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    • 专利摘要:
    The invention is a method for counting particles, especially blood cells, in a sample by an optical imaging device without a lens. The sample is disposed between a light source and an image sensor. The sample is illuminated by a light source and an image is acquired by the image sensor, the latter being exposed to a light wave called exposure wave. A digital propagation operator is applied to the acquired image to obtain a complex amplitude of the exposure wave along a surface facing the image sensor. From the module and / or the phase of this complex amplitude, a so-called reconstructed image is formed on which the particles to be counted appear in the form of regions of interest. The method then comprises a step of selecting the regions of interest corresponding to the particles to be counted.
    公开号:FR3060746A1
    申请号:FR1663028
    申请日:2016-12-21
    公开日:2018-06-22
    发明作者:Pierre BLANDIN；Anais ALI CHERIF；Estelle Gremion
    申请人:Commissariat a lEnergie Atomique CEA；Horiba ABX SAS；Commissariat a lEnergie Atomique et aux Energies Alternatives CEA；
    IPC主号:
    专利说明:

    Holder (s): ATOMIC AND ALTERNATIVE ENERGY COMMISSIONER Public establishment, HORIBA ABX SAS Simplified joint-stock company.
    Extension request (s)
    Agent (s): INNOVATION COMPETENCE GROUP.
    (54 / METHOD OF NUMERING PARTICLES IN A SAMPLE WITH LENS FREE IMAGING.
    FR 3 060 746 - A1 (5d / The invention is a method for counting particles, in particular blood cells, in a sample, by an optical imaging device without lens. The sample is placed between a light source and an image sensor. The sample is illuminated by a light source and an image is acquired by the image sensor, the latter being exposed to a light wave called the exposure wave. A digital propagation operator is applied to the image acquired so as to obtain a complex amplitude of the exposure wave along a surface facing the image sensor From the module and / or the phase of this complex amplitude, a so-called reconstructed image is formed , on which the particles which one wishes to count appear in the form of regions of interest.The method then comprises a step of selection of the regions of interest corresponding to the particles to be counted.

    i
    Method for counting particles in a sample by lensless imaging
    Description
    TECHNICAL AREA
    The invention is an optical method for counting particles, in particular blood cells, arranged in a sample using a lensless imaging device.
    PRIOR ART
    In the field of hematology, the counting of blood cells, for example red blood cells or white blood cells, is a common operation. The analysis laboratories are equipped with automatic devices allowing the production of reliable hemograms. The principles implemented by these automata are cytometry measurements based on a variation of impedance or a diffusion of a light beam. However, the automata are generally expensive and their use remains limited to the laboratory.
    Beyond hematology, the identification of particles of interest in a sample and their counting are commonly practiced operations.
    The document WO2008090330 describes a device allowing the observation of samples comprising cells by lensless imaging. The sample is placed between a light source and an image sensor, without having an optical magnification lens between the sample and the image sensor. Thus, the image sensor collects an image of a light wave transmitted by the sample. This image, also called hologram, is made up of interference figures between the light wave emitted by the light source and transmitted by the sample, and diffraction waves, resulting from the diffraction by the sample of the wave. light emitted by the light source. These interference figures are generally formed by a succession of concentric rings. They are sometimes called diffraction figures, or designated by the English term "diffraction pattern". We thus acquire images, whose field of observation is much larger than that of a microscope. When the concentration of cells in the sample is sufficiently low, each cell can be associated with an interference pattern; their enumeration allows the counting of the cells present in the sample. But the hologram does not allow reliable counting of cells when the concentration increases.
    The hologram acquired by the image sensor can be processed by a holographic reconstruction algorithm, so as to estimate optical properties of the sample, for example an absorption or a phase shift value of the light wave passing through the sample. and propagating to the image sensor. This type of algorithm is well known in the field of holographic reconstruction. An example of a holographic reconstruction algorithm is described in the publication Ryle et al, "Digital in-line holography of biological specimens", Proc. Of SPIE Vol. 6311 (2006). However, such algorithms can give rise to the appearance of reconstruction noise, designated by the term twin image, on the reconstructed image. Their application therefore requires certain precautions. Application US2012 / 0218379 describes a method for reconstructing a complex image of a sample, the latter comprising amplitude and phase information. Application US2012 / 0148141 applies the method described in application US2012 / 0218379 to reconstruct a complex image of spermatozoa and to characterize their mobility.
    The inventors wished to propose a simple and inexpensive alternative for counting particles of interest in a sample, by taking advantage of the large field of observation conferred by imaging without lenses. Applied to blood samples, the invention can constitute an alternative to the hematology machines currently available for carrying out cell counts in blood. The invention is particularly suitable for samples with high concentrations of particles, which can reach several hundreds of thousands of particles per microliter.
    STATEMENT OF THE INVENTION
    An object of the invention is a method for counting particles, in particular blood cells placed in a sample, the method comprising the following steps:
    a) illumination of the sample using a light source, the light source emitting an incident light wave propagating towards the sample;
    b) acquisition, using an image sensor, of an image of the sample, formed in a detection plane, the sample being placed between the light source and the image sensor, each image being representative of a light wave, called exposure light wave, to which the image sensor is exposed under the effect of said illumination;
    the method being characterized in that it also comprises the following steps:
    c) application of a propagation operator from the image acquired during step b), so as to calculate a complex expression of the exposure light wave according to a reconstruction surface extending opposite the plane detection;
    d) formation of an image, called reconstructed image, representative of a distribution of the module and / or of the phase of the complex expression according to the reconstruction surface;
    e) segmentation of the image formed during step d) to obtain a segmentation image, comprising regions of interest spaced apart from one another, all or part of the regions of interest being associated with at least one particle d 'interest;
    f) determining a size of regions of interest and classification of said regions of interest, according to their size, between at least one of the following classes:
    a class according to which the region of interest corresponds to a single particle of interest;
    a class according to which the region of interest corresponds to a predetermined number, greater than 1, of particles of interest;
    g) counting of the particles of interest which were the subject of a classification during the step
    f).
    The classification allows a more precise quantification of the particles of interest, compared to a simple counting of the selected regions of interest.
    Step f) may include, prior to classification:
    a determination of at least one morphological criterion for each region of interest; a selection of regions of interest (ROI) according to the morphological criterion, the classification being carried out on the regions of interest selected.
    A morphological criterion can in particular be chosen from a diameter, a size or a form factor of each region of interest. Several morphological criteria can be combined.
    Step f) may include taking into account a reference size, and / or a reference shape, representative of a predetermined number of particles of interest; the classification is then carried out according to said reference size.
    Step f) can include the following sub-steps:
    fi) a first classification of the regions of interest, carried out as a function of a first reference size, the first reference size corresponding to a predetermined number of particles of interest;
    fii) following the first classification, a determination of a second reference size, from the regions of interest classified, during the sub-step fi), as comprising a predefined number of particles of interest;
    fiii) a second classification of the regions of interest, carried out according to the second reference size (S re ^ _ 2 ) determined during the sub-step fii).
    Step f) may include, prior to classification:
    a calculation of a signal to noise ratio of several regions of interest;
    a selection of the regions of interest as a function of the signal to noise ratio calculated for each of them, for example on the basis of a comparison with respect to a threshold.
    the classification then being carried out on the regions of interest selected.
    The sample can be mixed with a spherization reagent before step b), to modify the shape of the particles of interest so as to make it spherical.
    Step c) can include the following sub-steps:
    ci) definition of an initial image of the sample in the detection plane, from the image acquired by the image sensor;
    cii) determination of a complex image of the sample in a reconstruction surface by applying a propagation operator to the initial image of the sample defined during sub-step ci) or to the image of the sample , in the detection plan, resulting from the previous iteration;
    ciii) calculation of a noise indicator from the complex image determined during the sub-step cii), this noise indicator depending on a reconstruction noise affecting said complex image;
    civ) updating the image of the sample in the detection plane by adjusting the phase values of the pixels of said image, the adjustment being carried out as a function of a variation of the indicator calculated during the sub-step ciii) according to said phase values;
    cv) reiteration of the sub-steps cii) to civ) until a convergence criterion is reached, so as to obtain a complex image of the sample in the detection plane and in the reconstruction surface.
    Preferably, no imaging optics or magnification optics are disposed between the sample and the image sensor.
    The particles of interest can be blood cells. According to one embodiment, during step d):
    - the particles of interest are red blood cells or white blood cells, the reconstructed image being representative of a distribution of the complex expression module according to the reconstruction surface;
    the particles of interest are platelets, the reconstructed image being representative of a distribution of a phase of the complex expression according to the reconstruction surface.
    Another object of the invention is a device for the counting of particles of interest arranged in a sample, the device comprising:
    - a light source capable of emitting an incident light wave propagating towards the sample;
    - a support, configured to hold the sample between the light source and an image sensor;
    a processor, configured to receive an image of the sample acquired by the image sensor and to implement steps c) to g) of the method as described in this application. Other advantages and characteristics will emerge more clearly from the description which follows of particular embodiments of the invention, given by way of nonlimiting examples, and represented in the figures listed below.
    FIGURES
    FIG. 1A represents an example of a device allowing the implementation of the invention. FIG. 1B represents another example of a device allowing the implementation of the invention. FIG. 2A is a flow diagram showing the main steps of a method according to the invention. FIG. 2B shows a reconstructed so-called amplitude image, the sample comprising red blood cells. FIG. 2C shows the image of FIG. 2B after segmentation, this figure showing several regions of interest distributed in the image. Figure 2D shows the image of Figure 2C after classification of each region of interest. FIG. 2E represents a histogram of the signal to noise ratio determined for each region of interest.
    FIGS. 3A and 3B represent regions of interest respectively before and after a thresholding as a function of a signal to noise ratio value. FIGS. 3C and 3D are histograms of the signal to noise ratios of the images 3A and 3B respectively.
    Figure 3E shows the main steps of a particular embodiment.
    FIG. 4A illustrates the main steps of a method making it possible to obtain a complex image of the sample, called the reconstructed image, in a reconstruction plane. FIG. 4B schematizes the steps described in connection with FIG. 4A.
    FIGS. 5A, 5B and 5C are curves representing counts of red blood cells implementing the invention, using a light emitting diode emitting respectively in blue, green and red spectral bands, as a function of counts carried out using 'a reference method.
    FIG. 5D is a curve representing counts of red blood cells implementing the invention using a laser light source, as a function of counts made using a reference method.
    FIG. 6A shows a reconstructed so-called amplitude image, the sample comprising white blood cells. Figure 6B shows the image of Figure 6A after segmentation. FIG. 6C shows a reconstructed so-called amplitude image, the sample comprising white blood cells. Figure 6D shows the image of Figure 6C after segmentation.
    FIG. 7A shows a reconstructed so-called amplitude image, the sample comprising glass beads. Figure 7B shows the image of Figure 7A after segmentation.
    Figures 8A and 8B are histograms showing a distribution of signal to noise ratios of regions of interest corresponding to red blood cells.
    EXPLANATION OF PARTICULAR EMBODIMENTS
    FIG. 1A represents an example of a device according to the invention. A light source 11 is capable of emitting a light wave 12, called the incident light wave, propagating towards a sample 10, along a propagation axis Z. The light wave is emitted according to a spectral band Δλ.
    The sample 10 is a sample comprising particles, among which particles of interest 10a which one wishes to count. By particles is meant, for example, cells, and in particular blood cells, but it can also be microorganisms, viruses, spores, or microbeads, usually used in biological applications, or even microalgae. It may also be droplets insoluble in the 10m liquid medium, for example oil droplets dispersed in an aqueous phase. Preferably, the particles 10a have a diameter, or are inscribed in a diameter, less than 100 μm, and preferably less than 50 μm or 20 μm. Preferably, the particles have a diameter, or are inscribed in a diameter, greater than 500 nm or 1 μm.
    In the example shown in FIG. 1A, the sample comprises a medium 10m in which the particles of interest 10a bathe. The particles 10a are, in this example, blood cells, for example red blood cells (erythrocytes) or white blood cells (leukocytes). The medium 10m, in which the particles are immersed, can in particular be a liquid, for example blood plasma, optionally diluted.
    The sample 10 is, in this example, contained in a fluid chamber 15. The fluid chamber 15 is for example a fluid chamber of the Countess® type with a thickness e = 100 μm. The thickness e of the sample 10, along the propagation axis Z typically varies between 10 pm and 1 cm, and is preferably between 20 pm and 500 pm. The sample 10 extends along a plane Pw> said plane of the sample, preferably perpendicular to the axis of propagation Z. It is maintained on a support 10s at a distance d from an image sensor 16.
    The distance D between the light source 11 and the sample 10 is preferably greater than 1 cm. It is preferably between 2 and 30 cm. Advantageously, the light source, seen by the sample, is considered as a point. This means that its diameter (or its diagonal) is preferably less than a tenth, better a hundredth of the distance between the sample and the light source. The light source 11 can be a light-emitting diode as shown in FIG. IA. It can be associated with a diaphragm 18, or spatial filter. The diaphragm opening is typically between 5 µm and 1 mm, preferably between 50 µm and 500 µm. In this example, the diaphragm is supplied by Thorlabs under the reference P150S and its diameter is 150 μm. The diaphragm can be replaced by an optical fiber, a first end of which is placed opposite the light source 11 and a second end of which is placed opposite the sample 10.
    The device may include a diffuser 17, disposed between the light source 11 and the diaphragm 18. The use of such a diffuser makes it possible to overcome constraints of centering of the light source 11 relative to the opening of the diaphragm 18. The function of such a diffuser is to distribute the light beam produced by the light source according to a cone of angle a. Preferably, the angle of diffusion a varies between 10 ° and 80 °. The presence of such a diffuser makes it possible to make the device more tolerant with respect to a decentralization of the light source relative to the diaphragm, and to homogenize the lighting of the sample. The diaphragm is not necessary, in particular when the light source is sufficiently punctual, in particular when it is a laser source.
    The light source 11 can be a laser source, such as a laser diode. In this case, it is not useful to associate a spatial filter 18 or a diffuser 17 with it. Such a configuration is shown in FIG. 1B.
    Preferably, the emission spectral band Δλ of the incident light wave 12 has a width less than 100 nm. By spectral bandwidth is meant a width at half height of said spectral band.
    The sample 10 is placed between the light source 11 and the image sensor 16 previously mentioned. The latter preferably extends parallel, or substantially parallel to the P-LO plane along which the sample extends. The term substantially parallel means that the two elements may not be strictly parallel, an angular tolerance of a few degrees, less than 20 ° or 10 ° being allowed. The image sensor 16 is able to form an image I o according to a detection plane P o . In the example shown, it is an image sensor comprising a pixel matrix, of CCD type or a CMOS. The detection plane P o preferably extends perpendicularly to the axis of propagation Z of the incident light wave 12. The distance d between the sample 10 and the pixel matrix of the image sensor 16 is advantageously between 50 pm and 2 cm, preferably between 100 pm and 2 mm.
    Note the absence of imaging optics, such as magnification optics, between the image sensor 16 and the sample 10. This does not prevent the possible presence of focusing microlenses at each pixel of the image sensor 16, the latter having no function of enlarging the image acquired by the image sensor.
    Under the effect of the incident light wave 12, the particles present in the sample can generate a diffracted wave 13, capable of producing, at the level of the detection plane P o , interference with a part 12 'of the wave incident light 12 transmitted by the sample. Furthermore, the sample can absorb part of the incident light wave.
    12. Thus, the light wave 14, called the exposure light wave, transmitted by the sample 10 and to which the image sensor 16 is exposed, can comprise:
    a component 13 resulting from the diffraction of the incident light wave 12 by each particle of the sample;
    a component 12 ′ resulting from the transmission of the incident light wave 12 by the sample.
    These components form interference in the detection plane. Also, the image I o acquired by the image sensor 16 includes interference figures (or diffraction figures), each interference figure being generated by a particle 10a of the sample 10.
    A processor 20, for example a microprocessor, is configured to process each image I o acquired by the image sensor 16. In particular, the processor is a microprocessor connected to a programmable memory 22 in which is stored a sequence of instructions for perform the image processing and calculation operations described in this description. The processor can be coupled to a screen 24 allowing the display of images acquired by the image sensor 16 or calculated by the processor 20.
    As indicated in relation to the prior art, an image I o acquired on the image sensor 16, also called a hologram, does not allow a sufficiently precise representation of the observed sample to be obtained. A propagation operator h can be applied to each image acquired by the image sensor, so as to calculate a quantity representative of the exposure light wave 14. Such a process, designated by the term holographic reconstruction, allows in particular to reconstruct an image of the module or of the phase of this light wave 14 in a reconstruction plane parallel to the detection plane P o , and in particular in the plane Pio along which the sample extends. For this, a convolution product of the image I o acquired by the image sensor 16 is carried out by a propagation operator h. It is then possible to calculate a complex expression A of the light wave 14 at any point of coordinates (x, y, z) of space, and in particular according to a reconstruction surface extending opposite the image sensor. . The reconstruction surface can in particular be a reconstruction plane P z located at a distance | z | of the image sensor 16, and in particular the plane Pio of the sample, with:
    A (x, y, z) = I 0 (x, y, z) * h, * designating the product operator of convolution.
    In the remainder of this description, the coordinates (x, y) designate a radial position in a plane perpendicular to the axis of propagation Z. The coordinate z designates a coordinate along the axis of propagation Z.
    The complex expression A is a complex quantity whose argument and modulus are respectively representative of the phase and the intensity of the light wave 14 to which the image sensor is exposed 16. The convolution product of the image I o by the propagation operator h makes it possible to obtain a complex image A z representing a spatial distribution of the complex expression A in the reconstruction plane P z , extending to a coordinate z of the detection plane P o . In this example, the detection plane P o has the equation z = 0. The complex image A z corresponds to a complex image of the sample in the reconstruction plane P z . It also represents a two-dimensional spatial distribution of the optical properties of the exposure light wave 14. The function of the propagation operator ha is to describe the propagation of light between the image sensor 16 and a point of coordinates (x, y, z), located at a distance | z | of the image sensor. It is then possible to determine the module M (x, y, z) and / or the phase φ (x, y, z) of the exposure light wave 14, at this distance | z |, called reconstruction distance, with:
    M (x, y, z) = abs [A (x, y, z)] (1);
    - p (x, y, z) = arg [A (x, y, z)] (2).
    The operators abs and arg respectively designate the module and the argument.
    In other words, the complex expression A of the exposure light wave 14 at any point of coordinates (%, y, z) of space is such that: A (x, y, z) = Μ (χ, γ, ζ) 6ί φ (· χ · ν · ζ ) (3). It is possible to form images M z and φ ζ respectively representing a distribution of the module or of the phase of complex expression A in a surface extending opposite the detection plane P o . Such a surface can in particular be a plane P z located at a distance | z | of the detection plane P o , with M z = mod (A z ) and φ ζ = arg (X z ). The previously mentioned surface is not necessarily plane, but it extends substantially parallel to the detection plane and is preferably a plane P z parallel to the detection plane. In the following description, the image obtained from the module and / or the phase of the complex image A z is designated by the term reconstructed image and is denoted l z .
    The inventors have developed a method for counting the particles 10a present in the sample according to a method described in connection with FIGS. 2A to 2E. The main steps of this process are described below.
    Step 100: illumination of the sample. During this step, the sample is illuminated by the light source 11.
    Step 110: acquisition of an image 70 of the sample 10 by the image sensor 16, this image forming a hologram. One of the advantages of the lensless configuration, represented in FIGS. 1A or 1B, is the wide field observed, making it possible to simultaneously address a high volume of sample. The field observed depends on the size of the sensor, being slightly smaller than the detection surface of the latter, due to the spacing between the sensor and the sample. The field observed is generally greater than 10 mm 2 , or even 20 mm 2 .
    Step 120: calculation of a complex image A z in a reconstruction plane P z . The complex image includes phase and amplitude information of the exposure light wave 14 to which the image sensor 16 is exposed. The reconstruction plane is the P-lo plane along which the image extends. sample. Step 120 can be performed by applying the propagation operator h, previously described, to an image from the acquired image I o . However, the application of the propagation operator to the acquired image can lead to a complex image.
    A z affected by significant reconstruction noise, frequently designated by the term twin image. In order to obtain a complex exploitable image, by limiting the reconstruction noise, iterative algorithms can be implemented. One of these algorithms is described below, in connection with FIGS. 4A and 4B.
    From the complex image A z , a reconstructed image 7 Z can be obtained from the module M z and / or from the phase φ ζ of the exposure light wave 14, in the reconstruction plane P z . The reconstruction distance relative to the image sensor 16 is determined either a priori, knowing the position of the sample 10 relative to the image sensor 16, or on the basis of a digital focusing algorithm, according to which the optimal reconstruction distance is that for which the reconstructed image 7 Z is the sharpest. Numerical focusing algorithms are known to those skilled in the art.
    FIG. 2B is an example of a part of an image reconstructed 7 Z from the module M z of a complex image A z obtained according to a method implementing the steps described in connection with FIGS. 4A and 4B. In this figure, the contrast has been reversed. The particles of interest 10a that one wishes to count are red blood cells. They appear in the form of ROI regions of interest distributed in the reconstructed image and isolated from each other. The sample and the experimental conditions are described more precisely below, in connection with the experimental tests.
    Step 130: segmentation. The image formed during step 120 is subject to segmentation, so as to isolate the regions of interest ROI corresponding to particles. By image segmentation is meant a partition of the image so as to group the pixels, for example as a function of their intensity. The image segmentation results in a segmentation image 7 Z in which the ROI regions of interest, spaced apart from each other, are delimited, each region of interest possibly corresponding to a particle, or to a cluster of particles as described thereafter. Different segmentation methods are known to those skilled in the art. We can for example apply an Otsu threshold, consisting in determining a value of an intensity threshold from the histogram of the image, the threshold allowing an optimal separation of the pixels according to two classes: a class of pixels representing the regions of interest and a class of pixels representing the background of the image. FIG. 2C represents the result of a segmentation of the image of FIG. 2B by applying a thresholding according to the Otsu algorithm. The image obtained is binarized, the pixels representing the background of the image being dark (minimum gray level) while the pixels representing each ROI area of interest are clear (maximum gray level).
    In this example, the segmentation image I z resulting from this step results from a segmentation of the image M z of the module of the complex amplitude A z of the exposure light wave 14. As a variant, the image from this step results from a segmentation of the image φ ζ of the phase of the exposure light wave. The segmentation image l z can also combine the regions of interest appearing following the respective segmentations of the image M z of the module as well as of the image φ ζ of the phase of the exposure light wave.
    A count of the particles of interest by a simple counting of the regions of interest appearing on the segmentation image I z can give a first order of magnitude on the quantity of the particles of interest. But the inventors have found that such a counting gives imprecise results. Indeed, certain regions of interest correspond to particles different from the particles that one wishes to count. In addition, certain regions of interest bring together several particles of interest, the latter forming clusters. This is due to the fact that certain particles of interest are close to each other, and are combined in the same region of interest. In FIGS. 1A and 1B, clusters 10a2 comprising two particles and diagrams 10a3 comprising three particles are shown diagrammatically. These causes of error are dealt with in the steps described below.
    Step 140: selection. During this stage, a selection is made of the regions of interest determined in the preceding stage as a function of their morphology, that is to say as a function of a morphological criterion, the latter possibly being the area , shape or size. This makes it possible to select the regions of interest corresponding to the particles of interest that one wishes to count.
    This step can comprise a filtering of each region of interest according to the morphological criterion previously mentioned, so as to select regions of interest representative of the particles of interest. For example, when the particles of interest are red blood cells, the filtering makes it possible to select the regions of interest having a diameter less than 100 μm, or registered in such a diameter. This allows not to take into account large dust or traces, while retaining clusters of red blood cells.
    In order to take into account agglutination effects between particles of interest, a classification of the regions of interest selected can be carried out according to their area and their shape, in particular based on a reference size S rep , for example a reference area, the latter being for example representative of a region of interest corresponding to a single particle of interest (singlet). The reference area can be predetermined or obtained from the segmentation image! (, For example by calculating an average, or a median, of the area of each region of interest ROI, and making the assumption that the majority of the regions of interest correspond to a single particle. When, as in this example, the image sensor is placed close to the sample. The size of each region of interest is then compared with reference size , depending on what each region of interest is assigned a number of particles. By size, we mean a diameter or an area or another characteristic dimension. For example, in connection with Figure 2D:
    when the size of a region of interest is between 0.5 times and 1.5 times the reference size, it is considered that the region of interest represents a single particle of interest, which corresponds to a singlet, such a region d interest being noted ROT. In Figure 2D, the singlets are represented by white dots.
    When the size of a region of interest is between 1.5 times and 2.5 times the reference size, it is considered that the region of interest represents two particles of interest; in FIG. 2D, such a region of interest ROI 2 , corresponding to a doublet, is symbolized by a white oval outline.
    When the size of a region of interest is between 2.5 times and 3.5 times the reference size, it is considered that the region of interest represents three particles; in FIG. 2D, such a region of interest ROI 3 is symbolized by a white frame.
    The ROI n notation corresponds to a region of interest considered, following the classification step, as comprising of n particle (s), n being a strictly positive integer.
    Alternatively or in addition, prior to classification, a selection can be made according to the shape of each region of interest. For example, a larger diameter and a smaller diameter are determined for each region of interest. The ratio between the largest diameter and the smallest diameter makes it possible to quantify a form factor of the region of interest considered. According to this selection, one can identify the regions of interest whose shape cannot be considered to be spherical, these regions of interest not being considered as representative of particles of interest and not being taken into account in the numeration. Thus, the selection can include a comparison between the shape of each region of interest and a shape representative of the particle of interest, or of clusters of particles of interest, to be detected.
    For each region of interest present in the segmentation image f, a signal to noise ratio S / B can be established. Such a signal-to-noise ratio can be calculated by making a ratio between an average value of so-called central pixels located at the center of the region of interest, for example 9 central pixels, and the standard deviation calculated on a background of the image, the background of the image corresponding to the reconstructed image without the regions of interest. FIG. 2E is a histogram of the values of the S / B ratios calculated for the different regions of interest identified on the sample illustrated in FIGS. 2C and 2D. Most regions of interest have an S / N ratio greater than 4, which translates into reliable detection and a good degree of confidence in the selection of regions of interest, presaging reliable counting. Step 140 may also include filtering of regions of interest as a function of their S / N signal-to-noise ratio. Indeed, below a certain threshold, it is considered that the signal-to-noise ratio is too low for it to be effectively possible to consider that a region of interest corresponds to a particle. FIGS. 3A and 3B represent an example of filtering of regions of interest on the basis of a threshold value of the signal to noise ratio equal to 1.5. As can be seen in the signal-to-noise ratio histograms of the regions of interest represented in FIGS. 3A and 3B, said histograms being the subject of FIGS. 3C and 3D respectively, the thresholding makes it possible to eliminate the regions of interest with a low signal to noise ratio, the latter being surrounded by a thick circle in FIG. 3A. In this example, the filtering of regions of interest based on the S / N ratio has been limited to singlets.
    According to one embodiment, step 140 can comprise the following sequence of operations, described in connection with FIG. 3E step 141: filtering of each region of interest ROI according to a range of diameter representative of the particles of interest, by example 3 pm - 12 pm in the case of red blood cells, and according to a form criterion.
    step 142: calculation of an average size of the regions of interest resulting from this filtering, this average size constituting a first reference size step 143: classification of each region of interest ROI of the segmentation image, on the basis of the first reference size S re f_ 1 , so as to assign an integer number n of particles to each region of interest ROI n ;
    step 144: calculation of an S / N signal to noise ratio of each region of interest
    ROIi considered to be a singlet;
    step 145: calculation of an average size of the ROf singlets having a sufficient signal-to-noise ratio, that is to say greater than a threshold, the threshold possibly being equal to 1.5, this average size constituting a second reference size S re p_ 2 ;
    step 146: classification of each region of interest ROI of the segmentation image, on the basis of the second reference size S re p_ 2 resulting from step 145; step 147: assignment, to each ROI region of interest, of a number of particles n determined during the classification.
    This algorithm allows a gradual adjustment of the reference size on the basis of which the classification of the regions of interest is carried out. This provides a more precise classification.
    Thus, step 140 makes it possible to select the regions of interest representative of the particles of interest, and / or to assign a number of particle (s) to each region of interest considered to be representative of particles of interest.
    Step 150: counting. During this step, there are the regions of interest selected during step 140, that is to say considered as representative of particles, taking into account a number of particles possibly assigned to each of them. they.
    The step 120 described above can be carried out by carrying out a convolution between a propagation operator and the image acquired by the image sensor. However, the application of such an operator can generate significant reconstruction noise. In order to optimize the reconstruction, by limiting the reconstruction noise, step 120 can be implemented according to an algorithm as described in patent application FR1652500 filed on March 23, 2016. We will now describe, in connection with FIGS. 4A and 4B, the main steps of this algorithm. It is an iterative algorithm, the steps 121 to 125 described below being reiterated, k designating the rank of an iteration. This process aims to determine the phase of the exposure light wave 14 in the detection plane P o , so as to gradually obtain an estimate of the complex amplitude 4 0 = A z = 0 in the detection plane. During each iteration k, the complex image in the detection plane obtained during a previous iteration, denoted 4§ _1 , is propagated in the reconstruction plane Pz, to form a complex image Az in the reconstruction plane Pz . Each iteration consists in adjusting the phase <ρθ of the complex image A * in the detection plane Po according to a noise criterion 8 k determined from the complex image Az.
    Step 121: propagation from the detection plan to the reconstruction plan
    During this step, the image formed in the detection plane P o is available . During the first iteration, an initial image at = = 0 is determined from the image Io acquired by the image sensor. The module M k = 0 of the initial image at § = 0 can be obtained by applying the square root operator to the image acquired Io by the image sensor, in which case Λίθ = 0 =
    An arbitrary value, for example 0, is assigned to the phase (p k = 0 of the initial image. During the following iterations, the image in the detection plane is the complex image with § _1 resulting from the iteration The image formed in the detection plane Po is propagated in the reconstruction plane Pz, by the application of a propagation operator h as previously described, so as to obtain a complex image Az, representative of the sample 10, in the reconstruction plane Pz. The propagation is carried out by convolution of the image at § _1 by the propagation operator h_z, so that:
    d k = Aq 1 * h_ z ,
    The index - z represents the fact that the propagation is carried out in a direction opposite to the axis of propagation Z. We speak of backpropagation.
    Step 122: Calculation of an indicator in several pixels
    During this step, a quantity e fe (x, y) is calculated associated with each pixel of a plurality of pixels (x, y) of the complex image at k , and preferably in each of its pixels. This quantity depends on the value Az (x, y) of the image Az, or its module, at the pixel (x, y) at which it is calculated. It can also depend on a dimensional derivative of the image in this pixel, for example the module of a dimensional derivative of this image. In this example, the quantity £ k (x, y) associated with each pixel is a module of a difference of the image Az, in each pixel, and the value 1. Such a quantity can be obtained according to the expression:
    £ fe (x, y) = J (A z (x, y) - l) (A k (x, y) - l) * = A k (x, y) - l |
    Step 123: establishment of a noise indicator associated with the image A k .
    During step 122, magnitudes £ k (x, y) are calculated in several pixels of the complex image A z . These quantities can form a vector E k , the terms of which are the quantities 8 k (x, y) associated with each pixel (x, y). In step 123, an indicator, called noise indicator, is calculated from a standard of the vector E k . The quantity £ k (x, y) calculated from the complex image
    A k , at each pixel (x, y) of the latter, is summed so as to constitute a noise indicator 8 k associated with the complex image A k e ^.
    So = Z ( X , y) £ fe (^ y)
    An important aspect of this step consists in determining, in the detection plane P o , phase values ç) q (x, y) of each pixel of the image A k in the plane of the sample, making it possible to obtain, during a following iteration, a reconstructed image A k + i whose noise indicator £ k + 1 is less than the noise indicator 8 k .
    During the first iteration, only relevant information is available on the intensity of the exposure light wave 14, but not on its phase. The first reconstructed image A k = i in the reconstruction plane Pz is therefore affected by significant reconstruction noise, due to the absence of relevant information as to the phase of the light wave 14 in the detection plane Po. Therefore, the indicator 8 k = 1 is high. During the following iterations, the algorithm proceeds to a progressive adjustment of the phase (p k (x, y) in the detection plane Po, so as to progressively minimize the indicator 8 k .
    The image in the detection plane is representative of the light wave 14 in the detection plane P o , both from the point of view of its intensity and of its phase. Steps 120 to 125 aim to establish, iteratively, the value of the phase (p k (x, y) of each pixel of the image A k , minimizing the indicator s k , the latter being obtained on the image A k obtained by propagation of image A k_1 in the reconstruction plane P z .
    The minimization algorithm can be a gradient descent or conjugate gradient descent algorithm, the latter being described below.
    Step 124: Adjustment of the phase value in the detection plane.
    Step 124 aims to determine a value of the phase (p k (x, y) of each pixel of the complex image so as to minimize the indicator 8 k + i resulting from propagation of the complex image A k in the reconstruction plane Pz, during the next iteration k + 1. For this, a phase vector is established, each term of which is the phase <p k (x, y) of a pixel (x, ÿ) of the complex image A k . The dimension of this vector is (NPj X , 1), where N P i X denotes the number of pixels considered. This vector is updated during each iteration, by the following update expression:
    <Po (x, ÿ) = <Ρο _1 (ΛΥ) + a k p k (x, y) WHERE:
    a k is an integer, designated by the term "step", and representing a distance;
    p k is a direction vector, of dimension (N P j X , 1), each term p (x, y) of which forms a direction of the gradient Vf fe of the indicator 8 k .
    This equation can be expressed in vector form, as follows:
    Ψο = Ψο 1 + a k p k
    We can show that:
    pk _ _ ^ f k _ | _ pkpk-l where:
    Vf k is a gradient vector, of dimension (N P i X , 1), each term of which represents a variation of the indicator 8 k as a function of each of the degrees of freedom of the unknowns of the problem, that is to say say the terms of the vector φ ^. ;
    p k ~ i is a direction vector established during the previous iteration;
    P k is a scale factor applied to the direction vector p k ~ r .
    Each term Ns k (x, y) of the gradient vector Vf, is such that
    V (R ') = ds k d <Po (r') = Iml r ').
    (C -1)
    A k -1 k re f I * h (r ') where Im represents the imaginary part operator and r' represents a coordinate (x, y) in the detection plane.
    The scale factor can be expressed so that:
    w y g w vg w p vd k - l vd k ~ l)
    The step a k can vary according to the iterations, for example between 0.03 during the first iterations and 0.0005 during the last iterations.
    The update equation allows an adjustment of the vector φ ^ to be obtained, which results in an iterative update of the phase <p k (x, y) in each pixel of the complex image This complex image A k , in the detection plane, is then updated by these new phase values associated with each pixel.
    Step 125: Reiteration or output of algorithm.
    As long as a convergence criterion is not reached, step 125 consists in reiterating the algorithm, by a new iteration of steps 121 to 125 on the basis of the complex image A k updated during the step 124. The convergence criterion may be a predetermined number K of iterations, or a minimum value of the gradient Vs fe of the indicator, or a difference considered to be negligible between two consecutive phase vectors <ρθ _1 , <ρθ. When the convergence criterion is reached, there is an estimate considered to be correct of a complex image of the sample in the detection plane P o .
    Step 126: Obtaining the complex image in the reconstruction plan.
    At the end of the last iteration, the method comprises a propagation of the complex image A * resulting from the last iteration in the reconstruction plane P z , so as to obtain a complex image in the reconstruction plane A z = A z .
    Experimental trials
    During a first series of tests, the previously described method was used on diluted blood samples to count red blood cells. FIGS. 5A to 5D represent the results obtained by the method as a function of values from an ABX Pentra 120 DX hematology automaton. The experimental conditions are as follows:
    sample 10: diluted blood contained in a Countess® fluid chamber with a thickness of 100 μm, the volume examined reaching 3 mm 3 , ie 3 μl.
    light source 11: light emitting diode Created MC-E Color, comprising three light emitting diodes emitting respectively in the following spectral bands Δλ: 450 nm - 465 nm; 520 nm - 535 nm; 630 nm - 640 nm. In this example, only one diode is activated during each illumination. Alternatively, a laser diode emitting at a wavelength of 405 nm was used, according to the embodiment shown in Figure IB.
    image sensor: monochrome 3840 x 2748 pixel IDS pEye CMOS sensor, each pixel measuring 1.67 μm by side, the detection surface extending over approximately 30 mm 2 ; distance D between the light source 11 and the sample 10: 8 cm when the light source 11 is the light-emitting diode and 15 cm when the light source is the laser diode distance d between the sample 10 and the sensor image 16: 1500 pm; thickness e of the fluid chamber 15: 100 μm;
    diameter of the opening of the spatial filter 18 when the light source 11 is a light-emitting diode: 150 μm;
    In this first series of tests, human blood was diluted in a spherical reagent, allowing a modification of the surface tension of the red blood cells so as to make them spherical. A calibration is carried out beforehand, using a reference automaton, so as to carry out, in a manner known to those skilled in the art, a readjustment between the concentration of the blood and the number of red blood cells counted. This registration takes into account the dilution factor, the thickness of the chamber and the surface of the sample exposed to the image sensor.
    The sample preparation protocol was as follows: dilution to 1/600 th in a sphering reagent;
    taking 10 μΙ of diluted blood and injecting it into the fluid chamber located opposite the image sensor;
    Each sample was counted by an ABX Pentra DX 120 automaton, used as a reference method.
    samples were measured. FIGS. 5A, 5B, 5C and 5D respectively show the results obtained by implementing the invention (axes of the ordinates) as a function of the reference measurements (axes of the abscissas), the light source being:
    the light emitting diode emitting in the spectral band 450nm - 465 nm; the light emitting diode emitting in the spectral band 520nm - 535 nm; the light emitting diode emitting in the spectral band 630nm - 640 nm; the laser diode.
    A linear regression was established for each of these figures, the expression of each regression being respectively:
    - y = 1.017% - 0.04 (r 2 = 0.98);
    - y = 1.019% - 0.07 (r 2 = 0.98);
    - y = 1.017% - 0.07 (r 2 = 0.98);
    - y = 1.027% - 0.11 (r 2 = 0.98).
    The coefficient r 2 is the coefficient of determination associated with each linear regression of the Passing-Bablok type.
    In a second series of tests, white blood cells were counted in human blood samples, after lysis of the red blood cells by adding a lysis reagent. The image segmentation was carried out on the basis of a thresholding based on a criterion of maximum entropy. In order to take into account the density of the white blood cells, the dilution factor used was equal to 10. FIG. 6A represents an image of the module of the complex image in the reconstruction plane P z , the latter being a plane according to which extends the sample. FIG. 6B represents the image of FIG. 6A after segmentation, binarization and detection of clusters comprising two white blood cells. These are bypassed by a white ring. The identification of singlets and doublets allows a more precise quantification of white blood cells, compared to a simple enumeration of the regions of interest. Figures 6C and 6D correspond respectively to Figures 6A and 6B on another sample. The acquisition was carried out using a laser type light source, as previously described.
    The method was also tested, during a third series of tests, on glass beads with a diameter of 5 μm. These are Bangs laboratories - SS06N reference beads, diluted to 1 / 2000th in a PBS-type saline buffer. The acquisition was carried out using a laser type light source, as previously described. We observe the detection of a cluster comprising two balls. FIG. 7A represents an image of the module of the complex image in the reconstruction plane P z , the latter being a plane along which the sample extends. FIG. 7B represents the image of FIG. 7A after segmentation, binarization and detection of clusters comprising two balls, these clusters being surrounded by a white outline.
    In the case of red blood cells, the inventors believe that it is preferable that the surface density of particles of interest to be counted is less than 2000 particles per mm 2 of detection surface. Beyond this, the particles are too close to each other, which degrades the signal-to-noise ratio of each region of interest. Also, it is advantageous to estimate, according to the different cases, a maximum surface density, and to adapt accordingly the dilution to be applied to the sample.
    One of the intended applications is the counting of blood cells, which can be white blood cells, red blood cells or platelets. The inventors have found that when it is a case of white blood cells or red blood cells, during step 120, it is preferable that the reconstructed image I z represents the modulus of the complex amplitude A z in the plane of reconstruction P z . The definition of the edges of the regions of interest, corresponding to the cells, is sharper. When the particles of interest are platelets, it seems preferable on the other hand that the reconstructed image l z represents the phase of the complex amplitude A z in the reconstruction plane P z . FIGS. 8A and 8B represent a histogram of the signal-to-noise ratio of regions of interest corresponding to red blood cells, based respectively on the module or the phase of the reconstructed complex image. The number of red blood cells considered was equal to 12166 and 12888, respectively. There is a higher signal-to-noise ratio when using an image of the module (average of 18.6) than when using a phase image ( average of 13.9). In these tests, the red blood cells were illuminated in a blue spectral band.
    The invention can be applied to the counting of particles of interest in the blood, but also in other body fluids of the urine, saliva, sperm type. It can also be applied in the quantification of microorganisms, for example bacteria or bacterial colonies. Beyond the applications related to biology or health, the invention can be implemented in the control of samples in industrial fields or the control of the environment, for example the food industry or the control of fluids industrial.
    权利要求:
    Claims (12)
    [1" id="c-fr-0001]
    1. Method for counting particles (10a), called particles of interest, arranged in a sample (10), the method comprising the following steps:
    a) illumination of the sample (10) using a light source (11), the light source emitting an incident light wave (12) propagating towards the sample (10);
    b) acquisition, using an image sensor (16), of an image ( 70 ) of the sample (10), formed in a detection plane (P o ), the sample being disposed between the light source (11) and the image sensor (16), each image being representative of a light wave (14), called the exposure light wave, to which the image sensor (16) is exposed ) under the effect of said illumination;
    the method being characterized in that it also comprises the following steps:
    c) from the image acquired ( 70 ) during step b), application of a propagation operator (h) so as to calculate a complex expression (4 Z ) of the exposure light wave (14), along a reconstruction surface (P z ) extending opposite the detection plane (P o );
    d) formation of an image (Ι ζ , Μ ζ , φ ζ ), said reconstructed image, representative of a distribution of the module or of the phase of the complex expression according to the reconstruction surface;
    e) segmentation of the image formed during step d) to obtain a segmentation image (7 Z ), comprising regions of interest (ROI) spaced from each other, all or part of the regions of interest being associated with at least one particle of interest;
    f) determining a size of regions of interest, and classification of said regions of interest according to their size according to at least one of the following classes:
    a class according to which the region of interest comprises a single particle of interest;
    a class according to which the region of interest comprises an integer, greater than 1, of particles of interest;
    g) counting of the particles of interest from the regions of interest which were the subject of a classification during step f).
    [2" id="c-fr-0002]
    2. Method according to claim 1, in which step f) comprises, prior to classification:
    - a determination of at least one morphological criterion for each region of interest;
    - a selection of regions of interest (ROI) according to the morphological criterion, the classification being carried out on the regions of interest selected.
    [3" id="c-fr-0003]
    3. Method according to claim 2, in which at least one morphological criterion is chosen from a diameter, a size or a form factor of each region of interest.
    [4" id="c-fr-0004]
    4. Method according to any one of the preceding claims, in which step f) takes account of a reference size (S re fi, representative of a predetermined number of particles of interest, the classification is made according to said reference size.
    [5" id="c-fr-0005]
    5. Method according to any one of the preceding claims, in which step f) comprises the following substeps:
    fi) a first classification of the regions of interest, carried out as a function of a first reference size (57 ^^), the first reference size corresponding to a predetermined number of particles of interest;
    fii) following the first classification, a determination of a second reference size (S re ^ _ 2 ), from the regions of interest classified, during sub-step fi), as comprising a predefined number of particles d 'interest;
    fiii) a second classification of the regions of interest, carried out according to the second reference size (S re ^ _ 2 ) determined during the sub-step fii).
    [6" id="c-fr-0006]
    6. Method according to any one of the preceding claims, in which step f) comprises, prior to classification:
    a calculation of a signal to noise ratio (S / B) of several regions of interest (ROI);
    a selection of the regions of interest (ROI) as a function of the signal to noise ratio (S / B) calculated for each of them;
    the classification being carried out on the regions of interest thus selected.
    [7" id="c-fr-0007]
    7. Method according to any one of the preceding claims, in which the sample is mixed with a spherization reagent prior to step b), to modify the shape of the particles of interest (10a) so as to make it spherical. .
    [8" id="c-fr-0008]
    8. Method according to any one of the preceding claims, in which step c) comprises the following substeps:
    ci) definition of an initial image of the sample (4§ = 0 ) in the detection plane, from the image (7 0 ) acquired by the image sensor;
    cii) determination of a complex image of the sample (4 Z ) in a reconstruction surface (P z ) by applying a propagation operator to the initial image of the sample (4§ = 1 ) defined during the sub-step ci) or like the sample (4§ _1 ), in the detection plan, resulting from the previous iteration (/ c - 1);
    ciii) calculation of a noise indicator (e fe ) from the complex image (4 Z ) determined during the sub-step cii), this noise indicator depending on a reconstruction noise affecting said complex image ( 4 Z );
    civ) update of the image of the sample in the detection plane (P 0 ) by an adjustment of phase values (</ θ (%, γ)) of the pixels of said image, the adjustment being performed as a function of a variation of the indicator calculated during the sub-step ciii) according to said phase values;
    cv) reiteration of the sub-steps cii) to civ) until a convergence criterion is reached, so as to obtain a complex image of the sample (10) in the detection plane (P o ) and in the reconstruction surface (P z ).
    [9" id="c-fr-0009]
    9. Method according to any one of the preceding claims, in which no image-forming optics are arranged between the sample (10) and the image sensor (16).
    [10" id="c-fr-0010]
    10. Method according to any one of the preceding claims, in which the particles of interest are blood cells.
    [11" id="c-fr-0011]
    11. The method of claim 10 in which, during step d):
    - The particles of interest (10a) are red blood cells or white blood cells, the reconstructed image being representative of a distribution of the module the complex expression (A z ) according to the reconstruction surface (P z );
    - The particles of interest (10a) are platelets, the reconstructed image being representative of a distribution of a phase of the complex expression (A z ) according to the reconstruction surface (P z ).
    [12" id="c-fr-0012]
    12. Device for counting particles of interest (10a) arranged in a sample (10), the device comprising:
    - a light source (11) capable of emitting an incident light wave (12) propagating towards the sample (10);
    - a support (10s), configured to hold the sample (10) between the light source (11) and an image sensor (16);
    5 a processor (20), configured to receive an image of the sample acquired by the image sensor (16) and to implement steps c) to g) of the method which is the subject of any one of claims 1 to 11.
    1/7
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    2018-06-22| PLSC| Publication of the preliminary search report|Effective date: 20180622 |
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    优先权:
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    FR1663028|2016-12-21|
    FR1663028A|FR3060746B1|2016-12-21|2016-12-21|METHOD FOR NUMBERING PARTICLES IN AN IMAGING SAMPLE WITHOUT LENS|FR1663028A| FR3060746B1|2016-12-21|2016-12-21|METHOD FOR NUMBERING PARTICLES IN AN IMAGING SAMPLE WITHOUT LENS|
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    US16/471,429| US11199488B2|2016-12-21|2017-12-20|Method for counting particles in a sample by means of lensless imaging|
    PCT/FR2017/053725| WO2018115734A1|2016-12-21|2017-12-20|Method for counting particles in a sample by means of lensless imaging|
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