CN112991213A - Cell counting method - Google Patents
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Abstract
The present invention provides a cell counting method comprising the steps of: (A1) constructing a first Gaussian filter G1And a second Gaussian filter G2And processing the live cell image f to obtain a first image fdg＝f*(G1‑G2) (ii) a The first Gaussian filter is used for retaining low-frequency information in the live cell image, the second Gaussian filter is used for filtering noise in the live cell image, and the first Gaussian filter G is used for filtering noise in the live cell image1Standard deviation of (a)1Greater than the second gaussian filter G2Standard deviation of (a)2(ii) a (A2) For the first image fdgCarrying out self-adaptive threshold processing to obtain a cell region; (A3) subjecting said cellular region to a poreHole filling and area constraint are carried out, and a second image is obtained; (A4) viable cell counts were taken of the second image. The invention has the advantages of accurate counting, high efficiency and the like.
Description
Technical Field
The present invention relates to cell proliferation, and in particular to cell counting methods.
Background
Cell proliferation is the basis for the growth, development, reproduction and inheritance of organisms and is closely related to the generation and development of diseases. The behavioral characteristics of cells during proliferation are a hotspot for the research in the biomedical field, which must be carried out at the level of viable cells. Monolayers of cells cultured in vitro, often used to study the behavior of the cells, are usually counted at various time points to assess their proliferation and differentiation to stimuli.
Previously, cell preparations have often been counted by means of a cell counter or the like, which involves dissociation of the cells by trypsinizing the culture at the bottom of the flask, disrupting the normal physiological activity of the cells, and failing to perform multiple measurements on the same cell preparation to generate a complete cell growth curve. Manual inspection and counting is very time consuming and highly dependent on the expertise of the operator.
As the demand for cell analysis increases, time and labor consuming manual analysis is gradually being replaced by automated cell counting methods. Among the automatic counting methods, the image processing algorithm is one of the most powerful and versatile methods for cell analysis. Fluorescence microscopy is the primary imaging tool in cell biology and provides high contrast images with molecular specificity, but is generally used for qualitative analysis of cell preparations, phototoxicity and photobleaching limiting fluorescence imaging of living cells. In addition, the use of exogenous markers can also cause irreversible damage to the cells, which can affect the normal physiological activity of the cells.
The phase contrast microscope adopts a phase contrast technology to convert weak phase change of light passing through cells into amplitude change, living cells are observed in a natural state, and the normal physiological activity of the cells cannot be damaged, so that the automatic analysis of the acquired images has important significance in solving various biological problems.
In automated analysis of cellular images, cell segmentation is the most fundamental and important field, and is also a prerequisite for cell counting. However, due to the imaging mechanism, the problems of uneven brightness and low contrast between the cell and the background of the cell image acquired by the phase-contrast microscope exist, and the segmentation difficulty is greatly increased. In order to solve the technical problem, the following solutions in the prior art are proposed:
ronneberger et al propose a U-NET model on the basis of FCN, splice the deconvolution layer and the feature layer to make up for information loss, but because of the disappearance of the gradient, the constructed network depth is limited.
2. Zhang wenxiu et al introduced a residual block model to enhance the feature propagation capability based on a U-NET network, and utilized an attention mechanism to enhance the weight of the cell region and mitigate the brightness inconsistency. These methods require manual labor to make large training sets and time-consuming model training, and may not be suitable for large-scale rapid analysis of cell images.
Yin et al establish a linear imaging model based on the imaging principle of a phase contrast microscope, recover an image using a sparse constraint regularization function, reduce the interference of unbalanced brightness and low contrast, and then realize high-quality segmentation only by using threshold processing, but consume a large amount of computing resources in the image recovery process.
Jaccard et al rapidly achieves cell segmentation in combination with a method of local contrast threshold and halo artifact correction, but this method suffers from severe under-segmentation when dealing with high density cell images.
5. Other methods, such as those proposed by Flight, Vicar, etc., are subject to the luminance imbalance and low contrast.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a cell counting method.
The purpose of the invention is realized by the following technical scheme:
a cell counting method comprising the steps of:
(A1) constructing a first Gaussian filter G1And a second Gaussian filter G2And processing the live cell image f to obtain a first image fdg＝f*(G1-G2) Representing convolution operation, the first Gaussian filter is used for retaining low-frequency information in the live cell image f, the second Gaussian filter is used for filtering noise in the live cell image f, and the first Gaussian filter is used for filtering noise in the live cell image fGauss filter G1Standard deviation of (a)1Greater than the second gaussian filter G2Standard deviation of (a)2；
(A2) For the first image fdgCarrying out self-adaptive threshold processing to obtain a cell region;
(A3) carrying out hole filling and area constraint on the cell area to obtain a second image;
(A4) viable cell counts were taken of the second image.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the characteristics that the gray level of a cell area in a live cell image changes rapidly and the gray level of a background area changes slowly, double Gaussian filtering is constructed in a targeted manner to process the image, the contrast ratio of the cell and the background is enhanced, and the interference of brightness imbalance factors is reduced;
2. from the angle of a frequency domain, a double Gaussian filter is constructed to process the image, and compared with other filtering technologies, the method has the advantage of high calculation efficiency, such as 1398 × 1040 image resolution, and the processing time is less than 2 s;
3. live cell images are collected by using a phase contrast microscope, and the live cell images are analyzed, so that the same cell product can be counted repeatedly in a non-invasive manner, the cell behavior characteristics can be longitudinally researched along with time, and resources are saved;
4. double Gaussian filtering and subsequent optimization processing of hole filling and area constraint improve counting accuracy and can process cell images with different densities in a growth cycle.
Drawings
The disclosure of the present invention will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are only for illustrating the technical solutions of the present invention and are not intended to limit the scope of the present invention. In the figure:
FIG. 1 is a flow chart of a cell counting method according to an embodiment of the present invention;
FIG. 2 is an image of a cell before and after a double Gaussian filter process in accordance with an embodiment of the present invention;
FIG. 3 is a graph showing the results of image segmentation of low density cells according to various methods of embodiments of the present invention;
FIG. 4 is a graph showing the results of segmentation of a medium density cell image by different algorithms according to an embodiment of the present invention;
FIG. 5 is a graph showing the segmentation results of high density cell images by different algorithms according to an embodiment of the present invention;
fig. 6 is a graph of robustness analysis results according to an embodiment of the present invention.
Detailed Description
Fig. 1-6 and the following description depict alternative embodiments of the invention to teach those skilled in the art how to make and use the invention. Some conventional aspects have been simplified or omitted for the purpose of explaining the technical solution of the present invention. Those skilled in the art will appreciate that variations or substitutions from these embodiments will be within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. Thus, the present invention is not limited to the following alternative embodiments, but is only limited by the claims and their equivalents.
Example 1:
fig. 1 schematically shows a flowchart of a cell counting method according to example 1 of the present invention, which includes the following steps, as shown in fig. 1:
(A1) constructing a first Gaussian filter G1And a second Gaussian filter G2And processing the live cell image f (as shown in fig. 2 (a)) to obtain a first image fdg＝f*(G1-G2) As shown in fig. 2(b), the first gaussian filter is used to retain low-frequency information in the live cell image f, the second gaussian filter is used to filter noise in the live cell image f, and the first gaussian filter G is used to filter noise in the live cell image f1Standard deviation of (a)1Greater than the second gaussian filter G2Standard deviation of (a)2；
(A2) For the first image fdgCarrying out self-adaptive threshold processing to obtain a cell region;
(A3) carrying out hole filling and area constraint on the cell area to obtain a second image;
(A4) viable cell counts were taken of the second image.
To obtain a high contrast image, further, in step (a1), the first gaussian filter G is adjusted1Standard deviation of (a)1So that the first image fdgThe standard deviation is the largest.
In order to obtain a fast and stable segmentation effect, further, in step (a2), the adaptive thresholding is performed by:
processing the first image f using an Otsu threshold segmentation algorithmdgThe first image fdgDivided into a cellular region and a background region.
To eliminate the holes and obtain the complete cell region, further, in step (a3), the hole filling and area constraint are performed by:
filling holes in the cell region by using binary morphology closed operation;
calculating the area of each connected domain in the cell region, setting an area threshold value, and removing the connected domains with the areas smaller than the threshold value.
To improve the counting accuracy, further, in step (a4), the viable cells are counted in the following manner:
and calculating the number of connected domains with the area larger than the threshold value in the cell region of the second image, wherein the number is the number of living cells.
Example 2:
an application example of the cell counting method according to example 1 of the present invention.
In this application example, the living cell technology method comprises the following steps:
(A1) obtaining the live cell image f using a phase contrast microscope, as shown in fig. 2 (a);
constructing a first Gaussian filter G1And a second Gaussian filter G2The Gaussian filter is constructed in the prior art, and the living cell image f is processed to obtain a first image fdg＝f*(G1-G2) As shown in fig. 2(b), thereby filtering low frequency informationHigh-frequency information is reserved and represents convolution operation, the first Gaussian filter is used for reserving low-frequency information in the live cell image f, the second Gaussian filter is used for filtering noise in the live cell image f, and the first Gaussian filter G is used for filtering noise in the live cell image f1Standard deviation of (a)1Greater than the second gaussian filter G2Standard deviation of (a)2；
Standard deviation sigma1The obtaining method is as follows: adjusting the first Gaussian filter G1Standard deviation of (a)1So that the first image fdgMaximum standard deviation, i.e. first image fdgThe contrast of (2) is highest; first Gaussian Filter G in the present embodiment1Standard deviation of (a)120, second gaussian filter G2Standard deviation of (a)2＝1；
(A2) For the first image fdgCarrying out self-adaptive threshold value treatment to obtain a cell region, wherein the specific mode is as follows:
processing the first image f using an Otsu threshold segmentation algorithmdgAnd use of
Finding a threshold value which enables the inter-class variance to be maximum, wherein k is an image gray value, L is an image gray value range, and sigma is2For inter-class variance, the k value corresponding to the maximum inter-class variance value is the optimal threshold k·；
Binary image formula after threshold processing
Giving, dividing the area smaller than the threshold into non-cell areas, such as background areas, and dividing the area larger than or equal to the threshold into cell areas;
(A3) and carrying out hole filling and area constraint on the cell area to obtain a second image, wherein the specific mode is as follows:
filling holes in the cell region by using binary morphology closed operation, calculating the area of each connected domain in the cell region, setting an area threshold value, and removing the connected domains with the areas smaller than the threshold value if the area threshold value is 50;
(A4) and calculating the number of connected domains with the area larger than the threshold value in the cell region of the second image, wherein the number is the number of living cells.
Comparative example:
the present invention uses a published data set of mouse myoblast cell population C2C12 from university of california, which has 48 sequences, each sequence taking 1013 images (1392 × 1040 resolution), selecting 1 sequence from them for the experiment, and using the CellCounter plug in ImageJ of the image tool to click on the cells in each image to achieve manual counting of cells as a standard set.
The experimental hardware and software environment is: inter (R) core (TM) i 36100 CPU @3.70GHz, 8.00GB memory, 64-bit Win7 operating system, and MATLAB R2014a is adopted to realize the technical scheme of the invention.
In order to evaluate the segmentation effect of the method, three indexes of Precision (Precision), Recall (Recall) and F value (F-score) are introduced for quantitative comparison and analysis, and the three indexes are calculated as follows:
wherein: TP represents the number of cells correctly identified by the method, FP represents the number of cells incorrectly identified by the method, and FN represents the number of cells correctly identified without the method. The higher the accuracy, the lower the probability that the cell is falsely detected; the higher the recall rate, the lower the probability of indicating that the cell is missed; the F value is a harmonic mean value of the accuracy rate and the recall rate, the overall performance of the algorithm is measured, and the higher the F value is, the better the detection effect of the algorithm is. In addition, the algorithmic average run Time is also used for evaluation.
In order to verify the performance of various methods, the existing accurate and fast algorithms (Jaccard algorithm, 2D-Otsu algorithm, Flight algorithm and Vicar algorithm) are compared with the method of the invention for analysis, as shown in FIGS. 3-5, FIGS. 3(a), 4(a) and 5(a) are original live cell images:
the segmentation result of the 2D-Otsu algorithm, which classifies the cells and the background into one class and the halo into the other class, fails to effectively segment the cells, as shown in FIGS. 3(b), 4(b) and 5 (b).
The Jaccard algorithm can overcome the influence of low contrast and uneven brightness of cells and background, and accurately segment isolated cells, but in the process of processing low-density cell images, the Jaccard algorithm has under-segmentation on aggregated and adhered cells, as shown in FIGS. 3(c) and 4 (c); when a high-density cell image is processed, the under-segmentation phenomenon is serious, as shown in fig. 5 (c).
The Flight algorithm is more accurate in processing the cell image segmentation result under different densities, but the edge is fractured, and the cell in the area with lower contrast is lost due to slight over-segmentation, as shown in fig. 3(d), 4(d) and 5 (d).
The Vicar algorithm is less affected by low contrast and uneven brightness of the cells from the background, and can effectively segment the cells, as shown in fig. 3(e) and 4(e), but the segmentation edges are rough, and there is under-segmentation when processing high density cell images, as shown in fig. 5 (e).
The method of the invention can better process the cell images with unbalanced brightness and low contrast, has less under-segmentation and over-segmentation phenomena when processing the cell images with different densities, and can accurately segment the cells, as shown in fig. 3(f), 4(f) and 5 (f).
The quality of the segmentation result is judged from the visual effect, the result is easily influenced by subjective factors of people, quantitative evaluation is still needed, and the segmentation results of the methods are compared as shown in the following table. The three indexes of the accuracy rate, the recall rate and the F value of the method respectively reach 0.9770, 0.9475 and 0.9609 which are superior to those of a comparison method, and under the condition that the running time of the method is not large, the method can realize the rapid and accurate segmentation of the cell image.
Comparison of segmentation results of different methods
To further compare the effects of the methods, the robustness of the algorithm was further analyzed, as shown in fig. 6. FIG. 6(a1) shows isolated and aggregated cells, the image is overall lighter; the cells in FIG. 6(a2) had adhesions; the cells in FIG. 6[ a3-a4] are dense, irregular in shape and low in contrast, and the image is dark overall.
The 2D-Otsu algorithm is severely affected by low contrast and inconsistent brightness, and fails to effectively segment cells, as shown in FIG. 6[ b1-b4 ];
the Jaccard algorithm accurately detects isolated cells, but there is under-segmentation of cells that are treated for adhesion, aggregation and irregular shape, as shown in FIG. 6[ c1-c4 ];
when the Flight algorithm is used for processing the image with dense cells, the cells in the region with low contrast are omitted, and the segmented cells are incomplete, as shown in FIG. 6[ d3-d4 ];
the Vicar algorithm suffers from under-segmentation when dealing with clustered, adherent cells, and loses cells in areas of lower contrast when dealing with images with dense cells, as shown in fig. 6[ e1-e4 ];
according to the method, the contrast ratio of the cells and the background is enhanced by constructing double Gaussian filters, the interference of uneven brightness is weakened, optimization processing is carried out subsequently, and as seen from the results of fig. 6 f1-f4, the method can achieve consistent segmentation results under complex conditions.
Aiming at the problems of unbalanced brightness and low contrast between cells and background of a phase-contrast microscopic living cell image, the invention researches a method for rapidly counting living cells from the angle of a frequency domain. The contrast of the cells and the background is enhanced by constructing double Gaussian filtering processing, the influence of uneven brightness is weakened, the cells are segmented by means of an adaptive threshold, the cell morphology is improved by using closed operation, and then the influence of impurities on the segmentation accuracy is reduced by adopting area constraint. The experimental result shows that compared with other algorithms, the method has better robustness and accuracy, can reduce or even eliminate the influence of factors of unbalanced brightness and low contrast in phase-difference microscopic cell images, better processes the images in the cell growth cycle, can also realize effective segmentation of adhered and irregularly-shaped cells, and lays a good foundation for the subsequent research of in-vitro cell proliferation behaviors.
Claims (7)
1. A cell counting method comprising the steps of:
(A1) constructing a first Gaussian filter G1And a second Gaussian filter G2And processing the live cell image f to obtain a first image fdg＝f*(G1-G2) Representing convolution operation, the first Gaussian filter is used for retaining low-frequency information in the live cell image f, the second Gaussian filter is used for filtering noise in the live cell image f, and the first Gaussian filter G is used for filtering noise in the live cell image f1Standard deviation of (a)1Greater than the second gaussian filter G2Standard deviation of (a)2；
(A2) For the first image fdgCarrying out self-adaptive threshold processing to obtain a cell region;
(A3) carrying out hole filling and area constraint on the cell area to obtain a second image;
(A4) viable cell counts were taken of the second image.
2. The cell counting method according to claim 1, wherein in the step (A1), the first Gaussian filter G is adjusted1Standard deviation of (a)1So that the first image fdgThe standard deviation is the largest.
3. The cell counting method of claim 2, wherein the first Gaussian filter G1Standard deviation of (a)120, second gaussian filter G2Standard deviation of (a)2＝1。
4. The cell counting method according to claim 1, wherein in step (a2), the adaptive thresholding is performed by:
processing the first image f using an Otsu threshold segmentation algorithmdgThe first image fdgDivided into a cellular region and a background region.
5. The cell counting method according to claim 1, wherein in step (A3), the hole filling and the area restriction are performed in a manner that:
filling holes in the cell region by using binary morphology closed operation;
calculating the area of each connected domain in the cell region, setting an area threshold value, and removing the connected domains with the areas smaller than the threshold value.
6. The cell counting method according to claim 5, wherein in the step (A4), the viable cells are counted in such a manner that:
and calculating the number of connected domains with the area larger than the threshold value in the cell region of the second image, wherein the number is the number of living cells.
7. The cell counting method according to claim 1, wherein the live cell image f is obtained using a phase contrast microscope.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116189178A (en) * | 2022-12-30 | 2023-05-30 | 广州市明美光电技术有限公司 | Identification method, equipment and storage medium for microscopic cell image |
| CN117221458A (en) * | 2023-10-23 | 2023-12-12 | 上海为旌科技有限公司 | Method and system for removing image chroma noise by frequency division |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116189178A (en) * | 2022-12-30 | 2023-05-30 | 广州市明美光电技术有限公司 | Identification method, equipment and storage medium for microscopic cell image |
| CN117221458A (en) * | 2023-10-23 | 2023-12-12 | 上海为旌科技有限公司 | Method and system for removing image chroma noise by frequency division |
| CN117221458B (en) * | 2023-10-23 | 2024-05-03 | 上海为旌科技有限公司 | Method and system for removing image chroma noise by frequency division |
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