimulation.
  • a cell image is obtained by expressing a fluorescent fusion protein or staining a cell alive with a chemical reagent or the like.
  • cells are fixed and stained to obtain a cell image. Fixed cells stop metabolizing. Therefore, in order to observe changes in the cells over time with fixed cells after stimulating the cells, it is necessary to prepare a plurality of cell culture vessels in which the cells are seeded. For example, it may be desired to apply a stimulus to a cell and observe a change in the cell after the first time and a change in the cell after a second time different from the first time. In this case, the cells are fixed and stained after a lapse of the first time after stimulation is applied to the cells, and a cell image is obtained.
  • a cell culture container different from the cells used for observation in the first hour is prepared, and after stimulating the cells and elapse of the second time, the cells are fixed and stained to obtain a cell image.
  • the number of cells used for observing changes in the cells between the first time and the second time is not limited to one. Therefore, images of a plurality of cells are acquired at the first time and the second time, respectively. For example, if the number of cells observing changes in the cells is 1000, 2,000 cells will be imaged in the first time and the second time. Therefore, when trying to acquire details of intracellular changes with respect to the stimulus, a plurality of cell images are required at each imaging timing from the stimulus, and a large amount of cell images are acquired.
  • the microscope device 20 captures, as an image of the above-described cells, light emission or fluorescence from the coloring substance itself taken into the biological substance, or light emission or fluorescence generated by binding of the substance having the chromophore to the biological substance. You may.
  • the microscope observation system 1 can acquire a fluorescence image, a confocal image, a super-resolution image, and a two-photon excitation fluorescence microscope image.
  • the method of acquiring an image of a cell is not limited to an optical microscope.
  • the method of acquiring an image of a cell may be an electron microscope.
  • the correlation may be obtained by using an image obtained by a different method. That is, the type of the cell image may be appropriately selected.
  • the cells in the present embodiment are, for example, primary cultured cells, established cultured cells, cells of tissue sections, and the like.
  • a sample to be observed may be observed using an aggregate of cells, a tissue sample, an organ, or an individual (animal or the like), and an image including cells may be obtained.
  • the state of the cell is not particularly limited, and may be a living state or a fixed state.
  • the state of the cell may be "in-vitro". Of course, the information of the living state and the fixed information may be combined.
  • the cells may be treated with a chemiluminescent or fluorescent protein (for example, a chemiluminescent or fluorescent protein expressed from an introduced gene (eg, green fluorescent protein (GFP)) and observed.
  • a chemiluminescent or fluorescent protein expressed from an introduced gene eg, green fluorescent protein (GFP)
  • cells may be observed using immunostaining or staining with a chemical reagent. You may observe them in combination.
  • the photoprotein to be used can be selected according to the type of discriminating the intranuclear structure (eg, Golgi apparatus) in the cell.
  • Pretreatment for analyzing correlation acquisition such as a means for observing the cells and a method for staining the cells, may be appropriately selected depending on the purpose. For example, to obtain dynamic information of cells by an optimal method to obtain dynamic behavior of cells, and to obtain information on intracellular signal transmission by optimal methods to obtain intracellular signal transmission. It does not matter.
  • the pre-processing selected according to the purpose may be different.
  • the well plate WP has one or more wells W.
  • the well plate WP has 96 8 ⁇ 12 wells W as shown in FIG.
  • the number of the well plates WP is not limited to this, but 48 wells of 6 ⁇ 8, 24 wells of 6 ⁇ 4, 12 wells of 3 ⁇ 4, and 6 wells of 2 ⁇ 3. , 384 wells W of 12 ⁇ 32 or 1536 Ws of 32 ⁇ 48.
  • the cells are cultured in the well W under specific experimental conditions. Specific experimental conditions include temperature, humidity, culture period, elapsed time since the stimulus was applied, type and intensity of the applied stimulus, concentration, amount, presence / absence of the stimulus, induction of biological characteristics, etc. Including.
  • the stimulus is, for example, a physical stimulus such as electricity, sound waves, magnetism, or light, or a chemical stimulus caused by administration of a substance or a drug.
  • Biological characteristics include the stage of cell differentiation, morphology, cell number, behavior of molecules in cells, morphology and behavior of organelles, behavior of various forms, nuclear structures, behavior of DNA molecules, etc. It is a characteristic shown.
  • FIG. 2 is a block diagram illustrating an example of a functional configuration of each unit included in the determination device 10 of the present embodiment.
  • the determination device 10 is a computer device that analyzes an image acquired by the microscope device 20.
  • the determination device 10 determines a response of a cell to a stimulus.
  • the determination device 10 includes a calculation unit 100, a storage unit 200, and a result output unit 300.
  • the image processed by the determination device 10 is not limited to the image captured by the microscope device 20.
  • the image stored in the storage unit 200 included in the determination device 10 or an external storage (not shown) may be used. It may be an image stored in the device in advance.
  • the operation unit 100 functions when the processor executes a program stored in the storage unit 200.
  • some or all of the functional units of the arithmetic unit 100 may be configured by hardware such as an LSI (Large Scale Integration) or an ASIC (Application Specific Integrated Circuit).
  • the calculation unit 100 includes a cell image acquisition unit 101, a feature amount calculation unit 102, a distribution calculation unit 103, a comparison unit 104, a reaction determination unit 105, and a correlation calculation unit 106.
  • the cell image acquisition unit 101 acquires the cell image captured by the imaging unit 22 and supplies the acquired cell image to the feature amount calculation unit 102.
  • the cell images acquired by the cell image acquisition unit 101 include a plurality of images in which the culture state of the cells is captured in chronological order, and a plurality of images in which the cells are cultured under various experimental conditions.
  • the feature amount calculation unit 102 calculates a plurality of types of feature amounts from the cell image supplied by the cell image acquisition unit 101.
  • the feature amount includes the luminance of the cell image, the cell area in the image, the variance of the luminance of the cell image in the image, the shape, and the like. That is, the feature amount includes a feature derived from information acquired from a captured cell image.
  • the feature amount calculation unit 102 calculates, for example, a position distribution of luminance in an acquired image. Using a plurality of images having different time series, from the change in the calculated position distribution of the luminance over a predetermined time, the position information indicating a change in the luminance different from the others is obtained, and the change in the luminance is characterized. It may be an amount.
  • information of a position indicating a different change in luminance may be used as the feature amount.
  • the behavior of the cell within a predetermined time may be used, or the shape of the cell may be changed within a predetermined time.
  • no change within a predetermined time is recognized from the captured cell image, no change may be made or the feature amount may be used.
  • the feature amount calculation unit 102 determines, from each of the plurality of images captured at predetermined time intervals, a healthy cell or a cell that is less affected by cell contraction, a heartbeat cycle, a cell movement speed, and a stimulus.
  • Changes in the degree of aggregation of nuclear chromatin which is an indicator of the growing cell, the rate of change in the number and length of projections of nerve cells, the number of synapses in nerve cells, nerve activity such as changes in membrane potential, changes in intracellular calcium concentration,
  • Dynamic features such as the activity of the second messenger, morphological changes of organelles, behavior of molecules in cells, nuclear morphology, behavior of nuclear structures, and behavior of DNA molecules may be calculated.
  • the feature amount calculation unit 102 may use, for example, a Fourier transform, a wavelet transform, and a time derivative for calculating these feature amounts, and may use a moving average for removing noise.
  • the feature amount calculation unit 102 supplies the calculated plurality of types of feature amounts to the distribution calculation unit 103.
  • the distribution calculation unit 103 acquires a plurality of types of feature amounts supplied by the feature amount calculation unit 102.
  • the distribution calculation unit 103 calculates a first distribution, which is a distribution of a feature amount calculated from the cell image before applying the stimulus to the cell, and a feature amount calculated from the cell image after a lapse of a predetermined time after the stimulus is applied.
  • a second distribution which is a distribution, is calculated.
  • a plurality of feature value values are extracted from the cell image.
  • the distribution calculation unit 103 calculates a distribution of the values of the feature values from the values of the plurality of feature values. That is, the distribution of the feature amount is a distribution of the value of the feature amount calculated from the cell image.
  • a luminance value of a predetermined protein is extracted as a feature amount from a cell image.
  • the cell image contains a plurality of predetermined proteins, and the values of the luminances indicating the respective proteins may be different.
  • the value of the brightness of the predetermined protein as a feature value has a distribution.
  • the distribution of the brightness value of the predetermined protein before applying the stimulus is calculated as the first distribution, and the distribution of the brightness value of the predetermined protein after the lapse of a predetermined time after the application of the stimulus is calculated as the second distribution. Calculate as distribution.
  • a frequency distribution chart can be created from the luminance values and the frequencies of the respective values.
  • the distribution calculation unit 103 calculates a first distribution and a second distribution for each of the obtained plural types of feature amounts.
  • the first distribution which is the distribution of the feature amounts calculated from the cell image before applying the stimulus to the cells, is referred to as a feature amount distribution GT0.
  • a feature amount distribution GTn (n) which is a feature amount distribution GT1, a feature amount distribution GT2,. Is a natural number).
  • the distribution calculation unit 103 calculates a representative value representing the first distribution from the first distribution and a representative value representing the second distribution from the second distribution for each of a plurality of types of feature amounts.
  • the distribution calculation unit 103 calculates a representative value representing the characteristic amount distribution GT0 from the characteristic amount distribution GT0 which is a distribution of the characteristic amount calculated from the cell image before applying the stimulus to the cells. Further, the distribution calculation unit 103 calculates a characteristic amount distribution GT1, a characteristic amount distribution GT2,... Which is a distribution of characteristic amounts calculated from a cell image after a predetermined time has elapsed since the application of the stimulus.
  • a representative value representing each of the feature amount distributions GTn (n is a natural number) is calculated. That is, the distribution calculation unit 103 calculates a representative value representing the distribution for each of the feature amount distributions GT0, GT1, GT2,..., And GTn (n is a natural number).
  • the representative value representing the first distribution is the average value of the first distribution.
  • the representative value representing the second distribution is an average value of the second distribution.
  • the representative value representing the feature amount distribution GT0 is an average value of the feature amount distribution GT0.
  • the distribution calculation unit 103 calculates the variance of the first distribution and the variance of the second distribution.
  • the distribution calculation unit 103 calculates the variance of the first distribution and the variance of the second distribution for each of a plurality of types of feature amounts.
  • the distribution calculation unit 103 calculates the variance of the feature amount distribution GT0 for each of a plurality of types of feature amounts.
  • the distribution calculation unit 103 calculates the variance of the feature amount distribution GT1 for each of the plurality of types of feature amounts.
  • the distribution calculation unit 103 calculates the variance of the feature amount distribution GT2 for each of the plurality of types of feature amounts.
  • the distribution calculation unit 103 calculates the variance of the feature amount distribution GTn (n is a natural number) for each of the plurality of types of feature amounts. That is, the distribution calculation unit 103 calculates the variances of the feature amount distributions GT0, GT1, GT2,..., And the feature amount distribution GTn (n is a natural number) for each of the plurality of types of feature amounts.
  • the distribution calculation unit 103 can calculate information on the distribution of the feature amount.
  • the magnitude of the variation of the distribution of the feature values from the average value is calculated.
  • the variance is used as a value indicating the magnitude of the variation, the present invention is not limited to this. For example, a standard deviation may be used.
  • the distribution of the feature amount may be a half-width, which is an index indicating the extent of the spread of the chevron function.
  • the distribution calculation unit 103 may calculate the median or the mode as a representative value representing the distribution of the feature amount.
  • the distribution calculation unit 103 may selectively use an average value, a median value, or a mode value as a representative value representing the distribution of the feature amount according to the type of experiment.
  • the distribution before applying the stimulus to the cell is compared with the distribution after a predetermined time after the stimulus is applied, and a difference between those distributions is calculated. A representative value of the difference distribution may be used. Therefore, the method of calculating the value of the characteristic amount representing the distribution of the characteristic amount can be appropriately selected by the user, and the value of the representative characteristic amount changes based on the selected calculation method.
  • the comparison unit 104 calculates a representative value of a first distribution, which is a distribution of a feature amount calculated from the cell image before applying the stimulus to the cell, and a feature calculated from the cell image after a predetermined time has elapsed since the application of the stimulus. A comparison is made with a representative value of a second distribution, which is a distribution of the quantity.
  • the comparing unit 104 compares the representative value of the first distribution with the representative value of the second distribution for each of the plurality of types of feature amounts.
  • the comparison unit 104 calculates, for each of a plurality of types of feature amounts, a representative value of a first distribution and a representative value of one second distribution which is a distribution of a feature amount calculated from a cell image at a certain time after applying a stimulus. May be compared.
  • the comparing unit 104 also compares the representative value of the first distribution with the representative values of a plurality of second distributions, which are distributions of feature amounts calculated from cell images at a plurality of times after the stimulus is applied. Good.
  • the comparing unit 104 compares the representative value of the feature amount distribution GT0 with the representative value of the feature amount distribution GT1.
  • the comparing unit 104 compares the representative value of the feature amount distribution GT0 with the representative value of the feature amount distribution GT2.
  • the comparing unit 104 compares the representative value of the feature amount distribution GT0 with the representative value of the feature amount distribution GTn. That is, the comparison unit 104 calculates the representative value of the feature amount distribution GT0 calculated by the distribution calculation unit 103, the representative value of the feature amount distribution GT1, the representative value of the feature amount distribution GT2,..., The feature amount distribution GTn (n is a natural number). Are compared with the representative values.
  • the representative value to be compared performed by the comparing unit 104 may be an average value of the distribution or a variance value of the distribution.
  • the comparison unit 104 compares the first distribution with the second distribution using a representative value representing the first distribution and a representative value representing the second distribution.
  • the comparison unit 104 compares the first distribution with the second distribution using a representative value representing the first distribution and a representative value representing the second distribution.
  • the comparison performed by the comparing unit 104 is to calculate a difference between a representative value representing the first distribution and a representative value representing the second distribution.
  • the comparison performed by the comparing unit 104 may be to calculate a ratio between a representative value representing the first distribution and a representative value representing the second distribution.
  • the comparison unit 104 calculates a difference between a representative value representing the feature amount distribution GT0 and a representative value representing the feature amount distribution GT1.
  • the comparing unit 104 calculates a difference between a representative value representing the feature amount distribution GT0 and a representative value representing the feature amount distribution GT2.
  • the comparison unit 104 calculates a difference between a representative value representing the feature amount distribution GT0 and a representative value representing the feature amount distribution GTn (n is a natural number). That is, when the representative value of the distribution is the average value, the comparing unit 104 calculates the difference between the average value of the feature amount distribution GT0 and the average value of the feature amount distribution GT1, the average value of the feature amount distribution GT0 and the average value of the feature amount distribution GT2. The difference between the average value of the feature amount distribution GT0 and the average value of the feature amount distribution GTn (n is a natural number) are compared.
  • FIG. 3 is a diagram illustrating an example of a temporal change of the average value of the distribution of the feature amounts according to the present embodiment.
  • the characteristic amount distribution GT0 is a distribution of characteristic amounts calculated from a cell image before applying a stimulus to a cell, and the characteristic amount distribution GT1, the characteristic amount distribution GT2, and the characteristic amount distribution GT3 are predetermined after the stimulus is applied. It is a distribution of a feature amount calculated from a cell image after a lapse of time.
  • the average value a1 of the feature amount distribution GT1 is shifted from the average value a0 of the feature amount distribution GT0, and the cell responds to the stimulus.
  • the average value a1 is larger than the average value a0.
  • the cell changes the average value in the direction opposite to the direction in which the average value a0 generated by the response to the stimulus of the cell changes to the average value a1 by negative feedback that suppresses the response to the stimulus.
  • the average value a2 of the feature amount distribution GT2 is smaller than the average value a0 of the feature amount distribution GT0.
  • the average value of the feature amount distribution GT3 is the same as the average value a0 before applying the stimulus to the cells.
  • the average value of the distribution of the feature amount calculated from the cell image before applying the stimulus to the cell and the average value of the distribution of the feature amount calculated from the cell image after a predetermined time has elapsed since the application of the stimulus can be examined.
  • the direction of the reaction is the average value of the distribution of the characteristic amount calculated from the cell image after a predetermined time has elapsed since the stimulus was applied, and the average value of the characteristic amount calculated from the cell image before the stimulus was applied to the cell. This is a direction indicating whether the average value of the distribution has changed in a large direction or a small direction.
  • the determination device 10 calculates the representative value representing the first distribution, which is the distribution of the feature amount calculated from the cell image before applying the stimulus to the cell, and the cell after a lapse of a predetermined time after the stimulus is applied. Using a representative value representing a second distribution which is a distribution of the feature amounts calculated from the image, a change in the feature amount with respect to the stimulus is calculated.
  • the comparison unit 104 compares the variance of the first distribution with the variance of the second distribution.
  • the comparing unit 104 compares the variance of the first distribution with the variance of the second distribution.
  • the comparison unit 104 compares the variance of the first distribution and the variance of the second distribution for each of the plurality of types of feature amounts.
  • the comparison performed by the comparing unit 104 is to calculate a difference between the variance of the first distribution and the variance of the second distribution.
  • the comparison performed by the comparison unit 104 may be to calculate the ratio of the variance of the first distribution to the variance of the second distribution.
  • the comparison unit 104 calculates a difference between the variance of the feature amount distribution GT0 and the variance of the feature amount distribution GT1.
  • the comparing unit 104 calculates a difference between the variance of the feature amount distribution GT0 and the variance of the feature amount distribution GT2. In the same manner, the comparison unit 104 calculates a difference between the variance of the feature amount distribution GT0 and the variance of the feature amount distribution GTn (n is a natural number). That is, the comparison unit 104 calculates the difference between the variance of the feature amount distribution GT0 and the variance of the feature amount distribution GT1, the difference between the variance of the feature amount distribution GT0 and the variance of the feature amount distribution GT2,. And the variance of the feature amount distribution GTn (n is a natural number) are compared.
  • FIG. 4 is a diagram illustrating an example of a temporal change in the variance of the distribution of the feature amount according to the present embodiment.
  • the feature amount distribution GTB is a distribution of a feature amount calculated from a cell image after a lapse of a predetermined time from the application of a stimulus to a cell.
  • the size and time at which cells respond to a stimulus vary from cell to cell.
  • the time when the cell responds to the stimulus is the time when the response starts or the time when the response lasts. For example, for cells observed at a certain time, the detection sensitivity of the response to the stimulus may be low, and the response to the stimulus of the cell may not be determined with sufficient accuracy.
  • some cells start the reaction earlier than the remaining cells, and the distribution of the feature calculated from the cell image of the cell that started these reactions is the feature distribution A-2.
  • the distribution of the feature calculated from the cell images of the remaining cells that have not started the reaction is the feature distribution A-1.
  • the feature amount distribution GTB is observed as a combination of the feature amount distribution A-1 and the feature amount distribution A-2.
  • a feature amount distribution A-2 calculated from a cell image of a cell that has started a reaction and a feature amount distribution A-1 calculated from a cell image of a cell that has not started a reaction are mixed. I have.
  • the shape of the feature amount distribution GTB is different from the shape of the feature amount distribution GT0 calculated from the cell image before applying the stimulus to the cells. Therefore, the variance of the feature amount distribution GTB differs from the variance of the feature amount distribution GT0.
  • the variance of the distribution of the feature calculated from the cell image before applying the stimulus to the cell and the variance of the distribution of the feature calculated from the cell image after a predetermined time has elapsed since the application of the stimulus.
  • the response determination unit 105 determines a response of the cell to the stimulus based on the result of the comparison by the comparison unit 104.
  • the response determination unit 105 determines the response of the cell to the stimulus for each of the plurality of types of feature amounts and for each predetermined time after the stimulus is applied.
  • the reaction determining unit 105 determines the result of the comparison by the comparing unit 104. It is determined that the greater the difference between the representative values of the distribution, the greater the response of the cell to the stimulus. Note that the response determination unit 105 may determine whether or not the cell responds to the stimulus.
  • the response determination unit 105 determines whether the difference between the average value and the variance, which is the result of the comparison by the comparison unit 104, is greater than a predetermined value. It is determined that there has been a response to
  • the correlation calculation unit 106 calculates a correlation between the plurality of types of feature amounts based on the calculation results of the plurality of types of feature amounts calculated by the comparison unit 104.
  • the correlation between the plurality of types of feature values calculated by the correlation calculation unit 106 is a correlation between the plurality of types of feature values with respect to time.
  • the time correlation between a plurality of types of feature values is also referred to as feature value correlation.
  • the correlation calculation unit 106 supplies the calculated feature amount correlation to the result output unit 300.
  • the result output unit 300 outputs the feature amount correlation supplied by the correlation calculation unit 106 to the display unit 30.
  • the result output unit 300 may output the feature value correlation supplied by the correlation calculation unit 106 to an output device other than the display unit 30 or a storage device.
  • the display unit 30 displays the feature amount correlation output by the result output unit 300.
  • FIG. 5 is a flowchart illustrating an example of a calculation procedure of the calculation unit 100 according to the present embodiment. Note that the calculation procedure shown here is an example, and the calculation procedure may be omitted or a calculation procedure may be added.
  • the cell image acquisition unit 101 acquires a cell image (Step S10).
  • the cell images include images of a plurality of types of biological tissues having different sizes, such as genes, proteins, and organelles.
  • the cell image contains cell shape information. Since the cell image contains information on a phenotype, a metabolite, a protein, and a gene, the determination device 10 can analyze the correlation of those time changes.
  • the feature amount calculation unit 102 extracts, for each cell, an image of a cell included in the cell image acquired in step S10 (step S20).
  • the characteristic amount calculation unit 102 extracts a cell image by performing image processing on the cell image.
  • the feature amount calculation unit 102 extracts a cell image by performing contour extraction, pattern matching, and the like on the image.
  • the feature amount calculation unit 102 determines the type of cell in the image of the cell extracted in step S20 (step S30). Further, the characteristic amount calculation unit 102 determines the component of the cell included in the image of the cell extracted in step S20 based on the determination result in step S30 (step S40).
  • components of a cell include organelles such as a cell nucleus, lysosome, Golgi apparatus, and mitochondria, proteins constituting an organelle, and the like.
  • the type of the cell is determined in step S30, the type of the cell does not have to be determined. In this case, if the type of cell to be introduced has been determined in advance, that information may be used. Of course, the type of the cell does not have to be specified.
  • the feature value calculation unit 102 calculates the feature value of the image for each component of the cell determined in step S40 (step S50).
  • the feature amount includes a luminance value of the pixel, an area of a certain area in the image, a variance of the luminance of the pixel, and the like.
  • the feature amount of the image of the cell nucleus includes the total brightness value in the nucleus, the area of the nucleus, and the like.
  • the feature amount of the cytoplasm image includes a total brightness value in the cytoplasm, an area of the cytoplasm, and the like.
  • the feature amount of the image of the entire cell includes the total luminance value in the cell, the area of the cell, and the like.
  • the feature amount of the mitochondrial image includes a fragmentation rate.
  • the feature amount calculation unit 102 may calculate the feature amount by normalizing the feature amount to a value between 0 (zero) and 1, for example.
  • the feature amount calculation unit 102 may calculate the feature amount based on information on experimental conditions for cells associated with the cell image. For example, in the case of a cell image captured in a case where an antibody is reacted with a cell, the characteristic amount calculation unit 102 may calculate a characteristic amount peculiar to a case where an antibody is reacted. In addition, in the case of staining a cell, or in the case of a cell image captured in the case where a fluorescent protein is added to a cell, the feature amount calculation unit 102 may be configured to stain the cell or to apply a fluorescent protein to the cell. May be calculated. In these cases, the storage unit 200 may include an experiment condition storage unit 202.
  • the experimental condition storage unit 202 stores information on experimental conditions for cells associated with the cell images for each cell image.
  • the information on the experimental conditions includes, for example, cell conditions, image acquisition conditions, and cell processing conditions.
  • the cell conditions include, for example, the type of cell and whether it is a control cell or an inhibitory cell.
  • the conditions at the time of acquiring an image include, for example, imaging conditions such as the type of the microscope apparatus used and the magnification at the time of acquiring the image.
  • the processing conditions for the cells include, for example, staining conditions when cells are stained, types of stimulation applied to the cells, and the like.
  • the experiment condition storage unit 202 does not store the experiment condition, the experiment condition may be input using an input unit (not shown).
  • the input unit includes, for example, a touch panel, a mouse, or a keyboard.
  • information may be obtained from another device.
  • the microscope device 20 may obtain information on the experimental conditions.
  • information on the experimental conditions may be obtained from a public database or literature.
  • the captured image may be compared with an image included in a public database or a document, a type of a cell included in the captured image may be specified, and the information may be used.
  • FIG. 6 is a diagram illustrating an example of a calculation result of a feature amount by the feature amount calculation unit 102 according to the present embodiment.
  • the characteristic amount calculation unit 102 calculates a plurality of characteristic amounts for the protein P1 for each cell and for each time.
  • the feature amount calculation unit 102 calculates a feature amount for N cells from the cell C1 to the cell CN. Further, in this example, the feature amount calculation unit 102 calculates the feature amounts for seven times from time 1 to time 7. Further, in this example, the feature amount calculation unit 102 calculates K types of feature amounts from the feature amount k1 to the feature amount kK.
  • the feature amount calculation unit 102 calculates the feature amounts in the directions of the three axes.
  • the axis in the cell direction is described as an axis Nc
  • the axis in the time direction is described as an axis N
  • the axis in the feature amount direction is described as an axis d1.
  • the K kinds of feature amounts from the feature amount k1 to the feature amount kK are combinations of the feature amounts of the protein P1. With respect to proteins other than protein P1 or components in cells other than protein P1, types and combinations of feature amounts may be different.
  • the characteristic amount calculation unit 102 supplies the characteristic amount calculated in step S50 to the distribution calculation unit 103.
  • the feature amount calculated from the cell image by the feature amount calculation unit 102 includes a first feature amount F1 and a second feature amount F2.
  • the first feature value F1 is a feature value of the first element forming the cell
  • the second feature value F2 is a feature value of the second element forming the cell.
  • the correlation calculation unit 106 calculates a feature amount correlation. (Step S60). The details of the process of calculating the feature amount correlation by the correlation calculation unit 106 will be described with reference to FIG.
  • FIG. 7 is a flowchart showing an example of the detailed process of step S60 shown in FIG.
  • the distribution calculating unit 103 calculates, for each of a plurality of types of feature amounts supplied by the feature amount calculating unit 102, a distribution of a feature amount calculated from a cell image before applying a stimulus to a cell, and a feature amount after a predetermined time from the stimulus.
  • the distribution is calculated (step S601).
  • the distribution of the feature amount calculated from the cell image before applying the stimulus to the cells of the first feature amount F1 is referred to as a feature amount distribution GT0-1 and the distribution of the feature amount after a predetermined time from the stimulation of the first feature amount F1.
  • the distribution of the feature amount calculated from the cell image before applying the stimulus to the cells of the second feature amount F2 is referred to as a feature amount distribution GT0-2, and the distribution of the feature amount after a predetermined time from the stimulation of the second feature amount F2 is obtained.
  • the distribution calculation unit 103 includes a feature amount distribution GT0-1 and a feature amount distribution GT0, which are distributions of feature amounts calculated from a cell image before applying a stimulus to each of the first feature amount F1 and the second feature amount F2.
  • the distribution calculation unit 103 calculates a representative value of the distribution for each distribution.
  • FIG. 8 is a diagram illustrating an example of a change over time in the distribution of the characteristic amount of the cell according to the present embodiment.
  • the vertical axis represents the frequency and the horizontal axis represents the feature value.
  • the average value of the feature amount distributions GT0-1 is 0.0, and the variance is 0.8.
  • the average value of the feature amount distribution GT1-1 is 0.3 and the variance is 1.0.
  • the average value of the feature amount distribution GT2-1 is 0.0, and the variance is 1.2.
  • the characteristic amount distribution GT10-1 and the characteristic amount distribution GT20-1 are obtained by converting the characteristic amount distribution GT1-1 and the characteristic amount distribution GT2-1 into characteristic amount distributions calculated from two cell groups showing different responses. This is an example of disassembly and display. Two cell groups showing different reactions cannot be observed separately since they cannot be separated at the time of observation. Here, for the sake of explanation, examples are shown as those that could be observed.
  • the distribution calculating unit 103 calculates the variance of the feature amount distribution GT1-1 instead of the average value of the feature amount distribution GT1-1, instead of the average value of the feature amount distribution GT1-1.
  • the variance of the feature amount distribution GT1-1 may be calculated using the average value of the feature amount distribution GT0-1 which is the distribution of (1) (this variance is referred to as temporal variance).
  • calculating the variance of the feature amount distribution GT1-1 using the average value of the feature amount distribution GT0-1 instead of the average value of the feature amount distribution GT1-1 means that each feature included in the feature amount distribution GT1-1 is calculated.
  • the difference between the quantity and the average value of the feature amount distributions GT0-1 is calculated, and the average of the square of the difference is calculated as the variance of the feature amount distribution GT1-1.
  • the temporal dispersion of GT1-1 is 1.1.
  • the distribution calculation unit 103 uses the average value of the feature amount distribution GT0-1 instead of the average value of the feature amount distribution GT2-1.
  • the variance of the distribution GT2-1 may be calculated.
  • the temporal dispersion of GT2-1 is 1.2.
  • the distribution calculation unit 103 calculates the feature amount distribution GT0-1 which is a distribution of the feature amount calculated from the cell image before applying the stimulus to the cell, and the cell image after a lapse of a predetermined time after the stimulus is applied.
  • a representative value representing the characteristic amount distribution GT2-1 may be calculated based on the characteristic amount distribution GT2-1 which is the distribution of the characteristic amount to be performed.
  • the variance of the feature amount distribution GT1-1 using the average value of the feature amount distribution GT0-1, which is the distribution of the feature amount calculated from the cell image before applying the stimulus to the cell, is more likely to be a response to the stimulation of the cell. Can be determined with higher sensitivity as compared with the case where the variance of the feature amount distribution GT1-1 is calculated using the average value of the feature amount distribution GT1-1 as in the past.
  • the frequency on the vertical axis in FIG. 8 may be the number of cells observed in the image.
  • the distribution calculation unit 103 calculates the calculated characteristic amount distribution GT0-1, the characteristic amount distribution GT1-1, the characteristic amount distribution GT2-1,..., The characteristic amount distribution GTn-1 (n is a natural number), and the characteristic amount distribution GT0-2. , A characteristic value distribution GT1-2, a characteristic value distribution GT2-2,..., A characteristic value distribution GTn-2 (n is a natural number).
  • the comparison unit 104 calculates the representative value of the feature amount distribution GT0-1 calculated by the distribution calculation unit 103 and the representative value of the feature amount distribution GT1-1, the feature amount distribution GT2-1,..., The feature amount distribution GTn-1 (n is a natural number). Are compared with each other (step S602).
  • the feature amount distribution GT0-1 is a distribution of the feature amount calculated from the cell image before applying the stimulus to the cells of the first feature amount F1.
  • the feature amount distribution GTn-1 (n is a natural number) are obtained from a cell image after a lapse of a predetermined time after the stimulation of the first feature amount F1 is applied. This is the distribution of the calculated feature amounts.
  • the feature amount distribution GT0-2 is a distribution of the feature amount calculated from the cell image before applying the stimulus to the cells of the second feature amount F2.
  • the characteristic amount distribution GT1-2, the characteristic amount distribution GT2-2,..., The characteristic amount distribution GTn-2 (n is a natural number) are obtained from a cell image after a lapse of a predetermined time after the stimulation of the second characteristic amount F2 is applied. This is the distribution of the calculated feature amounts. That is, the comparison unit 104 calculates the feature amount distribution GT0-1, which is the distribution of the feature amount calculated from the cell image before applying the stimulus to the cells of the first feature amount F1, and after a predetermined time has elapsed since the stimulus was applied.
  • a feature amount distribution GT0-2 which is a distribution of feature amounts calculated from a cell image before applying a stimulus to cells having the feature amount F2, and a feature calculated from a cell image after a predetermined time has elapsed since the stimulus was applied. .., And the representative value of a feature amount distribution GTn-2 (n is a natural number).
  • the comparison unit 104 calculates the difference between the variance of the feature amount distribution GT0-1 and the variance of the feature amount distribution GT1-1, the difference between the variance of the feature amount distribution GT0-1 and the variance of the feature amount distribution GT2-1,.
  • the difference between the variance of the feature amount distribution GT0-1 and the variance of the feature amount distribution GTn-1 (n is a natural number) is calculated.
  • the comparison unit 104 calculates the difference between the variance of the feature amount distribution GT0-2 and the variance of the feature amount distribution GT1-2, the difference between the variance of the feature amount distribution GT0-2 and the variance of the feature amount distribution GT2-2, ,
  • the differences between the variances of the feature amount distributions GT0-2 and GTn-2 (n is a natural number) are calculated.
  • the comparison unit 104 calculates the variance of the upper distribution GU, which is the distribution of the feature amount equal to or more than the average value of the distribution of the feature amount, and the variance of the lower distribution GL, which is the distribution of the feature amount equal to or more than the average value of the distribution of the feature amount.
  • the feature amount distribution GT0-1 may be compared with the feature amount distribution GT1-1, the feature amount distribution GT2-1,..., The feature amount distribution GTn-1 (n is a natural number).
  • the variance of the upper distribution GU is called an upper variance UD
  • the variance of the lower distribution GL is called a lower variance LD.
  • FIG. 9 is a diagram illustrating an example of the upper variance UD and the lower variance LD of the feature amount distribution according to the present embodiment.
  • the average value of the feature amount distribution GT2-1 shown in FIG. 9 is equal to the average value 0.0 of the feature amount distribution GT0-1 calculated from the cell image before applying the stimulus to the cells.
  • the feature amount distribution GT2-1 includes an upper distribution GU larger than the average value and a lower distribution GL smaller than the average value.
  • the comparing unit 104 converts the feature amount distribution GT2-1 calculated from the cell image after a predetermined time has elapsed since the application of the stimulus into the feature amount distribution GT0-1 calculated from the cell image before the stimulus is applied to the cell. In comparison, it is compared whether the feature amount of the cell has changed to increase or decrease. In the case of a distribution in which left-right symmetry is maintained with respect to the average value, such as the feature amount distribution GT0-1, the upper variance UD and the lower variance LD have the same value. On the other hand, in the case of a distribution in which left and right symmetry is lost with respect to the average value, such as the feature amount distribution GT2-1, the upper variance UD and the lower variance LD have different values.
  • the upper variance UD When the upper variance UD has a larger value than the lower variance LD, it means that the representative value of the cell has increased due to the response to the stimulus. If the upper variance UD has a smaller value than the lower variance LD, it means that the representative value of the cell has changed to decrease due to the response to the stimulus.
  • the comparison unit 104 determines the distribution of the feature amount larger than the predetermined value of the feature amount distribution GT2-1 which is the distribution of the feature amount calculated from the cell image after the lapse of a predetermined time from the application of the stimulus.
  • a feature amount distribution GT0-1 and a feature amount distribution GT2 which are distributions of feature amounts calculated from a cell image before applying a stimulus to a cell, based on a feature amount distribution smaller than a predetermined value of the amount distribution GT2-1. -1 may be compared.
  • the comparing unit 104 calculates the response of the cell to each of the stimuli of the first feature value F1 and the second feature value F2 (Step S602).
  • the comparison unit 104 calculates the response of the cell every predetermined time after the application of the stimulus.
  • FIG. 10 is a diagram illustrating an example of a change in variance of the distribution of the feature amount of the stimulated cell with time according to the present embodiment.
  • the difference D3-1 is shown in order of time.
  • the difference D3-1 is shown in
  • the difference D3-2 is shown in order of time.
  • the comparing unit 104 supplies the calculated result to the correlation calculating unit 106.
  • the correlation calculation unit 106 calculates a correlation between the first feature value F1 and the second feature value F2 based on the calculation result of the first feature value F1 and the calculation result of the second feature value F2 calculated by the comparison unit 104. Is calculated. (Step S604).
  • FIG. 11 is a diagram illustrating an example of a correlation between different feature amounts according to the present embodiment.
  • the correlation calculated by the correlation calculation unit 106 is a correlation between the representative value of the distribution of the first feature value F1 and the representative value of the distribution of the second feature value F2 with respect to the time since the stimulation was applied to the cell. .
  • the correlation between the first feature value F1 and the second feature value F2 is shown by associating the difference D2-1 to the difference D2-3 with respect to the representative value of the distribution of F2 for each time.
  • FIG. 11 shows that there is a positive correlation between the first feature value F1 and the second feature value F2 with respect to the time after the stimulus is applied to the cell.
  • the correlation calculation unit 106 supplies the calculated feature amount correlation to the result output unit 300.
  • the result output unit 300 outputs the feature amount correlation supplied by the correlation calculation unit 106 to the display unit 30 (Step S300).
  • the comparing unit 104 determines the difference between the variance of the feature amount distribution GT0-1 and the variance of the feature amount distribution GT1-1, the variance of the feature amount distribution GT0-1 and the feature amount distribution.
  • the difference between the variance of GT2-1 and the difference between the variance of the feature amount distribution GT0-1 and the variance of the feature amount distribution GTn-1 (n is a natural number) has been described. However, the present invention is not limited to this.
  • the comparison unit 104 calculates the difference between the average value of the feature amount distributions GT0-1 and GT1-1, and the difference between the average value of the feature amount distributions GT0-1 and GT2-1.
  • the difference between the average value of the feature amount distributions GT0-1 and GTn-1 (n is a natural number) may be calculated.
  • the comparison unit 104 calculates the difference between the average value of the feature amount distribution GT0-2 and the average value of the feature amount distribution GT1-2, the average value of the feature amount distribution GT0-2, and the average value of the feature amount distribution GT2-2.
  • the difference between the average value of the feature amount distributions GT0-2 and GTn-2 (n is a natural number) may be calculated.
  • the comparison unit 104 may perform comparison using another representative value representing the distribution in accordance with the comparison result using a certain representative value representing the distribution. For example, a difference between an average value of the feature amount distributions GT0-1 and GT1-1, a difference between an average value of the feature amount distributions GT0-1 and GT2-1,.
  • the variance of the feature amount distribution GT0-1 and the feature amount distribution GT1- 1 When the difference between the average value of the feature amount distributions GT0-1 and GTn-1 (n is a natural number) is smaller than a predetermined value, the variance of the feature amount distribution GT0-1 and the feature amount distribution GT1- 1, the difference between the variance of the feature amount distribution GT0-1 and the variance of the feature amount distribution GT2-1,..., The variance of the feature amount distribution GT0-1 and the feature amount distribution GTn-1 (n is a natural number) ) May be calculated.
  • FIG. 12 is a diagram illustrating an example of a change over time in the distribution of the characteristic amount of the cell according to the present embodiment.
  • the average value of the feature amount distributions GT0-3 and GT3-3 is the average value a0
  • the average value of the feature amount distributions GT1-3 is the average value a3
  • the average value of the feature amount distributions GT2-3 is the average value a4. .
  • the difference between the average value a3 and the average value a0 is larger than a predetermined value, and the difference between the average value a4 and the average value a0 is smaller than the predetermined value.
  • the feature amount distribution GT2-3 has a different shape from the feature amount distribution GT0-3 and a difference in variance, but the difference between the average value a4 and the average value a0 is smaller than a predetermined value. In such a case, if the comparison unit 104 compares the distributions based only on the average value, the response determination unit 105 may not be able to determine the response with sufficient accuracy.
  • the comparison unit 104 calculates the difference between the average value of the feature amount distributions GT0-3 and GT1-3, and the difference between the average value of the feature amount distributions GT0-3 and GT2-3. ,..., When there is a difference between the average value of the feature amount distribution GT0-3 and the average value of the feature amount distribution GTn-3 (n is a natural number) smaller than a predetermined value, Difference between variance and variance of feature amount distribution GT1-3, difference between variance of feature amount distribution GT0-3 and variance of feature amount distribution GT2-3,..., Variance of feature amount distribution GT0-3 and feature amount distribution GTn The difference from the variance of ⁇ 3 (n is a natural number) is compared.
  • the comparing unit 104 compares the first distribution and the second distribution with variance. I do. That is, when the difference between the average value representing the first distribution and the average value representing the second distribution is smaller than the predetermined value, the comparing unit 104 determines the representative value based on the variance of the first distribution and the second distribution. The first distribution and the second distribution are compared using a representative value based on the variance.
  • the determination device 10 of the present embodiment includes the feature amount calculation unit 102, the distribution calculation unit 103, the comparison unit 104, and the reaction determination unit 105.
  • the feature amount calculation unit 102 calculates a feature amount from a cell image obtained by imaging a cell.
  • the distribution calculation unit 103 is calculated from a representative value of a first distribution, which is a distribution of a feature amount calculated from a cell image before applying a stimulus to a cell, and a cell image after a predetermined time has elapsed since the application of a stimulus.
  • a representative value of a second distribution which is a distribution of the feature amount, is calculated.
  • the comparing unit 104 compares the representative values of the first distribution and the second distribution.
  • the response determination unit 105 determines the response of the cell to the stimulus based on the result compared by the comparison unit 104.
  • the determination device 10 of the present embodiment can determine the response of the cell to the stimulus with higher sensitivity than when the comparison between the first distribution and the second distribution is not used.
  • the distribution calculation unit 103 calculates a representative value representing the first distribution from the first distribution, and calculates a representative value representing the second distribution from the second distribution.
  • the comparison unit 104 compares the first distribution with the second distribution using a representative value representing the first distribution and a representative value representing the second distribution.
  • the distribution calculation unit 103 of the present embodiment calculates a representative value representing the second distribution from the second distribution based on the first distribution and the second distribution.
  • the determination of the response to the stimulus of the cell is performed based on only the second distribution, as compared with the case where a representative value representing the second distribution is calculated from the second distribution. It can be performed at high sensitivity.
  • the comparing unit 104 determines another one of the first distribution and the second distribution. Compare with the representative value.
  • the representative value representing the first distribution is the average value of the first distribution
  • the representative value representing the second distribution is the average value of the second distribution.
  • the comparison unit 104 of the present embodiment compares the distribution of the feature value larger than the predetermined value of the second distribution with the distribution of the feature value smaller than the predetermined value of the second distribution. With this configuration, the determination device 10 of the present embodiment can determine whether the characteristic amount of the cell has changed to increase or decrease with respect to the stimulus.
  • the distribution calculation unit 103 of the present embodiment calculates the variance of the first distribution and the variance of the second distribution
  • the comparison unit 104 calculates the variance of the calculated first distribution and the calculated second distribution.
  • the variance of With this configuration the determination device 10 of the present embodiment can easily determine the presence or absence of a response to a stimulus based on the variance of the first distribution and the variance of the second distribution.
  • the second distribution has, for example, two peaks
  • the second distribution is separated into two distributions based on the two peaks in the determination method 10 according to the related art. It was not convenient because it was necessary to determine the presence or absence of
  • the feature amount calculated by the feature amount calculation unit 102 from the cell image includes the first feature amount and the second feature amount.
  • the distribution calculation unit 103 calculates the representative value of the first distribution and the representative value of the second distribution of the first feature amount and the second feature amount, respectively.
  • the comparing unit 104 compares the first distribution and the second distribution of the first feature amount, and also compares the representative value of the first distribution and the representative value of the second distribution of the second feature amount.
  • the response determining unit 105 determines a response to each stimulus of the first feature amount and the second feature amount based on a result compared by the comparing unit 104.
  • the response of the cell to each of the stimuli of the first feature amount and the second feature amount is higher than in the case where the comparison between the first distribution and the second distribution is not used. It can be determined in sensitivity.
  • the first characteristic amount is a characteristic amount of a first element constituting the cell
  • the second characteristic amount is a characteristic amount of a second element constituting the cell.
  • the determination device 10 of the present embodiment includes a correlation calculation unit 106.
  • the correlation calculation unit 106 calculates a correlation between the first element and the second element based on the representative value of the first feature value and the representative value of the second feature value determined by the comparison unit 104. With this configuration, the determination device 10 of the present embodiment can calculate the correlation between the first element and the second element using the result of comparison between the first distribution and the second distribution.
  • a program for executing each process of the determination device 10 in the embodiment of the present invention is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read and executed by a computer system. , The various processes described above may be performed.
  • the “computer system” here may include an OS and hardware such as peripheral devices.
  • the “computer system” also includes a homepage providing environment (or a display environment) if a WWW system is used.
  • the “computer-readable recording medium” includes a writable nonvolatile memory such as a flexible disk, a magneto-optical disk, a ROM, and a flash memory, a portable medium such as a CD-ROM, and a hard disk incorporated in a computer system. Storage device.
  • a “computer-readable recording medium” refers to a volatile memory (for example, a DRAM (Dynamic)) in a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. Random (Access @ Memory)), which includes a program that is held for a certain period of time. Further, the above program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium.
  • a volatile memory for example, a DRAM (Dynamic)
  • Random Access @ Memory
  • the "transmission medium” for transmitting a program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
  • the program may be for realizing a part of the functions described above.
  • a difference file difference program

Landscapes

  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Organic Chemistry (AREA)
  • Zoology (AREA)
  • Wood Science & Technology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Biotechnology (AREA)
  • General Engineering & Computer Science (AREA)
  • Genetics & Genomics (AREA)
  • General Health & Medical Sciences (AREA)
  • Microbiology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • Biochemistry (AREA)
  • Molecular Biology (AREA)
  • Immunology (AREA)
  • Biophysics (AREA)
  • Medicinal Chemistry (AREA)
  • Biomedical Technology (AREA)
  • Sustainable Development (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Apparatus Associated With Microorganisms And Enzymes (AREA)

Abstract

Dispositif de détermination comprenant: une unité de calcul de quantité de caractéristiques pour calculer une quantité de caractéristiques à partir d'une image de cellule obtenue par l'imagerie de cellules; une unité de calcul de distribution pour calculer une première distribution qui est la distribution de la quantité de caractéristiques calculée à partir d'une image de cellule prise avant qu'un stimulus soit fourni aux cellules, et une seconde distribution qui est la distribution de la quantité de caractéristiques calculée à partir d'une image de cellule prise après qu'une période de temps prédéterminée s'est écoulée depuis le stimulus; une unité de comparaison pour comparer la première distribution à la seconde distribution; et une unité de détermination de réaction pour déterminer la réaction des cellules en réponse au stimulus sur la base du résultat de la comparaison effectuée par l'unité de comparaison.
PCT/JP2018/037410 2018-10-05 2018-10-05 Dispositif de détermination, programme de détermination et procédé de détermination WO2020070885A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/JP2018/037410 WO2020070885A1 (fr) 2018-10-05 2018-10-05 Dispositif de détermination, programme de détermination et procédé de détermination

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2018/037410 WO2020070885A1 (fr) 2018-10-05 2018-10-05 Dispositif de détermination, programme de détermination et procédé de détermination

Publications (1)

Publication Number Publication Date
WO2020070885A1 true WO2020070885A1 (fr) 2020-04-09

Family

ID=70055369

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2018/037410 WO2020070885A1 (fr) 2018-10-05 2018-10-05 Dispositif de détermination, programme de détermination et procédé de détermination

Country Status (1)

Country Link
WO (1) WO2020070885A1 (fr)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009064398A (ja) * 2007-09-10 2009-03-26 Olympus Corp 細胞解析方法及び装置並びにプログラム
JP2012202743A (ja) * 2011-03-24 2012-10-22 Yokogawa Electric Corp 画像解析方法および画像解析装置
JP2013543733A (ja) * 2010-11-12 2013-12-09 アッヴィ・インコーポレイテッド 生細胞に対する試験物質の効果を判定する高スループットの光学的方法およびシステム
WO2017154203A1 (fr) * 2016-03-11 2017-09-14 株式会社ニコン Dispositif de traitement d'image, dispositif d'observation, et programme
WO2018066039A1 (fr) * 2016-10-03 2018-04-12 株式会社ニコン Dispositif d'analyse, procédé d'analyse et programme
WO2018087861A1 (fr) * 2016-11-10 2018-05-17 国立大学法人東京大学 Dispositif d'analyse, procédé d'analyse, et programme

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009064398A (ja) * 2007-09-10 2009-03-26 Olympus Corp 細胞解析方法及び装置並びにプログラム
JP2013543733A (ja) * 2010-11-12 2013-12-09 アッヴィ・インコーポレイテッド 生細胞に対する試験物質の効果を判定する高スループットの光学的方法およびシステム
JP2012202743A (ja) * 2011-03-24 2012-10-22 Yokogawa Electric Corp 画像解析方法および画像解析装置
WO2017154203A1 (fr) * 2016-03-11 2017-09-14 株式会社ニコン Dispositif de traitement d'image, dispositif d'observation, et programme
WO2018066039A1 (fr) * 2016-10-03 2018-04-12 株式会社ニコン Dispositif d'analyse, procédé d'analyse et programme
WO2018087861A1 (fr) * 2016-11-10 2018-05-17 国立大学法人東京大学 Dispositif d'analyse, procédé d'analyse, et programme

Similar Documents

Publication Publication Date Title
JP6967232B2 (ja) 画像処理装置、画像処理方法及び画像処理プログラム
JP6756339B2 (ja) 画像処理装置、及び画像処理方法
JP2022105045A (ja) 解析装置
Forouhesh Tehrani et al. Spatial frequency metrics for analysis of microscopic images of musculoskeletal tissues
JP6818041B2 (ja) 解析装置、解析方法、及びプログラム
WO2018193612A1 (fr) Dispositif de calcul de corrélation, procédé de calcul de corrélation, et programme de calcul de corrélation
US20200372652A1 (en) Calculation device, calculation program, and calculation method
US20200043159A1 (en) Analysis device, analysis method, and program
WO2018186120A1 (fr) Procédé d'évaluation de masse cellulaire et dispositif d'analyse d'état de masse cellulaire
US11645859B2 (en) Analysis device, analysis method, analysis program and display device
WO2020070885A1 (fr) Dispositif de détermination, programme de détermination et procédé de détermination
Knighton et al. Toward cardiac tissue characterization using machine learning and light-scattering spectroscopy
Staikopoulos et al. Hyperspectral imaging of endogenous fluorescent metabolic molecules to identify pain states in central nervous system tissue
WO2019159247A1 (fr) Dispositif de calcul, programme d'analyse et procédé d'analyse
WO2018122908A1 (fr) Dispositif d'analyse, programme d'analyse et procédé d'analyse
WO2020090089A1 (fr) Dispositif, procédé et programme de détermination
Wiltgen et al. Automatic interpretation of melanocytic images in confocal laser scanning microscopy
WO2018109826A1 (fr) Dispositif d'analyse, programme d'analyse et procédé d'analyse
WO2018142570A1 (fr) Dispositif de traitement d'image, dispositif d'analyse, procédé de traitement d'image, programme de traitement d'image et dispositif d'affichage
JP2020124220A (ja) 画像処理装置
Myers et al. Two-Photon Imaging of NADH in SKH1 Mice Reveals Changes in Keratinocyte Metabolism with Chronic UVA Exposure
Barba Assessment of Functional Activity in Isolated Cardiomyocytes Using Computational Methods

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18936130

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18936130

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: JP