• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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    • 专利摘要:
    The invention is a method for determining an amount of leukocytes in a sample. The method is based on an image, or hologram, of the sample obtained by an image sensor when the sample is illuminated by a light source. The hologram is the object of a holographic reconstruction, so as to obtain a complex image, called complex reference image, representing a light wave transmitted by the sample, in a reconstruction plane. To this complex reference image is applied a holographic propagation operator, so as to obtain a plurality of so-called secondary complex images, forming a stack of images, from which a profile describing the evolution of an optical characteristic is determined. of the light wave transmitted by the sample along the axis of propagation of this light wave. The presence of leukocytes and their quantity are determined according to these profiles.
    公开号:FR3056749A1
    申请号:FR1659161
    申请日:2016-09-28
    公开日:2018-03-30
    发明作者:Cedric ALLIER；Lionel Herve；Sophie MOREL；Damien ISEBE；Michel Drancourt (Pr.)；Anais ALI CHERIF
    申请人:Aix Marseille Universite；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, UNIVERSITE D'AIX-MARSEILLE Public establishment.
    Agent (s): INNOVATION COMPETENCE GROUP.
    U> 4) METHOD FOR NUMERING LEUKOCYTES IN A SAMPLE.
    FR 3 056 749 - A1 (5d) The invention is a method for determining an amount of leukocytes in a sample. The method is based on an image, or hologram, of the sample obtained by an image sensor when the sample is illuminated by a light source. The hologram is the subject of a holographic reconstruction, so as to obtain a complex image, called complex reference image, representing a light wave transmitted by the sample, in a reconstruction plane. A holographic propagation operator is applied to this complex reference image, so as to obtain a plurality of so-called secondary complex images, forming a stack of images, from which a profile is described describing the evolution of an optical characteristic. of the light wave transmitted by the sample along the axis of propagation of this light wave. The presence of leukocytes and their quantity are determined according to these profiles.
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    Method for counting leukocytes in a sample
    Description
    TECHNICAL AREA
    The invention is an optical method for the analysis of particles contained in a sample. In particular, the invention allows the counting of cells in the cerebrospinal fluid. A targeted application is to aid in the diagnosis of meningitis.
    PRIOR ART
    Cytological analysis of the cerebrospinal fluid is an important step in the diagnosis of meningitis. It is recognized that a concentration of at least 10 leukocytes per μΙ, may be the signature of a meningitis, in particular of a bacterial meningitis. Currently, such an analysis is generally carried out under a microscope, but the field of observation is weak. Consequently, the analysis of a sample is long, difficult to automate and requires an expensive analysis device as well as the intervention of a highly qualified operator. Furthermore, such an analysis can be operator dependent.
    Furthermore, the observation of samples, and in particular biological samples, by lensless imaging has experienced significant development over the past ten years. This technique makes it possible to observe a sample by placing it 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 the light wave transmitted by the sample. This image, also called hologram, is made up of interference patterns 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 sometimes called diffraction figures, or designated by the English term "diffraction pattern".
    The document WO2008090330 describes a device allowing the observation of biological samples, in this case cells, by lensless imaging. The device makes it possible to associate, with each cell, an interference figure whose morphology makes it possible to identify the type of cell.
    Lensless imaging then appears as a simple, inexpensive alternative to a conventional microscope. In addition, it makes it possible to acquire images whose field of observation is much larger than that of a microscope.
    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 incident light wave, emitted by the light source. Such algorithms are well known in the field of holographic reconstruction. For this, the distance between the sample and the image sensor being known, a propagation algorithm is applied, taking into account this distance, as well as the wavelength of the incident light wave. We can then reconstruct an image of an optical property of the sample. The reconstructed image can, in particular, be a complex image of the light wave transmitted by the sample, comprising information on the optical absorption or phase variation properties of the sample. 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, holographic reconstruction algorithms can induce reconstruction noise in the reconstructed image, designated by the term of “twin image”. This is essentially due to the fact that the image formed on the image sensor does not include information relating to the phase of the light wave reaching this sensor. Therefore, the holographic reconstruction is carried out on the basis of incomplete optical information, based solely on the intensity of the light wave collected on the image sensor. The improvement of the quality of the holographic reconstruction is the subject of numerous developments, by implementing algorithms frequently called “phase retrieval”, allowing an estimation of the phase of the light wave at which the image sensor is exposed. However, such algorithms may require a multitude of acquisitions, for example by modifying the position of the light source relative to the sample. If the reconstruction performance is correct, the complexity of implementation confines these algorithms to laboratory uses and makes them difficult to apply to current uses.
    Application US2012 / 0218379 describes, for example, a method for reconstructing a complex image of a sample, said complex image comprising amplitude and phase information. Such an image makes it possible to obtain certain information allowing the identification of a cell. Application US2012 / 0148141 applies the method described in application US2012 / 0218379 to reconstruct a complex image of spermatozoa and to characterize their mobility, their orientation, or certain geometric parameters, for example the size of their flagellum.
    The inventors looked for a method allowing the counting of particles in a liquid sample, and more particularly of leukocytes, allowing a reliable observation, being able to be automated, and having a high field of observation. Unlike the prior art, the method implements a complex image not to track the position of a particle, but to count and identify it. In addition, the process is simple, easily automated, and compatible with routine use.
    STATEMENT OF THE INVENTION
    An object of the invention is a method for determining an amount of particles of interest contained in a sample, the sample extending along a plane, called the plane of the 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 along a propagation axis;
    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) from the image acquired during step b), application of a propagation operator so as to calculate a complex image, called complex reference image, representative of the sample, in a reference plane;
    d) determination of a radial position of several particles in a plane parallel to the detection plane, each radial position being associated with a particle;
    e) calculation of at least one quantity characteristic of the exposure light wave, at each radial position determined during step d), and at a plurality of distances from the detection plane, called reconstruction distances;
    f) formation of a profile, representing an evolution of the characteristic quantity calculated during step e) as a function of the reconstruction distance, along an axis parallel to the axis of propagation and passing through each radial position determined during step d), each profile being associated with a particle;
    g) as a function of the profile formed during step f), identification of the particles of interest;
    h) determining an amount of particles of interest in the sample from a number of particles of interest identified during step g).
    The particles of interest can for example be blood cells, for example leukocytes and / or erythrocytes.
    The reference plane can be the sample plane.
    According to one embodiment, during step e), the characteristic quantity comprises the module or the phase of a complex expression of the exposure light wave. It can be the module, the phase, or a combination of the module and the phase, for example in the form of a ratio.
    According to one embodiment, step d) comprises the following sub-steps:
    dl) application of a propagation operator to the reference image, so as to obtain complex images, called secondary complex images, at different distances from the reference plane, or from the detection plane, along the propagation axis, the secondary complex images and the complex reference image forming a stack of complex images; d2) determination of a radial position of particles from the images of the complex image stack obtained during sub-step dl).
    In this case, the radial position of each particle is obtained by forming:
    a first image, each point of which represents a maximum value, at said point and along the axis of propagation, of a module or of a phase of the images of the stack of complex images;
    a second image, each point of which represents a minimum value, at said point and along the axis of propagation, of a module or of a phase of the images of the stack of complex images;
    a differential image representing a difference between the first image and the second image;
    the radial position of each particle then being obtained by applying a threshold to the differential image.
    Step d) can be implemented from the image acquired by the image sensor during step b), or from the complex reference image calculated during step c).
    According to one embodiment, step e) comprises the following sub-steps:
    el) application of a propagation operator to the reference image, so as to obtain complex images, called secondary complex images, at different distances from the reference plane along the propagation axis, the secondary complex images and the complex reference image forming a stack of complex images;
    e2) determination of the module or of the phase of the wave to which the image sensor is exposed from images of the stack of complex images obtained during sub-step e1).
    Step g) may include a classification of one or more profiles associated with each particle. This classification may include the application of a threshold to each profile, so that a profile crossing a predetermined threshold is considered to be representative of a particle of interest. The profile can for example be a profile representing the evolution of the phase of the exposure light wave, along the axis of propagation.
    Step g) can comprise the determination of a reconstruction distance, along the propagation axis, of a maximum or minimum value of a profile, such that a profile for which the maximum or minimum value s 'extending beyond a threshold distance from the sample plane, or from the detection plane, is considered to be representative of a leukocyte. The profile may be a profile representing an evolution of the module of the exposure light wave, along the axis of propagation.
    Step g) can comprise the calculation of a surface extending between the profile and a line segment connecting a first remarkable point and a second remarkable point of the profile. The first remarkable point of the profile can be an extremum of the profile, for example the minimum. The second remarkable point can be a point at which the profile takes a predetermined value, at a reconstruction distance greater than that corresponding to the first point.
    According to a preferred embodiment:
    step a) comprises an illumination of the sample according to two spectral bands distinct from one another;
    step b) comprises an acquisition of an image of the sample in each of the two spectral bands;
    step d) is implemented on the basis of the image acquired, during step b), in the first spectral band;
    step e) is implemented on the basis of the image acquired, during step b), in the second spectral band.
    The first spectral band can be between 400 nm and 550 nm. The second spectral band can be between 500 nm and 600 nm.
    According to one embodiment, the method comprises the following steps:
    gbis) classification of each particle, the radial position of which was identified during step d), as being, or not, an erythrocyte according to the profile formed during step f); hbis) determination of an amount of erythrocytes in the sample as a function of the classifications carried out during step g);
    and eventually :
    i) determination of a ratio between the quantity of leukocytes determined during step h) and the quantity of erythrocytes determined during step hbis).
    Preferably, there is no magnification optic between the sample and the image sensor. Another object of the invention is a device for counting particles of interest, for example leukocytes, in a sample, the device comprising:
    - a light source capable of emitting an incident light wave propagating towards the sample, along an axis of propagation;
    - a support, configured to hold the sample between said light source and an image sensor, the image sensor extending along a detection plane;
    a processor, configured to receive an image of the sample acquired by the image sensor and to implement steps c) to h) of the method as described in this description. Preferably, no magnification optics are arranged between the sample and the image sensor when the sample is held on the support.
    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
    Figure 1 shows an example of a device for implementing the invention.
    FIG. 2A represents the main steps of a method according to the invention. Figures 2B to 2G show the results of certain steps described in connection with Figure 2A. FIG. 2B shows part of an image acquired by the image sensor. FIGS. 2C and 2D respectively represent the module and the phase of a complex image, called the reference image, reconstructed on the basis of FIG. 2A, in a reference plane. FIG. 2E shows diagrammatically a stack of images obtained by propagation of the complex reference image in different planes parallel to each other, on either side of the reference plane. FIGS. 2F and 2G respectively illustrate the evolution of the module and of the phase of the wave to which the image sensor is exposed, the module and the phase being determined from the images of the stack of images represented on the Figure 2E. FIG. 2H represents a preferred variant of a method according to the invention.
    FIG. 3A illustrates the main steps of a method making it possible to obtain a complex image of the sample, called the reference image, in a reference plane. Figure 3B schematizes the steps described in connection with Figure 3A.
    FIG. 4A shows thumbnails representing the evolution of an area of interest of the module of each image of the stack represented in FIG. 2E, along the axis of propagation of the light. The area of interest represented on each vignette corresponds to the same particle. FIG. 4B shows thumbnails representing the evolution, along the axis of light propagation, of an area of interest of the phase of each image of the stack shown in FIG. 2E.
    FIGS. 5A and 5B respectively show profiles representing the evolution, in a direction parallel to the axis of propagation of light, of the modulus and of the phase of the light wave to which the image sensor is exposed, the radial position of the profiles being centered on particles, in this case leukocytes or erythrocytes. These profiles were produced by illuminating the sample with a red spectral band.
    FIGS. 5C and 5D respectively show profiles representing the evolution, in a direction parallel to the axis of propagation of light, of the modulus and of the phase of the light wave to which the image sensor is exposed, the radial position of the profiles being centered on leukocytes or on erythrocytes detected. These profiles were produced by illuminating the sample with a blue spectral band.
    FIG. 6A represents an area calculated between on the one hand the profile and on the other hand a straight line connecting a minimum point of the profile and a point of the profile at which the profile takes a predetermined value. The area shown in this figure is used to classify the particles according to their profile. FIG. 6B represents the value of such an area on profiles corresponding to erythrocytes and leukocytes.
    FIG. 7 shows the results of a leukocyte and erythrocyte count on samples of cerebrospinal fluid taken from human patients with different pathologies.
    EXPLANATION OF PARTICULAR EMBODIMENTS
    FIG. 1 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 in the direction of a sample 10, along a propagation axis Z. The light wave is emitted according to a spectral band Δλ.
    Sample 10 is a sample that we wish to characterize. It notably comprises a medium 10m in which particles 10a and 10b bathe. The particles 10a and 10b are, in this example, blood cells, in particular red blood cells (erythrocytes) and white cells (leukocytes) respectively. The medium 10m, in which the particles are immersed, can be a body fluid, and for example cerebrospinal fluid (CSF), possibly diluted. The sample may contain other particles. By particle is meant for example a cell, or a fragment thereof, a microorganism, a spore, or a microbead.
    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, typically varies between 10 μm and 1 cm, and is preferably between 20 μm and 500 μm. The sample extends along a plane Pw> said plane of the sample, 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. In Figure 1, the light source is a light emitting diode. It is generally associated with 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 facing the light source 11 and a second end of which is placed opposite the sample 10. The device represented in FIG. 1 also includes a diffuser 17 , arranged between the light source 11 and the diaphragm
    18. The use of such a diffuser makes it possible to overcome the constraints of centering 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 an elementary light source 11 ,, (l <i <3) according to a cone of angle a. Preferably, the angle of diffusion a varies between 10 ° and 80 °. The presence of such a diffuser is particularly useful when the light source comprises different elementary light sources, as described below.
    Alternatively, the light source can be a laser source, such as a laser diode. In this case, it is not useful to associate a spatial filter or a diffuser.
    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.
    According to one embodiment, the light source 11 comprises several elementary light sources 11, each being able to emit an incident light wave 12, in a spectral band Δλ ,. Preferably, the spectral bands Δλ, of the different light sources 11, are different from each other.
    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 plane Pio 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 perpendicular 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 preferably between 50 pm and 2 cm, preferably between 100 pm and 2 mm.
    Note the absence of 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 n having no magnification function of 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, in particular with part of the incident light wave 12 transmitted by the sample. Furthermore, the sample can absorb part of the incident light wave 12. Thus, the light wave 14, transmitted by the sample, and to which the image sensor 20 is exposed, designated by the term d wave , may include:
    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 absorption of the incident light wave 12 by the sample.
    These components form interference in the detection plane. Also, the image acquired by the image sensor includes interference patterns (or diffraction patterns), each interference pattern being generated by a particle of the sample.
    A processor 20, for example a microprocessor, is able 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.
    An image I o acquired by the image sensor 16, also called a hologram, does not allow a sufficiently precise representation of the observed sample to be obtained. As described in connection with the prior art, a holographic propagation operator h can be applied to each image acquired by the image sensor, so as to calculate a quantity representative of the light wave 14 transmitted by the sample. 10, and to which the image sensor 16 is exposed. Such a method, designated by the term holographic reconstruction, makes it possible in particular to reconstruct an image of the module or of the phase of the exposure light wave 14 in a plane of reconstruction parallel to the detection plane P o , and in particular in the plane Pw 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 reconstruct a complex expression A of the light wave 14 at any point of coordinates (x, y, z) of space, and in particular in a reconstruction plane P z located at a distance z of the image sensor 16, called the reconstruction distance, this reconstruction plane preferably being the plane of the sample Pw> with:
    A (x, y, z) = l 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 of exposure of the image sensor 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 a plane, called the reconstruction plane P z , extending to a coordinate z of the plane of P o detection. 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 wave 14.
    The function of the propagation operator h 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) the light wave 14, at this distance | z |, called the reconstruction distance, with:
    M (x, y, z) = abs [A (x, y, z)] (1);
    - <p (x, y, z) = arg [X (x, y, z)] (2);
    The operators abs and arg respectively designate the module and the argument.
    The propagation operator is for example the Fresnel-Helmholtz function, such that: at (x, y, z) = - ^ e l2 Ne X p (j n ^ -}.
    In other words, the complex expression A of the light wave 14, at any point of coordinates (x, y, z) of space, is such that: 4 (x, y, z) = M (x, y , ζ) β Γφ (χ, ν , ζ > (3). It is possible to form images M z and φ ζ representing respectively the module or the phase of complex expression A in 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 inventors have developed a method for counting leukocytes in a sample, this method being described in connection with FIGS. 2A to 2G. The sample may in particular be, or include, cerebrospinal fluid taken by lumbar puncture, the leukocyte count being carried out for the diagnosis of meningitis. Indeed, as described in connection with the prior art, a concentration, exceeding a certain threshold, of leukocytes in the cerebrospinal fluid can be a signature of meningitis. It is recognized that a concentration of leukocytes per microliter constitutes a threshold concentration above which meningitis must be suspected. The main steps of the numbering method according to the invention are:
    the acquisition, by the image sensor, of an image I o of the sample in one or more spectral bands;
    determining radial coordinates of particles detected on the acquired image, the term radial signifying in a plane parallel to the detection plane; from the acquired image, the calculation of a characteristic quantity, for example the module or the phase, of the exposure light wave 14, at different distances from the sample, called reconstruction distances;
    the formation of a profile representing an evolution of the characteristic quantity as a function of the reconstruction distance, each profile being associated with a particle; from one or more profiles associated with each particle, the identification of leukocytes as particles of interest;
    the enumeration of the leukocytes thus identified, so as to estimate an amount of leukocytes in the sample.
    FIG. 2A represents a first example of a process implemented, the steps of which are described below.
    Step 100: Acquisition of an image I o of the sample 10 by the image sensor 16, this image forming a hologram. Figure 2B shows part of such an image. One of the advantages of the configuration without lens, represented in FIG. 1, is the wide field observed, making it possible to simultaneously address a high volume of sample. This allows several particles to be observed simultaneously. The field observed depends on the size of the image sensor, being slightly smaller than the detection surface of the latter, due to the spacing between the pixels of the sensor and the sample. The field observed is generally greater than 10 mm 2 , and is typically between 10 mm 2 and 50 mm 2 , which is significantly higher than with a microscope.
    Step 110: Formation of a complex image called initial A§ = 0 of the sample 10 in the detection plane Po. During this step, an initial image A§ = 0 of the sample 10 is defined, from of the image Io acquired by the image sensor 16. This step is an initialization of the iterative algorithm described later in connection with step 120, the exponent k designating the rank of each iteration. The module αθ = θ of the initial image A§ = 0 can be obtained by applying the square root operator to the image I o acquired by the image sensor, in which case αθ = θ = - / 7 /
    In this example, the acquired image I o is normalized by a term representative of the intensity of the light wave 12 incident on the sample 10. The latter can be, for example, the square root of an average I o of the image I o , in which case each pixel
    I j rχ Λ /) / 0 (% y) of the acquired image is divided by said average, so that αθ 0 = PZ— y Io
    The phase <ρθ = θ of the initial image X§ = 0 is either considered as zero in each radial coordinate (x, y), or predetermined according to an arbitrary value. Indeed, the initial image X§ = 0 results directly from the image I o acquired by the image sensor 16. However, the latter does not include any information relating to the phase of the light wave 14 transmitted by sample 10, the image sensor 16 being sensitive only to the intensity of this light wave.
    Step 120: Calculation of a complex image A re f, called the reference image, of the sample 10 in a reference plane Pref ′ this reference image is a complex image comprising phase and amplitude information of the wave light 14 to which the image sensor 16 is exposed. The reference plane is a plane advantageously perpendicular to the propagation axis Z, and / or parallel to the detection plane P o . It is preferably the plane of the sample Pw · Step 120 is carried out by applying the propagation operator h, previously described, to the initial complex image X§ = 0 . However, the application of the propagation operator to the initial complex image can result in a reference image A re f affected by a significant reconstruction noise, frequently designated by the term twin image. In order to obtain an exploitable complex reference image, by limiting the reconstruction noise, iterative algorithms can be implemented. One of these algorithms is described below.
    The complex image A re f is designated as a reference image because it serves as the basis for the formation of profiles on the basis of which the particles of the sample are characterized. FIGS. 2C and 2D respectively show an image of the module M re f and of the phase (p re f of the complex image of reference A re f.
    The coordinate z re f of the reference plane Pref is determined either a priori, in particular when the position of the sample is controlled relative to the image sensor 16, or by means of an autofocus, based on a criterion sharpness of the reference image A re f, the latter being all the more clear as the reference plane corresponds to the plane in which the particles are found. The sharpness criterion can be applied to the image of the module M re f or of the phase of the image (p re f of reference.
    Step 130: Application of a propagation operator h to the complex reference image A re ^ so as to calculate complex images A re ^ z , called secondary, along the propagation axis Z. During this step, the complex reference image A re f is propagated according to a plurality of reconstruction distances z, using a propagation operator h as defined above, so as to have a plurality of complex images , called secondary, A re f Z reconstructed at different distances z from the brief reference plane - Thus, this step includes the determination of a plurality of complex images A re f z such as: Aref, z A re y * h z with z min - z - z max The values zmjnet zmax are the minimum and maximum coordinates, along the Z axis, between which the complex reference image is propagated. Preferably, the complex images are reconstructed according to a plurality of z coordinates between the sample 14 and the image sensor 16. The inventors considered that it was preferable to obtain secondary complex images on either side of the Prey reference plane, so that zmin <zref < z max- Unlike the image acquired Io by the image sensor 16, or the initial complex image X§ = 0 , the complex reference image described the exposure light wave 14 correctly, in particular at its phase. Consequently, it is estimated that the secondary images A re ^ z , obtained by propagation of the reference image, form a good descriptor of the propagation of the exposure light wave 14 along the propagation axis Z.
    Preferably, two adjacent reconstruction planes are spaced from each other according to a fine mesh, for example between 5 pm and 50 pm, and for example 25 pm. It is a local propagation, since it is carried out at a distance between 250 μm and 2 μm on either side of the reference plane P re f, for example ± 500 μm. Based on a reconstruction according to a distance of 500 μm on either side of the reference plane Pref> and a distance between two adjacent planes of 25 μm, the complex reference image A re f is propagated according to forty planes of Pref.zi reconstruction so as to form as many secondary complex images, A re f Z. FIG. 2E illustrates a stack of images, formed by the complex image of reference A re f as well as various secondary images A re f Z obtained by local propagation of the complex image of reference A re f. This stack of images can in particular make it possible to detect the presence of particles i at each radial coordinate (step 140) and to obtain a profile representative of the module and / or of the phase of the light wave 14 along a passing propagation axis. by each particle detected (step 150).
    Step 140: detection of particles in the sample. This step aims to determine a radial position (x ^ yj of each particle i to be characterized, either by using the complex reference image A re f, or the image l 0 acquired by the image sensor 16, or by using the stack of images obtained during step 130, this last option constituting the preferred embodiment.
    For this, at each radial coordinate (x, y) of the reference plane, the maximum value as well as the minimum value of the module in the stack of complex images formed during step 130 is determined. An image of the maximum module M max , of which each coordinate Ai max (x, y) gathers the maximum values of the module, in the image stack, at the radial coordinate (x, y). An image of the minimum module M min is also formed , each coordinate of which M min (x, y) brings together the minimum values of the module, in the image stack, at the radial coordinate (x, y). From the image of the maximum module and the image of the minimum module, a differential image of the module ΔΜ is established, with AM (x, y) = M max (x, y) - M min (x, y) . Each radial coordinate (x ^ yO corresponds to the center of a particle i when the value of the differential image of the module is greater than a certain threshold, i.e. ΔΜ (Χ;, ^)> M Th .
    FIG. 4A represents a series of thumbnails, each thumbnail corresponding to the module of an image of the stack of images, in a region of interest. The thumbnails represent the same region of interest, centered on a particle. There is a significant variation in the value of the module between the different labels. Thus, in the presence of a particle i at a radial coordinate (Xi, yi), the value of the differential image, at this coordinate, is high AM (Xj, yj). The thresholding previously described makes it possible to identify the radial coordinates (Xj, yj) corresponding to a particle, and in particular designating the center of each particle.
    Based on the phase of each image in the image stack, we can similarly form an image of the maximum phase (p max and an image of the minimum phase (p m i n · We then establish an image phase differential Δφ, with Aç (x, y) = (p max (x, y) - < / 'min (x, y) · Each radial coordinate (Xj, yj) corresponds to the center of a particle i when the value of the phase differential image is greater than a certain threshold, ie Aç (Xj, yj)><p Th . FIG. 4B represents a series of thumbnails, each thumbnail representing the phase of an image in the image stack, in a region of interest corresponding to the same particle as in FIG. 4A. In the same way as for the module, we observe, in the presence of a particle, a variation significant of the value of the phase between the different vignettes.
    Thus the radial position of each particle is determined by retaining the points of the differential image of the module ΔΜ or of the differential image of the phase Δφ whose values are greater than the predetermined thresholds. It is also possible to apply a criterion of minimum distance between two adjacent radial positions, in order to avoid that two radial positions which are too close to each other do not correspond to the same particle. At the end of step 140, the radial position of each particle is available as well as a number of detected particles.
    According to a variant, the detection of the radial position (Xi, yd of each particle is carried out by considering an image of the stack of images, whether it is the reference image A re f obtained during the step 120 or of an image A re f z of the stack of images. On the image of the module or of the phase of the image considered, each particle takes the form of a spot, as can be seen on 2C and 2D The application of image processing methods makes it possible to determine the coordinates of the centroid of each spot, the latter forming the different radial positions (% j, yj) to be considered.
    Step 150: Formation of a profile associated with each particle. From the reference complex image A re ç and each secondary complex image A re ^ z we estimate a quantity characteristic of the light wave 14, at each radial position (% j, yj) previously selected during the step 140, and at a plurality of distances z for reconstruction of the reference plane Pref (or of the detection plane P o ), then a profile is created representing an evolution of the characteristic quantity as a function of z, along the propagation axis Z. The characteristic quantity can in particular be established from the modulus and the phase of each particle. FIGS. 2F and 2G respectively represent a profile of the module M (z) and a profile of the phase φ (ζ), at the same radial position (% j, yj), each profile being obtained from the images of the stack d images formed during step 130, by interpolating between the coordinates of two adjacent images.
    Etapel60: Identification of each particle from the profiles formed during step 150. Preferably, we have a database of standard profiles formed during a learning phase using samples known standards. Identification is then carried out by a comparison or classification of the profile associated with each particle, based on standard profiles. The identification makes it possible to determine whether each particle corresponds to a so-called particle of interest, which one wishes to count.
    Step 170: Counting. The particles of interest identified during the previous step are counted. This makes it possible to estimate an amount (number or concentration) of particles of interest in the sample.
    The inventors have noted that it was preferable for step 140 to be implemented on the basis of a reference image Α νβ [· (Δλ Ύ ) reconstructed from an image / ο / Δλ-Ο acquired by the image sensor in a first spectral band (Δλ-J between 400 nm and 550 nm, and preferably between 400 nm and 500 nm which corresponds to the blue wavelengths of the visible spectrum. In such a spectral band, the particles are more distinguished from the background of the image, whether it is the module image or the phase image, which allows better particle detection and more precise localization. from each radial position, especially when the particles are leukocytes or erythrocytes. They also found that step 150 can give better results based on a reference image Δ Γ6 ^ (Δλ 2 ) reconstructed from 'an image / 0 (Δλ 2 ) acquired se by the detector in a second spectral band (Δλ 2 ) between 600 nm and 700 nm, which corresponds to the red wavelengths of the visible spectrum.
    Thus, according to a preferred embodiment, step 140 is carried out using a complex reference image calculated on the basis of an image / o / AA-Jacquise by the image sensor in a first spectral band Δλ χ while that step 150 is implemented using a complex reference image Δ Γ6 ^ (Δλ 2 ) calculated on the basis of an image / 0 (AÀ 2 ) acquired by the image sensor in a second spectral band Δλ 2 , different from the first spectral band. In this example, the first spectral band Δλ χ is between 400 and 550 nm, and the second spectral band Δλ 2 is between 600 nm and 700 nm. This combination of spectral bands has proved to be particularly suitable for the observation of blood cells of the leukocyte or erythrocyte type. This embodiment is described in connection with FIG. 2H.
    One of the important points of this algorithm is the step 120 of forming the complex reference image A re f from an image l 0 acquired by the image sensor 16. This complex image is formed from of an iterative algorithm, each iteration comprising a propagation of a complex image representative of the sample from the detection plane P o to the reference plane, in this case the plane of the sample Pw> then an update the value of the phase of each pixel of the complex image of the sample 10 in the detection plane
    P o . Several algorithms are potentially usable. It is important that the algorithm has good reconstruction performance, the reconstructed image having a good signal-to-noise ratio, while being simple to implement, without requiring an excessive number of acquisitions, and without moving the source. of light compared to the sample.
    It is conceivable to obtain a complex reference image by a simple application of the propagation operator to the image I o acquired by the image sensor. However, this gives rise to a reconstruction affected by significant noise, as previously mentioned.
    According to a first preferred possibility, the sample is illuminated successively or simultaneously in different spectral bands AA, and in the detection plane P o , an image / 0 (ΔΛ) representative of each spectral band is acquired. The algorithm makes it possible to obtain a complex image 4 re ^ (AA) of the sample 10, in the reference plane, in each spectral band Δλ. The complex images thus obtained can be combined, for example by averaging, in each pixel, their modulus and their phase, which makes it possible to form the reference image A re f. Alternatively, the complex reference image is a complex image 4 re ^ (AA) in a spectral band ΔΛ. This algorithm has been described in the publication SNA Morel, A. Delon, P. Blandin, T. Bordy, O. Cioni, L. Hervé, C. Fromentin, J. Dinten, and C. Allier, Wide-Field Lensfree Imaging of Tissue Slides, in Advanced Microscopy Techniques IV; and Neurophotonics II, E. Beaurepaire, P. So, F. Pavone, and E. Hillman, eds., Vol. 9536 of SPIE Proceedings (Optical Society of America, 2015) as well as in patent application FR1554811 filed on May 28, 2015, and more specifically in steps 100 to 500 described in this application. It has been shown that the use of two or three different spectral bands makes it possible to obtain a good reconstruction performance.
    Another preferred possibility is to reconstruct a complex reference image based on an image acquired from the sample when the latter is illuminated in a single spectral band Δλ. The complex reference image can be obtained using an algorithm as described in patent application FR1652500 filed on March 23, 2016. According to this second algorithm, the main steps in the formation of the complex reference image are described below. after, in connection with FIGS. 3A and 3B. This algorithm is based on a single acquisition, which facilitates its implementation. It is an iterative algorithm, the steps 120 to 125 described below being reiterated, k designating the rank of an iteration.
    Step 121: propagation from the detection plane to the reference plane
    During this step, there is an image formed in the detection plane P o . During the first iteration, it is the initial image X§ = 0 , described in connection with step 110. During the other iterations, it is the image X§ _1 resulting from the previous iteration. The image formed in the detection plane Po is propagated in a reference plane Pref, by the application of a propagation operator h as previously described, so as to obtain a complex image X k ey, representative of the sample 10, in the reference plane P re f. The propagation is carried out by convolution of the image X§ _1 by the propagation operator h_ zre f, so that:
    s ^ ref Λ 0 * '''—zreff
    The index -zref 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 k (x, y) associated with each pixel of a plurality of pixels (x, y) of the complex image and preferably in each of its pixels is calculated. This quantity depends on the value X k e ^ (x, y) of the image A k ef, 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 X k ej ·, in each pixel, and the value 1. Such a quantity can be obtained according to the expression:
    £ fe (%, y) = J (A k ef (x, y) - l) (ri k e / (x, y) - l) * = | zi k e / (x, y) - l |
    Step 123: establishment of a noise indicator associated with the image X k ej ·.
    During step 122, magnitudes £ k (x, y) are calculated in several pixels of the complex image Az. 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 this step, an indicator, called noise indicator, is calculated from a standard of the vector E k . In general, an order is associated with a norm, so that the norm | x | p of order p of a vector x of dimension n with coordinates x n,) is such that: | x | | p = (Σ = ιI x ll p ) 1 / p , with p> 0. In the present case, we use a standard of order 1, in other words p = 1. During this step, the quantity f fe (x, y) calculated from the complex image 4 ^, 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, AV / Uy)
    An important aspect of this step consists in determining, in the detection plane P o , phase values ç / fx, 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 + f 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 light wave 14, but not on its phase. The first reconstructed image A k / d in the reconstruction plane Pref 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 <ρθ (x, y) in the detection plane Po, so as to progressively minimize the indicator 8 k .
    The image 4§ 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 160 aim to establish, iteratively, the value of the phase <ρθ (x, y) of each pixel of the image A k , minimizing the indicator s k , the latter being obtained on the image A k e ^ obtained by propagation of the image 4§ _1 in the Pref reference plane. The minimization algorithm can be a gradient descent, or conjugate gradient descent, 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 4§ so as to minimize the indicator 8 k + 1 resulting from a propagation of the image complex A k in the Pref reference plane, during the next iteration k + 1. For this, a phase vector is established, each term of which is the phase <p k (x, ÿ) of a pixel (x, y) 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:
    Ç'o (^ y) = <Po _1 (Xy) + a k p k (x, ÿ) 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) forming a direction of the gradient Vf fe of the indicator f fe .
    This equation can be expressed in vector form, as follows:
    Ψο = <Po 1 + œ k p k
    We can show that:
    pk _ _y £ 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
    Vf (/) = df * = Iml r ').
    (V -1)
    A k -1 V 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:
    Vf w .Vf wp Vf ^ .Vf ^
    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 values of the phase 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 and in the reference plane PrefEtape 126: Obtaining the complex reference image .
    At the end of the last iteration, the method can comprise a propagation of the complex image A * resulting from the last iteration in the reference plane Pref 'so as to obtain a complex image of reference A re f = A ge f . Alternatively, the complex reference image A re f is the complex image Ajÿ resulting from the last iteration in the reference plane Pref According to one embodiment, each profile is obtained by propagating an image I o acquired by the sensor d image at different reconstruction distances z, which gives rise to as many reference images A re f as reconstructed distances. We then obtain a stack of complex reference images. However, such an embodiment supposes a precise estimation of the complex amplitude of the light wave of exposure 14 at the different reconstruction distances z. The inventors estimated that it was preferable to reconstruct, in a rigorous manner, a reference image A re f in a reference plane Pref From this complex reference image A re f, the profiles are obtained applying a simple digital propagation , by considering different reconstruction distances, to the complex reference image, so as to obtain the so-called secondary complex images A re ^ z
    Experimental tests.
    FIGS. 5A to 5D represent experimental profiles obtained on leukocytes 10b and erythrocytes 10a immersed in cerebrospinal fluid. The experimental conditions are as follows:
    sample 10: cerebrospinal fluid contained in a Countess® fluid chamber, the volume examined reaching 3 mm 3 , ie 3 μΙ.
    light source 11: light emitting diode Created MC-E Color, comprising three light emitting diodes that can be simultaneously or successively activated, each diode emitting respectively in the following spectral bands Δλ: 450nm - 465 nm; 520nm - 535nm; 620nm - 630nm;
    image sensor: 3840 x 2748 pixel monochrome CMOS sensor, each pixel measuring 1.67 μιτι aside, the detection surface extending over approximately 30 mm 2 ; distance D between the light source 11 and the sample 10: 5 cm;
    distance d between the sample 10 and the image sensor 16: 2000 μιτι; thickness e of the fluid chamber 15: 100 μιτι; diameter of the opening of the spatial filter 18: 150 μιτι;
    The sensor being monochrome, images of the sample are acquired by activating one of the light-emitting diodes composing the light source 11, so as to acquire an image representative of the spectral band Δλ of the activated diode.
    The method described in connection with FIG. 2A has been implemented, the complex reference image being obtained as described in connection with FIGS. 3A and 3B.
    FIGS. 5A and 5B respectively represent profiles of the module and of phase obtained according to generatrices parallel to the axis of propagation Z and passing through leukocytes (10b) or erythrocytes (10a), the sample being illuminated in the spectral band 620nm - 630nm, which corresponds to illumination in the red. In this example, as shown in FIG. 2E, the reference plane P re f corresponds to the plane of the sample Pw> located at a distance of 2000 μιτι from the detection plane P o . Note that the presence of an erythrocyte results in variations in the phase and the modulus on either side of the plane of the sample, the latter corresponding to the coordinate z = 0. For most particles, qu '' it is a erythrocyte or a leukocyte, the module takes a minimum value at a distance between 0 and 100 μιτι from the plane of the sample Pw · However, for some leukocytes, this minimum value is reached at a distance greater than 100 μιτι from the plane of the sample.
    Figures 5C and 5D are similar to Figures 5A and 5B, the sample being illuminated in the spectral band 450 nm - 465 nm, which corresponds to blue. As previously indicated, the inventors have considered that illumination in a red spectral band, typically between 500 nm and 600 nm, allows better discrimination between leukocytes and erythrocytes.
    The profile of the module or of the phase of the exposure light wave 14, along the propagation axis Z, makes it possible to identify the erythrocytes 10a and the leukocytes 10b, each particle having a typical profile, both at the level of the only phase of the module. This property has been applied to the count of 10b leukocytes in cerebrospinal fluid to aid in the diagnosis of meningitis. As indicated in connection with the prior art, it is known that in the presence of a concentration of leukocytes greater than 10 cells per μl, bacterial meningitis must be suspected.
    The inventors analyzed, with the device and the method described above, 215 samples of cerebrospinal fluid taken, each sample being taken from a different person. Of these 215 samples:
    correspond to a case of meningitis;
    correspond to cases of cancer (glyoma, medulloblastoma or carcinoma);
    correspond to hemorrhages;
    correspond to an autoimmune disease;
    150 are negative samples, no particular pathology being associated therewith, including 17 having a particularly high erythrocyte concentration, due to a trauma which occurred during the lumbar puncture.
    The method described in connection with FIG. 2H was applied to each sample, the complex reference image being obtained according to the method described in connection with FIGS. 3A and 3B. The sample was illuminated according to the spectral band [450nm - 465 nm] to determine the radial position (Xj, yi) of the center of each cell, then according to the spectral band [620 nm - 630 nm] to acquire an image of the sample based on which the complex reference image is obtained. From the complex reference image, profiles of the module or of the phase were obtained in a spatial interval of 500 μm on either side of the plane of the sample. 40 complex images were reconstructed, at z coordinates spaced 25 µm apart. From these images, the profile and phase values were obtained at each reconstruction height and then interpolated so as to have continuous profiles.
    First of all, a first so-called initial threshold was applied, so that the profiles which do not cross an initial threshold are not considered subsequently. This makes it possible to exclude irrelevant particles, such as dust. The value of the initial threshold can be 0.1 when the profile is a phase profile.
    In order to discriminate leukocytes from erythrocytes, a high threshold Th h and a low threshold Thi were applied to the profiles representing the evolution of the phase. These thresholds are shown in Figure 5B. The phase profiles of each particle were determined, and those crossing the high threshold Th h and / or the low threshold Thi were considered to be representative of a leukocyte. The value of the high threshold and the low threshold was +1.38 rad and -1.38 rad respectively. Thus, a first identification of leukocytes was based on the basis of a thresholding of the phase profile associated with each particle.
    A second classification was based on the position of the minimum value of the module. When the latter is located at a distance greater than 100 μm from the plane of the sample R 10> the particle associated with the profile is considered to be a leukocyte. The distance of 100 μm is shown by a double arrow in FIG. 5A. This second classification was applied to the profiles associated with particles not considered to be leukocytes following the first classification.
    The classifications indicated above prove to be correct, but other types of classifications, based on profiles obtained from the module or the phase, or combining the module and the phase are possible.
    A possible classification of profiles is based on the calculation of the area of a surface extending between the profile and a line connecting two remarkable points of the profile. A first remarkable point PI is for example the point at which the profile reaches its minimum value. A second remarkable point P2 is a point at which the profile takes a predetermined value at a reconstruction distance z greater than that for which the minimum value is obtained. FIG. 6A represents the area between a profile and a line extending between the first remarkable point PI and the second remarkable point P2. FIG. 6B shows values of such an area for profiles established on erythrocytes 10a and on leukocytes 10b. To establish these areas, we considered:
    for the first remarkable point PI, the minimum value of each profile, for the second remarkable point P2, the point reaching a percentage of the minimum value of the profile, at a reconstruction distance z greater than that corresponding to the first remarkable point PI.
    It is observed that the calculated area is significantly higher on leukocytes than for erythrocytes. Such an area constitutes a reliable metric for the classification of detected particles.
    Furthermore, the identification of particles of interest can be obtained by applying, to the profile associated with each particle, conventional classification methods of the Principal Component Analysis type. These classification methods are based on standard profiles obtained by calibrations using calibration samples containing known particles.
    FIG. 7 represents the experimental results obtained using a classification of the profiles illustrated in connection with FIGS. 5A and 5B. The abscissa and ordinate axes respectively represent the concentrations of erythrocytes and leukocytes determined in each sample. The horizontal line L1, of equation y = 10 represents the threshold concentration of 10 leukocytes per μΙ. The proven cases of meningitis appear in the form of gray discs, the other cases appearing in the form of spots (hemorrhages, cancer, trauma, autoimmune disease) or circles (negative cases). With such an identification, 57 positive cases were determined, including the 15 cases of meningitis (true positives) symbolized by the discs, and 42 false positives. We note that this classification does not generate false negatives, which indicates a high sensitivity. In the legend of this figure, the bacteria causing meningitis have been indicated.
    The number of false positives can be reduced by applying another threshold, based on a ratio between the leukocyte and erythrocyte concentrations. When this ratio exceeds a certain threshold, for example 1/200, the sample is considered to be representative of a hemorrhage. This second threshold is represented, in FIG. 7, by the line L2 whose equation is y = Taking this second threshold into account makes it possible to eliminate 8 false positives, which increases the specificity of the process, without impact on sensitivity. The two lines L1 and L2 delimit a half-space, corresponding to a concentration of leukocytes and erythrocytes for which meningitis can be suspected.
    During this test, samples were analyzed according to a conventional method, under a microscope. 2 positive samples, surrounded by a dotted line in Figure 7, were not considered positive. These first tests tend to show that the sensitivity of the method could be higher than the conventional measurement under the microscope.
    The method described above makes it possible to analyze simultaneously, that is to say from the same image, several microliters of cerebrospinal fluid, to establish a concentration of leukocytes, or even erythrocytes, and to perform a analysis of samples with good sensitivity towards a pathology, in this case meningitis, with a view to diagnosis. It relies on the implementation of inexpensive equipment, simple and easy to implement. It can in particular be automated, which makes it possible to envisage analyzes close to the sampling point, according to so-called point of care devices. In addition, the method makes it possible to obtain a result in a few minutes.
    The method described above could be used for the detection of cells as an aid in the diagnosis of other pathologies. The body fluid can thus be urine or lymph or blood, in particular diluted blood, synovial fluid.
    权利要求:
    Claims (17)
    [1" id="c-fr-0001]
    1. Method for determining an amount of leukocytes (10b) contained in a sample (10), the sample comprising particles (10a, 10b) and extending along a plane, called the plane of the sample (P 10 ), the process comprising the following steps:
    a) illumination of the sample using a light source (11), the light source emitting an incident light wave (12) propagating towards the sample (10) along a propagation axis (Z) ;
    b) acquisition, using an image sensor (16), of an image (/ 0 ) 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 exposure, to which the image sensor (16) is exposed under the effect of illumination;
    the method being characterized in that it also comprises the following steps:
    c) from the image acquired (/ 0 ) during step b), application of a propagation operator (h), so as to calculate a complex image (ri re y), called the reference image, representative of the sample, in a reference plane (P re f);
    d) determination of radial positions (x ,, yj of several particles in a plane parallel to the detection plane (P o ), each radial position being associated with a particle;
    e) calculation of at least one characteristic quantity (Μ, φ) of the exposure light wave (14), at each radial position (x ^ yj, and at a plurality of distances (z) from the detection plane ( P o );
    f) formation of a profile (M (z), <p (z)), representing an evolution of the characteristic quantity calculated during step e) along an axis parallel to the axis of propagation (Z) and passing by each radial position (Xj, yj) determined during step d), each profile being associated with a particle;
    g) as a function of each profile formed during step f), identification of the leukocytes;
    h) determination of an amount of leukocytes in the sample from a number of leukocytes identified during step g).
    [2" id="c-fr-0002]
    2. Method according to claim 1, wherein during step c), the reference plane (P re f) is the plane of the sample (P 10 ).
    [3" id="c-fr-0003]
    3. Method according to any one of the preceding claims, in which during step e), the characteristic quantity comprises the module (M) or the phase (φ) of a complex expression (X) of the light wave exposure (14).
    [4" id="c-fr-0004]
    4. Method according to any one of the preceding claims, in which step d) comprises the following substeps:
    dl) application of a propagation operator (h) to the reference image (4 re ^), so as to obtain complex images, called secondary complex images (4 re y z ), at different distances (z) from the reference plane (P re f) along the propagation axis (Z), the secondary complex images and the reference image forming a stack of complex images;
    d2) determination of a radial position (% j, yj) of particles from the images of the complex image stack obtained during the sub-step dl).
    [5" id="c-fr-0005]
    5. Method according to claim 4, in which the radial position (% j, yj) of particles is obtained by forming:
    a first image (M max , Pmax) of which each point represents a maximum value, at said point and along the axis of propagation (Z), of a module or of a phase of the images of the stack of complex images ;
    a second image (M min , (p m in) of which each point represents a minimum value, at said point and along the axis of propagation (Z), of a module or of a phase of the images of the stack d 'complex images;
    a differential image (ΔΜ, Δφ) representing a difference between the first image and the second image;
    the radial position (x ^ yj of each particle being obtained by applying a threshold to the differential image.
    [6" id="c-fr-0006]
    6. Method according to any one of the preceding claims, in which step e) comprises the following substeps:
    el) application of a propagation operator (h) to the reference image (4 re ^), so as to obtain complex images, called secondary complex images (4 re ^ z ), at different distances (z) from reference plane (P re f) along the propagation axis (Z), the secondary complex images and the complex reference image forming a stack of complex images;
    e2) determination of the module or of the phase of the exposure light wave (14) the image sensor (16) from images of the stack of complex images obtained during sub-step e1).
    [7" id="c-fr-0007]
    7. Method according to any one of the preceding claims, in which step g) comprises the application of a thresholding to each profile (Ai (z), φ (ζ)}, a profile crossing a threshold (Th h , Th t ) predetermined being considered as representative of a leukocyte.
    [8" id="c-fr-0008]
    8. The method of claim 7, wherein each profile represents the evolution of the phase of the exposure light wave (14), along the axis of propagation.
    [9" id="c-fr-0009]
    9. Method according to any one of the preceding claims, in which step g) comprises the determination of a position, along the propagation axis (Z), of a maximum or minimum value of profiles, so that that a profile for which said maximum or minimum value extends beyond a threshold distance from the plane of the sample (P 10 ) is considered to be representative of a leukocyte.
    [10" id="c-fr-0010]
    10. Method according to any one of the preceding claims, in which step g) comprises:
    - the selection, on the profile, of a first remarkable point (PI) and a second remarkable point (P2);
    - the calculation of an area extending between the profile and a line segment joining the two remarkable points.
    [11" id="c-fr-0011]
    11. The method of claim 10, in which the first remarkable point is the point at which the profile takes a minimum value or a maximum value, the second remarkable point being a point at which the profile takes a predetermined value.
    [12" id="c-fr-0012]
    12. Method according to any one of the preceding claims, in which:
    step a) comprises an illumination of the sample according to two spectral bands (Δλ 1; Δλ 2 ) distinct from each other;
    step b) comprises an acquisition of an image of the sample (/ qCAÀ-l), / 0 (Δλ 2 )) in each of the two spectral bands;
    step d) is implemented on the basis of the acquired image (/ qCAÀ-l)), during step b), in the first spectral band (AA-J;
    step e) is implemented on the basis of the acquired image (/ 0 (Δλ 2 )), during step b), in the second spectral band (Δλ 2 ).
    [13" id="c-fr-0013]
    13. The method of claim 12, wherein the first spectral band (ΔΑ 1 ) is between 400 nm and 550 nm, and wherein the second spectral band (Δλ 2 ) is between 500 nm and 600 nm.
    [14" id="c-fr-0014]
    14. Method according to any one of the preceding claims, comprising the steps: gbi S ) classification of each particle, the radial position of which was identified during step d), as being, or not, an erythrocyte (10a) as a function of each profile formed during step f);
    hbis) determination of an amount of erythrocytes (10a) in the sample as a function of the classifications carried out during step gbis);
    i) determination of a ratio between the quantity of leukocytes (10b) determined during step h) and the quantity of erythrocytes (10a) determined during step hbis).
    [15" id="c-fr-0015]
    15. Method according to any one of the preceding claims, in which there is no magnification optic between the sample (10) and the image sensor (16).
    [16" id="c-fr-0016]
    16. Device for counting leukocytes (10b) contained in a sample (10), the device comprising:
    - a light source (11) capable of emitting an incident light wave (12) propagating towards the sample (10), along a propagation axis (Z);
    - a support (10s), configured to hold the sample (10) between said light source (11) and an image sensor (16), the image sensor extending along a detection plane (P o );
    a processor (20), configured to receive an image of the sample acquired by the image sensor (16) and to implement steps c) to h) of the method which is the subject of any one of claims 1 to 15 .
    [17" id="c-fr-0017]
    17. Device according to claim 16, in which no magnification optics are arranged between the sample (10) and the image sensor (16) when the sample (10) is held on the support (10s).
    1/7
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    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US20140327944A1|2011-12-02|2014-11-06|Csir|Hologram processing method and system|
    WO2015015023A1|2013-08-02|2015-02-05|Universitat De València|Holographic reconstruction method using lensless in-line microscopy with multiple wavelengths, holographic lensless in-line microscope using multiple wavelengths and computer program|CN110794660A|2018-08-02|2020-02-14|河北工程大学|Image recording system and image classification method|GB0701201D0|2007-01-22|2007-02-28|Cancer Rec Tech Ltd|Cell mapping and tracking|
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    US8842901B2|2010-12-14|2014-09-23|The Regents Of The University Of California|Compact automated semen analysis platform using lens-free on-chip microscopy|
    FR3034196B1|2015-03-24|2019-05-31|Commissariat A L'energie Atomique Et Aux Energies Alternatives|PARTICLE ANALYSIS METHOD|FR3086758B1|2018-09-28|2020-10-02|Commissariat Energie Atomique|METHOD AND DEVICE FOR OBSERVING A SAMPLE UNDER AMBIENT LIGHT|
    FR3097050B1|2019-06-07|2021-08-27|Centre Nat Rech Scient|Method and device for analyzing a sample, using a resonant support.|
    FR3097639B1|2019-06-22|2021-07-02|Commissariat Energie Atomique|Holographic reconstruction process|
    法律状态:
    2017-09-29| PLFP| Fee payment|Year of fee payment: 2 |
    2018-03-30| PLSC| Publication of the preliminary search report|Effective date: 20180330 |
    2018-09-28| PLFP| Fee payment|Year of fee payment: 3 |
    2019-09-30| PLFP| Fee payment|Year of fee payment: 4 |
    2020-09-30| PLFP| Fee payment|Year of fee payment: 5 |
    2021-09-30| PLFP| Fee payment|Year of fee payment: 6 |
    优先权:
    申请号 | 申请日 | 专利标题
    FR1659161A|FR3056749B1|2016-09-28|2016-09-28|METHOD FOR NUMBERING LEUKOCYTES IN A SAMPLE|
    FR1659161|2016-09-28|FR1659161A| FR3056749B1|2016-09-28|2016-09-28|METHOD FOR NUMBERING LEUKOCYTES IN A SAMPLE|
    EP17783940.4A| EP3520022A1|2016-09-28|2017-09-26|Method for counting white blood cells in a sample|
    PCT/FR2017/052591| WO2018060589A1|2016-09-28|2017-09-26|Method for counting white blood cells in a sample|
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