JP2009064398A - Cell analysis method, apparatus and program - Google Patents

Cell analysis method, apparatus and program Download PDF

Info

Publication number
JP2009064398A
JP2009064398A JP2007234077A JP2007234077A JP2009064398A JP 2009064398 A JP2009064398 A JP 2009064398A JP 2007234077 A JP2007234077 A JP 2007234077A JP 2007234077 A JP2007234077 A JP 2007234077A JP 2009064398 A JP2009064398 A JP 2009064398A
Authority
JP
Japan
Prior art keywords
cell
distribution characteristic
step
luminance
distribution
Prior art date
Legal status (The legal status 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 status listed.)
Granted
Application number
JP2007234077A
Other languages
Japanese (ja)
Other versions
JP4922109B2 (en
Inventor
浩輔 ▲高▼木
Yuichiro Matsuo
Yoshihiro Shimada
Kosuke Takagi
佳弘 島田
祐一郎 松尾
Original Assignee
Olympus Corp
オリンパス株式会社
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 Olympus Corp, オリンパス株式会社 filed Critical Olympus Corp
Priority to JP2007234077A priority Critical patent/JP4922109B2/en
Publication of JP2009064398A publication Critical patent/JP2009064398A/en
Application granted granted Critical
Publication of JP4922109B2 publication Critical patent/JP4922109B2/en
Application status is Active legal-status Critical
Anticipated expiration legal-status Critical

Links

Images

Abstract

<P>PROBLEM TO BE SOLVED: To provide a cell analysis method, apparatus and program that can increase the accuracy of evaluation of drug reactions of cells. <P>SOLUTION: The cell analysis method includes an imaging step of imaging each cell group cultured under a different condition, a luminance measurement step of measuring the luminance of each cell from the cell image acquired in the imaging step, a distribution characteristic creation step of creating distribution characteristics of the luminance data under each condition, and a normalization step of normalizing the distribution characteristics created under each condition. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a cell analysis method, a cell analysis program, and a cell analysis device used for pathological diagnosis of cancer and the like using living cells and drug screening.

Evaluating the effects of various drugs and searching for particularly effective drugs are generally referred to as drug screening. In particular, in drug screening using living cells, after the drug is administered, an appropriate stimulus is applied to the cultured cells, and the difference in the cells from the cells that have not been stimulated is quantified to measure the drug effect.
For example, in drug screening using fluorescence, a fluorescent protein gene is administered to a cell, and the amount of fluorescence emitted from the cell is detected after a lapse of time, thereby quantifying the chemical reaction of the cell and measuring the drug effect. To do. In this case, the greater the expression level of a specific gene in the cell, the greater the amount of fluorescence.

In general, the drug reaction as described above is observed by an examiner by visually observing cell changes before and after drug administration with a microscope or the like. However, the work by the inspector not only consumes a great deal of labor, but also has a problem of lack of quantitativeness. Therefore, in recent years, a cell analysis apparatus that automatically performs such cell analysis has been proposed (for example, see Patent Document 1).
In Patent Document 1, a container containing cells is placed on a stage, a sample is imaged by a camera to obtain a cell image to be analyzed, and the chemical reaction of the cell is quantified by analyzing the cell image. A cell analysis device is disclosed.
JP 2001-307066 A

  However, when the drug reaction in the cell is automatically quantified by image analysis, there is a problem that the analysis accuracy decreases due to noise or the like included in the image. For example, cell brightness (fluorescence amount) in cell images has various effects such as the amount of illumination light applied to the sample during image acquisition, autofluorescence of the container containing the sample, and changes in the thickness of the container. Since these appear in the cell image as noise, the analysis accuracy decreases.

  This invention is made | formed in view of the situation mentioned above, Comprising: It aims at providing the cell analysis method and apparatus which can improve the evaluation precision of the drug reaction of a cell, and a program.

In order to solve the above problems, the present invention employs the following means.
The present invention provides an imaging step of imaging each cell group cultured under different conditions, a luminance measurement step of measuring the luminance of each cell from the cell image acquired in the imaging step, and luminance data for each of the conditions There is provided a cell analysis method including a distribution characteristic creation step of creating a distribution characteristic of the above and a normalization step of normalizing the distribution characteristic created for each of the conditions.

  According to the present invention, a plurality of cell images are acquired by imaging each cell group cultured under different conditions. Subsequently, the luminance of each cell is measured in each acquired cell image, a distribution characteristic of luminance data is created for each culture condition, and the distribution characteristic is normalized. Thus, by normalizing distribution characteristics, errors due to differences in culture conditions can be reduced, and highly reliable distribution characteristics can be obtained. Therefore, by comparing these distribution characteristics between the culture conditions, it becomes possible to more accurately evaluate the drug reaction of the cells.

  In the cell analysis method, the normalization step includes a reference setting step of setting a reference distribution characteristic serving as a reference based on a plurality of the distribution characteristics, and the distribution so that each of the distribution characteristics matches the reference distribution characteristic. A distribution characteristic conversion step of normalizing the distribution characteristic by linearly converting each luminance data in the characteristic.

  Since the reference distribution characteristics are set and each luminance data is linearly converted so that each distribution characteristic matches the reference distribution characteristics, normalization can be easily performed.

  In the cell analysis method, the distribution characteristic conversion step may linearly convert each luminance data in the distribution characteristic so that each distribution characteristic peak and the reference distribution characteristic peak coincide with each other.

For example, when the reaction evaluation based on the drug concentration is performed, not all cells respond to the drug, and most of them are unresponsive in many cases. In addition, since the non-reactive cells exhibit substantially the same luminance, it can be determined that the peak in the distribution characteristic is the data indicated by the non-reactive cells. Therefore, by combining the peaks in each distribution characteristic, it is possible to easily reduce the error between the distribution characteristics.
Furthermore, when performing the reaction evaluation based on the drug concentration, it is preferable to compare luminance data indicating a high value between the drug concentrations. In such a case, according to the present invention, the luminance data in the distribution characteristic is linearly converted to normalize the distribution characteristic so that the peak of the distribution characteristic coincides with the peak of the reference distribution characteristic. Therefore, it is possible to accurately grasp the change in the luminance data in the luminance range that is important when performing the measurement, and it is possible to obtain a highly reliable evaluation result of the drug reaction.

The cell analysis method may include a correction step of comparing the normalized distribution characteristics and deleting or correcting luminance data that shows a different tendency compared to other distribution characteristics.
In this way, it is possible to delete luminance data with low reliability. As a result, it is possible to acquire distribution characteristics with higher reliability.

  The present invention is a cell analysis apparatus that performs cell analysis using a cell image acquired by imaging a plurality of cells cultured under different conditions, and the luminance for measuring the luminance of each cell from the cell image There is provided a cell analyzer comprising measurement means, distribution characteristic creation means for creating distribution characteristics of luminance data for each condition, and normalization means for normalizing the distribution characteristics created for each condition.

  The present invention is a cell analysis program for performing cell analysis using a cell image acquired by imaging a plurality of cells cultured under different conditions, and measuring the luminance of each cell from the cell image Analysis program for causing a computer to execute a luminance measurement step, a distribution characteristic creation step for creating a distribution characteristic of luminance data for each condition, and a normalization step for normalizing the distribution characteristic created for each condition I will provide a.

  According to this invention, there exists an effect that the evaluation precision of the drug reaction of a cell can be improved.

  Hereinafter, an embodiment of a cell analysis method, a cell analysis program, and a cell analysis apparatus of the present invention will be described with reference to the drawings.

FIG. 1 is a diagram showing a configuration of a cell analysis apparatus according to an embodiment of the present invention.
As shown in FIG. 1, the cell analyzer according to the present embodiment includes a stage 101 on which a sample plate 1 is mounted, an excitation light source 102, a power supply unit 103 connected to the excitation light source 102, and an excitation light source 102. A shutter unit 104 that turns on and off the light, a lens 105 that converts the light into parallel light, a filter unit 106 that selects the wavelength of the excitation light that is incident on the sample, and the excitation light that is reflected and incident on the sample. A mirror set 107 that transmits light, an objective lens 108 that condenses excitation light on a specimen, an imaging lens 109 that condenses fluorescence that is collected by the objective lens 108 and passes through the mirror set 107, and an imaged fluorescence. Controls the CCD camera 110 for imaging, the shutter unit 104, the filter unit 106, and the mirror set 107. And Nibasaru control box 111, and stage controller 115 controls the stage 101, along with processing the fluorescence captured by the CCD camera 110, and a computer 112 for controlling the universal control box 111 and the stage controller 115.

  As the sample plate 1, as shown in FIG. 2, for example, a plate in which 15 wells 1a having a diameter of 20 mm and a depth of 15 mm are arranged in 3 rows and 5 columns is used. Cells stained with a fluorescent dye to be examined are seeded in each well 1a. The fluorescent dye used in the present embodiment is a dye that can emit fluorescence while keeping cells alive, such as GFP, and is stained in the cytoplasm. In addition, a barcode 1 b is attached to the sample plate 1. The bar code 1b is read by a bar code reader (not shown), so that the bar code 1b is collated with the database, and the consistency between the sample plate 1 and the data in the database is maintained.

The stage 101 is mounted with the sample plate 1 and can be electrically driven and driven two-dimensionally in the horizontal direction to position the sample plate 1 at an arbitrary position.
As the excitation light source 102, a mercury light source or the like is used. The shutter unit 104 is disposed on the optical path of the excitation light emitted from the excitation light source 102 and incorporates a shutter plate 104a that can be opened and closed.

  The filter unit 106 includes two excitation filters 106a, and can irradiate the sample with two types of excitation light having different wavelengths. The mirror set 107 includes a dichroic mirror 107A that reflects excitation light and transmits fluorescence, and an excitation filter 107D and an absorption filter 107C attached thereto.

The mirror set 107 is stored in a mirror holder (not shown) for a necessary excitation wavelength, and an optical path can be arbitrarily switched by an electric unit (not shown). On the reflected light path of the mirror set 107, the objective lens 108 and the sample plate 1 are arranged. An imaging lens 109 and a CCD camera 110 are disposed on the transmission light path of the mirror set 107. The CCD camera 110 is, for example, a 1 million pixel camera with 12 bits and 1000 × 1000 pixels.
A keyboard 113, a monitor 114, and a mouse 116 are connected to the computer 112.

A cell analysis method using the cell analysis apparatus according to this embodiment configured as described above will be described with reference to the drawings.
First, in the first row of the well 1a of the sample plate 1 (see FIG. 2), for example, a compound A that is a drug candidate for implanting cells is administered at a concentration a. Similarly, in the second row, cells are implanted and Compound A as a drug candidate is administered at a concentration b. Further, on the third line, cells are implanted and compound A as a drug candidate is administered at a concentration c. Thus, in this embodiment, a plurality of cells are cultured under three different conditions. Each well 1a is given a well number. Thereby, the culture condition of the cell can be discriminated by the well number.

  Subsequently, the inspector sets the sample plate 1 on the stage 101, activates a control program (not shown) on the computer 112, reads the barcode (ID: 10001) of the sample plate 1, and starts observation and photographing. . Thereby, a cell image is acquired for every well 1a, and a well number and a cell image are matched and preserve | saved at the hard disk in the computer 112 (imaging process).

  When image acquisition is completed for all wells, the examiner activates a cell analysis program on the computer 112 and instructs the start of analysis. Thereby, the cell analysis program is executed.

Hereinafter, a cell analysis method realized by executing a cell analysis program by a computer will be specifically described with reference to FIG. FIG. 3 is a flowchart showing the procedure of the cell analysis method according to the present embodiment.
First, in step SA1, the cell image stored in the hard disk of the computer 112 is loaded into a memory (not shown).
In step SA2, a plurality of cell images are classified for each culture condition based on the well number. In step SA3, the luminance of each cell is measured from the cell image for each condition, and a luminance data set is created for each condition (luminance measurement step). Here, in general, the luminance varies depending on the location even within one cell. For this reason, in this embodiment, the average luminance of the whole cell or the maximum luminance is adopted as the luminance data.

In subsequent step SA4, a distribution characteristic is created from the luminance data set under each condition (distribution characteristic creation step). In the present embodiment, since the cells are cultured under the three conditions of concentrations a, b, and c, three distribution characteristics are created.
FIG. 4 shows an example of luminance distribution characteristics. As shown in FIG. 4, the luminance distribution characteristic is obtained as a graph in which the horizontal axis represents luminance and the vertical axis represents frequency. The frequency may be, for example, the number of cells or a ratio obtained by dividing the number by a predetermined value. As shown in FIG. 4, for example, in the case of a drug response, the distribution characteristic has a peak, and the frequency gradually decreases as the luminance increases.

Next, in step SA5, the above distribution characteristics are normalized (standardization process). FIG. 5 is a flowchart showing the procedure of the normalization process performed in step SA5.
First, in step SB1, a reference distribution characteristic is set (reference setting step). The reference distribution characteristic is used to determine a distribution characteristic that serves as a reference for normalization. For example, one of the three distribution characteristics created in step SA4 is set as the reference distribution characteristic.

  In subsequent step SB2, one of the distribution characteristics other than the reference distribution characteristic is selected as a normalization target, and in subsequent step SB3, one or more of the reference distribution characteristic and the selected distribution characteristic (hereinafter referred to as “selection distribution characteristic”) are selected. Set a representative point. As representative points, for example, as shown in FIG. 6, the peak, the minimum luminance value, the upper 10% boundary luminance, and the like can be cited as examples.

Next, in step SB4, as shown in FIG. 6, the luminance data set related to the selection distribution characteristic is converted into the following conversion formula (1) so that each representative point of the selection distribution characteristic matches those of the reference distribution characteristic. Is used for linear conversion (distribution characteristic conversion step).
For example, when the luminance data is x, the luminance data x is linearly converted using the following equation (1).
X ′ = ax + b (1)
In the equation (1), X ′ is luminance data after linear conversion, a and b are variables, and x is measured luminance data.

By changing the variable b, the selection distribution characteristic can be translated along the X axis. This data operation corresponds to, for example, an operation for reducing noise caused by autofluorescence or the like of the sample plate 1. That is, since the plastic sample plate 1 emits fluorescence, the measured luminance of the cell is a value affected by the autofluorescence. For this reason, by adjusting the variable b, it is possible to correct data variation due to autofluorescence.
Further, by changing the variable a, it is possible to change the maximum frequency and the luminance data taking the maximum frequency. This data operation corresponds to an operation for reducing a measurement error of luminance data due to a change in intensity of illumination light.

  In step SB4, the selection distribution characteristics are linearly converted by arbitrarily changing the values of a and b within a predetermined range, and an error at each representative point between the converted selection distribution characteristics and the reference distribution characteristics is calculated. The values of a and b when the accumulated error is minimized are specified. Thus, by performing data normalization, it is possible to reduce noise components generated by various factors as described above.

Next, in step SB5, it is determined whether or not normalization has been performed for all distribution characteristics. As a result, if normalization has not been performed for all distribution characteristics, the process returns to step SB2, a distribution characteristic that has not been standardized is selected, and the processing after step SB3 is performed for the selected distribution characteristic. Similarly, the distribution characteristics are normalized.
On the other hand, in step SB5, when normalization has been performed for all distribution characteristics, the normalization process ends and the flow returns to the flow of FIG.

  In step SA6 of FIG. 4, each normalized distribution characteristic is compared, and luminance data that shows a different tendency compared to other distribution characteristics is deleted or corrected (correction step).

  In the correction step, an average distribution characteristic is obtained by averaging the normalized distribution characteristics, and a deviation from the average distribution characteristic is obtained for each distribution characteristic. If the deviation is equal to or greater than a predetermined threshold value, it is possible to further reduce noise by deleting the luminance data of the portion or correcting the luminance data to approximate the average characteristic.

  FIG. 7 shows average distribution characteristics and normalized distribution characteristics. As shown in FIG. 7, noise removal is performed by adopting, adjusting, or deleting luminance data of other characteristics for the region P in which the deviation is greater than a predetermined threshold with respect to the average distribution characteristics. We are going to plan.

In this way, by obtaining the average distribution characteristic from the normalized distribution characteristic and deleting or correcting the luminance data showing a unique tendency with respect to the average distribution characteristic, a highly reliable distribution characteristic can be obtained. Is possible.
In addition, when cells are cultured using different types of drugs, it is difficult to determine whether the drug has a specific tendency or a noise has a specific tendency. In that case, it is possible to leave the luminance data showing a unique tendency with respect to the average distribution characteristic as it is without deleting or correcting it, and to analyze this unique tendency in a later evaluation.

  When a highly reliable distribution characteristic is obtained in this way, the chemical reaction caused by the drug is quantified and evaluated by comparing the distribution characteristic for each condition finally obtained in step SA7 in FIG. Perform the evaluation process.

  As described above, according to the cell analysis method, apparatus, and program according to the present embodiment, the luminance distribution characteristic created for each culture condition is normalized, so that the error in the luminance data due to the difference in the culture condition can be reduced. Therefore, it is possible to obtain luminance data and distribution characteristics with high reliability. Therefore, by comparing these distribution characteristics between the culture conditions, it becomes possible to more accurately evaluate the drug reaction of the cells.

  For example, when evaluating the difference in response due to the drug concentration, it is preferable to compare luminance data indicating high values between the drug concentrations. At this time, in order to compare the number of cells reacting with the drug, there is an evaluation method in which a threshold value of brightness is set, and the number of cells that have a brightness higher than the threshold value is counted as a reaction. Even when such an evaluation method is adopted, the cell analysis method according to the present embodiment linearly converts the luminance data in the distribution characteristic so that the peak of each distribution characteristic matches the peak of the reference distribution characteristic. Therefore, it is possible to accurately compare the tendency of the distribution characteristics in the luminance range, which is important when evaluating the drug reaction. Thereby, an appropriate luminance threshold value can be set in the drug evaluation according to these comparison results, and the drug evaluation can be performed with high accuracy.

  In the cell analysis method according to the above-described embodiment, in the normalization process performed in step SA5 of FIG. 4, the linear conversion of the distribution characteristics is performed so that the representative points of the reference distribution characteristics coincide with the representative points of the selected distribution characteristics. However, this method is an example, and for example, the following method 1 or method 2 may be adopted.

[Method 1]
First, as shown in FIG. 8, when the minimum luminance of the reference distribution characteristic is min_i, the maximum luminance is max_i, the minimum luminance of the distribution characteristic is min_j, and the maximum luminance is max_j, the following equations (2) and (3) Are used to calculate the differences Li and Lj between the maximum luminance and the minimum luminance in the respective distribution characteristics.
Li = max_i−min_i (2)
Lj = max_j−min_j (3)
Subsequently, the above-described a is obtained by dividing the difference Li relating to the reference distribution characteristic by the difference Lj relating to the distribution characteristic using the following equation (4).
a = Li / Lj = (max_i-min_i) / (max_j-min_j) (4)
The above b is calculated using the following equation (5).
b = min_j−min_i (5)

  As described above, a and b may be acquired using the minimum luminance and the maximum luminance of each distribution characteristic, and the selection distribution characteristic may be normalized using the acquired values of a and b. The reason why a and b can be obtained by such a method is related to the fact that each distribution characteristic shows a similar characteristic. In other words, each distribution characteristic has a peak in a low-luminance region, and the frequency (number) gradually decreases as the luminance increases. Since the shapes of the distribution characteristics are substantially the same as described above, the values of a and b can be easily obtained by using the above-described method, and the selection distribution characteristics can be easily standardized. .

[Method 2]
In Method 2, as in Method 1 described above, when the minimum luminance of the reference distribution characteristic is min_i, the maximum luminance is max_i, the minimum luminance of the distribution characteristic is min_j, and the maximum luminance is max_j, the change numerical value ranges of a and b are set. The candidate values of a and b are respectively set by determining in advance as follows and dividing the setting range by a preset number.
| A | <(max_i-min_i) / (max_j-min_i)
| B | <min_j-min_i

  Then, the distribution characteristic is converted while changing the set combination of the candidate value a and the candidate value b as needed, and the combination of the values of a and b when the cumulative error d between the reference distribution characteristic and the distribution characteristic in each combination is the smallest Ask for. The accumulated error d is expressed by the following equation (6).

d = sam_k | Pi (tk) −Pj (tk) | (6)
However, 0 ≦ k ≦ n

Here, as shown in FIG. 9, Pi (tk) represents the frequency of the reference distribution characteristic at the luminance tk, and Pj (tk) represents the frequency of the selected distribution characteristic at the luminance tk. As tk is set in finer increments, the error between the selection distribution characteristic and the reference distribution characteristic can be determined more finely.
By performing the above process for each distribution characteristic, it is possible to obtain the values of a and b for each distribution characteristic when each distribution characteristic is most approximated to the reference distribution characteristic.

As mentioned above, although embodiment of this invention was explained in full detail with reference to drawings, the specific structure is not restricted to this embodiment, The design change etc. of the range which does not deviate from the summary of this invention are included.
For example, in the above-described embodiment, the distribution characteristic is created for each condition. However, the present invention is not limited to this. It is good also as performing. In this way, by performing normalization for each well, it is possible to further reduce noise due to well differences and noise in illumination during photographing.
In the above embodiment, one of the three distribution characteristics is set as the reference distribution characteristic. However, the present invention is not limited to this example. For example, a new reference distribution characteristic may be provided from the three distribution characteristics. Alternatively, any reference distribution characteristic that is set in advance may be used.

It is a whole lineblock diagram showing the cell analysis device concerning one embodiment of the present invention. It is a perspective view which shows an example of the sample plate used for the cell analyzer of FIG. It is a flowchart which shows the procedure of the cell analysis method which concerns on one Embodiment of this invention. An example of luminance distribution characteristics is shown. It is the flowchart shown about the procedure of the normalization process. It is a figure for demonstrating an example of a representative point and the normalization method of distribution characteristics. It is a figure which shows an average distribution characteristic and the distribution characteristic after normalization. It is explanatory drawing used for description about the method 1 of a normalization process. It is explanatory drawing used for description about the method 2 of a normalization process.

Explanation of symbols

1 Sample plate 102 Excitation light source 110 CCD camera 111 Universal control box 112 Computer

Claims (6)

  1. An imaging step of imaging each cell group cultured under different conditions;
    A luminance measurement step of measuring the luminance of each cell from the cell image acquired in the imaging step;
    A distribution characteristic creation step of creating a distribution characteristic of luminance data for each of the conditions;
    And a normalization step of normalizing the distribution characteristics created for each condition.
  2. The standardization process includes
    A reference setting step for setting a reference distribution characteristic as a reference based on the plurality of distribution characteristics;
    The cell analysis according to claim 1, further comprising: a distribution characteristic conversion step of normalizing the distribution characteristics by linearly converting each luminance data in the distribution characteristics so that each of the distribution characteristics coincides with the reference distribution characteristics. Method.
  3.   The cell analysis method according to claim 2, wherein in the distribution characteristic conversion step, luminance data in the distribution characteristic is linearly converted so that a peak of each distribution characteristic coincides with a peak of the reference distribution characteristic.
  4.   The cell analysis method according to any one of claims 1 to 3, further comprising a correction step of comparing the normalized distribution characteristics and deleting or correcting luminance data showing a different tendency compared to other distribution characteristics.
  5. A cell analysis device that performs cell analysis using a cell image acquired by imaging a plurality of cells cultured under different conditions,
    A luminance measuring means for measuring the luminance of each cell from the cell image;
    A distribution characteristic creating means for creating a distribution characteristic of luminance data for each condition;
    A cell analyzer comprising: normalization means for normalizing the distribution characteristics created for each condition.
  6. A cell analysis program for performing cell analysis using a cell image obtained by imaging a plurality of cells cultured under different conditions,
    A luminance measurement step of measuring the luminance of each cell from the cell image;
    A distribution characteristic creation step of creating a distribution characteristic of luminance data for each of the conditions;
    The cell analysis program which makes a computer perform the normalization step which normalizes the said distribution characteristic produced for every said conditions.
JP2007234077A 2007-09-10 2007-09-10 Cell analysis method, apparatus and program Active JP4922109B2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2007234077A JP4922109B2 (en) 2007-09-10 2007-09-10 Cell analysis method, apparatus and program

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP2007234077A JP4922109B2 (en) 2007-09-10 2007-09-10 Cell analysis method, apparatus and program

Publications (2)

Publication Number Publication Date
JP2009064398A true JP2009064398A (en) 2009-03-26
JP4922109B2 JP4922109B2 (en) 2012-04-25

Family

ID=40558913

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2007234077A Active JP4922109B2 (en) 2007-09-10 2007-09-10 Cell analysis method, apparatus and program

Country Status (1)

Country Link
JP (1) JP4922109B2 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8258594B2 (en) 2007-08-06 2012-09-04 Pnsensor Gmbh Avalanche photodiode

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001066128A (en) * 1999-08-30 2001-03-16 Nok Corp Surface inspecting device and its method
JP2006192058A (en) * 2005-01-13 2006-07-27 Pentax Corp Image processor
JP2006194869A (en) * 2004-12-16 2006-07-27 Pentax Corp Automatic testing equipment and debris inspection method
JP2006340686A (en) * 2005-06-10 2006-12-21 Olympus Corp Cell analysis method, cell analysis program and cell analysis apparatus
WO2006137216A1 (en) * 2005-06-22 2006-12-28 Mitsubishi Denki Kabushiki Kaisha Imaging device and gradation converting method for imaging method
JP2007054504A (en) * 2005-08-26 2007-03-08 Hitachi Medical Corp Ultrasonographic apparatus

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001066128A (en) * 1999-08-30 2001-03-16 Nok Corp Surface inspecting device and its method
JP2006194869A (en) * 2004-12-16 2006-07-27 Pentax Corp Automatic testing equipment and debris inspection method
JP2006192058A (en) * 2005-01-13 2006-07-27 Pentax Corp Image processor
JP2006340686A (en) * 2005-06-10 2006-12-21 Olympus Corp Cell analysis method, cell analysis program and cell analysis apparatus
WO2006137216A1 (en) * 2005-06-22 2006-12-28 Mitsubishi Denki Kabushiki Kaisha Imaging device and gradation converting method for imaging method
JP2007054504A (en) * 2005-08-26 2007-03-08 Hitachi Medical Corp Ultrasonographic apparatus

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8258594B2 (en) 2007-08-06 2012-09-04 Pnsensor Gmbh Avalanche photodiode

Also Published As

Publication number Publication date
JP4922109B2 (en) 2012-04-25

Similar Documents

Publication Publication Date Title
US5107422A (en) Method and apparatus for measuring multiple optical properties of biological specimens
US6496309B1 (en) Automated, CCD-based DNA micro-array imaging system
Hong et al. Development of the smartphone-based colorimetry for multi-analyte sensing arrays
US7116354B2 (en) Absolute intensity determination for a light source in low level light imaging systems
US5072382A (en) Methods and apparatus for measuring multiple optical properties of biological specimens
JP6363666B2 (en) Measurement of cell volume and components
US7136518B2 (en) Methods and apparatus for displaying diagnostic data
EP1428016B1 (en) Method of quantitative video-microscopy and associated system and computer software program product
US6639668B1 (en) Asynchronous fluorescence scan
JP2015509582A (en) Methods, systems, and apparatus for analyzing colorimetric assays
US7282723B2 (en) Methods and apparatus for processing spectral data for use in tissue characterization
JP4532261B2 (en) Optical image analysis for biological samples
US20110110567A1 (en) Methods and Apparatus for Visually Enhancing Images
US7755757B2 (en) Distinguishing between renal oncocytoma and chromophobe renal cell carcinoma using raman molecular imaging
US20040206882A1 (en) Methods and apparatus for evaluating image focus
CA2690633C (en) Method and system for standardizing microscope instruments
US20030146663A1 (en) Light calibration device for use in low level light imaging systems
EP1228354B1 (en) Apparatus and method for calibration of a microarray scanning system
US20140294265A1 (en) Color-based reaction testing of biological materials
Wachsmuth et al. High-throughput fluorescence correlation spectroscopy enables analysis of proteome dynamics in living cells
JP5739959B2 (en) Cell analysis apparatus and cell analysis method
DE10222779A1 (en) Method and arrangement for examining samples
US7899636B2 (en) Calibration of optical analysis making use of multivariate optical elements
EP2096429A1 (en) Fluorescent signal analyzing apparatus and fluorescent signal analyzing method
AU2002227343B2 (en) System for normalizing spectra

Legal Events

Date Code Title Description
A621 Written request for application examination

Free format text: JAPANESE INTERMEDIATE CODE: A621

Effective date: 20091126

A977 Report on retrieval

Free format text: JAPANESE INTERMEDIATE CODE: A971007

Effective date: 20110607

A131 Notification of reasons for refusal

Free format text: JAPANESE INTERMEDIATE CODE: A131

Effective date: 20110614

A521 Written amendment

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20110812

A131 Notification of reasons for refusal

Free format text: JAPANESE INTERMEDIATE CODE: A131

Effective date: 20110927

A521 Written amendment

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20111125

TRDD Decision of grant or rejection written
A01 Written decision to grant a patent or to grant a registration (utility model)

Free format text: JAPANESE INTERMEDIATE CODE: A01

Effective date: 20120124

A01 Written decision to grant a patent or to grant a registration (utility model)

Free format text: JAPANESE INTERMEDIATE CODE: A01

A61 First payment of annual fees (during grant procedure)

Free format text: JAPANESE INTERMEDIATE CODE: A61

Effective date: 20120203

FPAY Renewal fee payment (event date is renewal date of database)

Free format text: PAYMENT UNTIL: 20150210

Year of fee payment: 3

S531 Written request for registration of change of domicile

Free format text: JAPANESE INTERMEDIATE CODE: R313531

R350 Written notification of registration of transfer

Free format text: JAPANESE INTERMEDIATE CODE: R350

R250 Receipt of annual fees

Free format text: JAPANESE INTERMEDIATE CODE: R250