Artificially generated color blood smear image

Abstract

Apparatus and methods for use with blood samples are described. Using a microscope (24), three images of a microscopic imaging field of a blood sample are acquired, each of the images acquired using a respective different imaging condition, and a first of the three images acquired under violet light brightfield imaging. Using at least one computer processor (28), an artificial color microscopic image of the microscopic imaging field is generated by mapping the first of the three images to a red color channel of the artificial color microscopic image, mapping a second of the three images to a second color channel of the artificial color microscopic image, and mapping a third of the three images to a third color channel of the artificial color microscopic image. Other applications are also described.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 62 / 946,988, filed December 12, 2019, by Halperin et al., entitled “Artificial generation of a color blood smear image,” which is incorporated herein by reference.

[0003] Field of the Invention

[0004] Some applications of the presently disclosed subject matter relate generally to analysis of body samples, and particularly to densitometric and microscopic measurements performed on blood samples.

[0005] background

[0006] In some optically based methods (e.g., diagnostic and / or analytical methods), properties of biological samples such as blood samples are determined by performing optical measurements. For example, the density of a component (e.g., the number of components per unit volume) can be determined by counting the components within a microscopic image. Similarly, the concentration and / or density of a component can be measured by performing light absorption, transmission, fluorescence, and / or luminescence measurements on the sample. Typically, the sample is placed in a sample carrier, and measurements are performed on a portion of the sample contained in the chamber of the sample carrier. The measurements performed on a portion of the sample contained in the chamber of the sample carrier are analyzed to determine the properties of the sample.

[0007] Implementation Plan Overview

[0008] According to some applications of the present invention, more than one image of the microscopic imaging field of view of a blood sample is obtained, each of the images being obtained using different imaging conditions. Typically, at least one of the images is a bright field image obtained under violet illumination conditions (e.g., under illumination with light of a wavelength in the range of 400nm-450nm). In addition, typically, at least one of the images is a fluorescence image. A computer processor combines data from each of the more than one images to generate an artificial color microscopic image of the microscopic imaging field of view that looks like a color smear image. For some applications, the computer processor runs a neural network to combine the images to generate an artificial color microscopic image of the microscopic imaging field of view that looks like a color smear image. Typically, one or more color models, such as RGB, CIE, HSV and / or a combination thereof, are used to generate the artificial color microscopic image.

[0009] Typically, the image acquired under brightfield violet light illumination conditions is mapped to the red color channel of the artificial color microscopic image. Further, typically, the image is converted to a negative contrast image prior to being mapped to the red color channel. For some applications, the result of mapping to the negative contrast image of the image acquired under brightfield violet light illumination conditions is that the red blood cells have an appearance similar to the appearance of red blood cells in a color smear image (e.g., similar to the appearance produced using Giemsa or Wright-Romanowsky smear staining).

[0010] For some applications, three images are acquired under respective imaging modalities. For example, in addition to the image acquired under brightfield violet light illumination conditions, two fluorescence images can be acquired. For example, the two fluorescence images can be acquired after exciting the blood sample with light of respective wavelength bands (e.g., UV light and blue light). Alternatively, the two fluorescence images can be acquired after exciting the sample with light of the same wavelength band but using respective different emission filters. Typically, the second image is mapped to a second color channel of the artificial color microscopic image, and the third image is mapped to a third color channel of the artificial color microscopic image. For example, when using an RGB color model, the first image can be mapped to the red color channel (as described above), the second image can be mapped to the green color channel, and the third image can be mapped to the blue color channel.

[0011] Accordingly, according to some applications of the present application, there is provided a method for use in processing a blood sample, the method comprising:

[0012] acquiring, using a microscope, three images of a microscopic imaging field of the blood sample, each of the images being acquired using a respective different imaging condition, and a first one of the three images being acquired under violet light brightfield imaging; and

[0013] generating, using at least one computer processor, an artificial color microscopic image of the microscopic imaging field by:

[0014] mapping the first one of the three images to a red color channel of the artificial color microscopic image;

[0015] mapping a second one of the three images to a second color channel of the artificial color microscopic image; and

[0016] mapping a third one of the three images to a third color channel of the artificial color microscopic image.

[0017] In some applications, wherein the first one of the three images is an image acquired under out-of-focus, violet light brightfield imaging conditions.

[0018] In some applications, generating the artificial color microscopic image of the microscopic imaging field includes using a neural network to generate the artificial color microscopic image of the microscopic imaging field.

[0019] In some applications, generating the artificial color microscopic image of the microscopic imaging field includes using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

[0020] In some applications, mapping the first one of the three images to a red channel of the artificial RGB microscopic image includes generating a negative contrast image of the first one of the three images, and mapping the negative contrast image to the red channel of the artificial RGB microscopic image.

[0021] According to some applications of the present invention, there is also provided an apparatus for use with a blood sample, the apparatus comprising:

[0022] a microscope configured to acquire three images of a microscopic imaging field of the blood sample, each one of the images acquired using a respective different imaging condition, and a first one of the three images acquired under violet light brightfield imaging;

[0023] an output device; and

[0024] at least one computer processor configured to generate, on the output device, an artificial color microscopic image of the microscopic imaging field by:

[0025] mapping a first one of the three images to a red channel of the artificial color microscopic image,

[0026] mapping a second one of the three images to a second color channel of the artificial color microscopic image, and

[0027] mapping a third one of the three images to a third color channel of the artificial color microscopic image.

[0028] In some applications, wherein the microscope is configured to acquire the first one of the three images is an image acquired under out-of-focus, violet light brightfield imaging condition.

[0029] In some applications, wherein the computer processor is configured to generate the artificial color microscopic image of the microscopic imaging field using a neural network.

[0030] In some applications, wherein the computer processor is configured to generate the artificial color microscopic image of the microscopic imaging field using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

[0031] In some applications, wherein the computer processor is configured to map the first one of the three images to a red color channel of the artificial RGB microscopic image by generating a negative contrast image of the first one of the three images and mapping the negative contrast image to the red color channel of the artificial RGB microscopic image.

[0032] According to some applications of the present application, there is also provided a method for use with a blood sample, the method comprising:

[0033] acquiring, using a microscope, more than one image of a microscopic imaging field of the blood sample, each of the images acquired using a respective different imaging condition; and

[0034] combining, using at least one computer processor, data from each of the more than one images so as to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image.

[0035] In some applications, wherein combining data from each of the more than one images so as to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image comprises using a neural network to combine data from each of the more than one images so as to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image.

[0036] In some applications, wherein combining data from each of the more than one images so as to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image comprises using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

[0037] According to some applications of the present application, there is also provided an apparatus for use with a blood sample, the apparatus comprising:

[0038] a microscope configured to acquire more than one image of a microscopic imaging field of the blood sample, each of the images acquired using a respective different imaging condition;

[0039] an output device; and

[0040] at least one computer processor configured to combine data from each of the more than one images so as to generate, on the output device, an artificial color microscopic image of the microscopic imaging field that looks like a color smear image.

[0041] In some applications, the computer processor is configured to combine data from each of the more than one image to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

[0042] In some applications, the computer processor is configured to combine data from each of the more than one image to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

[0043] According to some applications of the invention, there is also provided a method for use with a blood sample, the method comprising:

[0044] acquiring, using a microscope, three images of a microscopic imaging field of the blood sample, each of the images acquired using a respective different imaging condition; and

[0045] generating, using at least one computer processor, an artificial color microscopic image of the microscopic imaging field by:

[0046] generating a normalized version of each of the images so as to remove pixels within the images having an intensity below a threshold; and

[0047] mapping the normalized version of each of the images to a respective different channel within an additive color model.

[0048] In some applications, generating the artificial color microscopic image of the microscopic imaging field includes using a neural network to generate the artificial color microscopic image of the microscopic imaging field.

[0049] In some applications, generating the artificial color microscopic image of the microscopic imaging field includes using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

[0050] In some applications, wherein for at least one of the images, generating a normalized version of each of the images includes:

[0051] determining a maximum intensity within the image; and

[0052] removing all pixels having an intensity less than half of the maximum intensity.

[0053] In some applications, wherein for at least one of the images, generating a normalized version of each of the images further includes:

[0054] generating an intensity histogram of the image; and

[0055] for each pixel within the image having an intensity at least equal to half the maximum intensity:

[0056] identifying in the intensity histogram a nearest local maximum having an intensity greater than half the maximum intensity within the image; and

[0057] normalizing the intensity of the pixel based on the difference between the maximum intensity and the intensity of the local maximum.

[0058] According to some applications of the application, there is also provided an apparatus for use with a blood sample, the apparatus comprising:

[0059] a microscope configured to acquire three images of a microscopic imaging field of the blood sample, each of the images acquired using a respective different imaging condition;

[0060] an output device; and

[0061] at least one computer processor configured to generate on the output device an artificial color microscopic image of the microscopic imaging field by:

[0062] generating a normalized version of each of the images so as to remove pixels within the images having an intensity below a threshold, and

[0063] mapping the normalized version of each of the images to a respective different channel within an additive color model.

[0064] In some applications, wherein the computer processor is configured to generate the artificial color microscopic image of the microscopic imaging field using a neural network.

[0065] In some applications, wherein the computer processor is configured to generate the artificial color microscopic image of the microscopic imaging field using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

[0066] In some applications, wherein the computer processor is configured to generate the normalized version of each of the images by, for at least one of the images:

[0067] determining a maximum intensity within the image; and

[0068] removing all pixels having an intensity less than half the maximum intensity.

[0069] In some applications, wherein the computer processor is configured to generate, for at least one of the images, a normalized version of each of the images by:

[0070] generating an intensity histogram of the image; and

[0071] for each pixel within the image having an intensity at least equal to one-half of the maximum intensity:

[0072] identifying, in the intensity histogram, a nearest local maximum having an intensity greater than one-half of the maximum intensity within the image; and

[0073] normalizing the intensity of the pixel based on a difference between the maximum intensity and the intensity of the local maximum.

[0074] According to some applications of the present application, there is also provided a method for use with a blood sample, the method comprising:

[0075] acquiring, using a microscope, three images of a microscopic imaging field of the blood sample, each of the images acquired using a respective different imaging condition; and

[0076] generating, using at least one computer processor, an artificial color microscopic image of the microscopic imaging field by:

[0077] mapping each of the images to a respective different channel within an additive color model to generate an initial color image; and

[0078] generating a normalized version of the initial color image so as to remove pixels within the image having an intensity below a threshold value.

[0079] In some applications, wherein generating the artificial color microscopic image of the microscopic imaging field comprises using a neural network to generate the artificial color microscopic image of the microscopic imaging field.

[0080] In some applications, wherein generating the artificial color microscopic image of the microscopic imaging field comprises using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

[0081] In some applications, wherein generating the normalized version of the initial color image comprises:

[0082] determining a maximum intensity within the initial color image; and

[0083] removing all pixels having an intensity less than one-half of the maximum intensity.

[0084] In some applications, wherein generating a normalized version of the initial color image further comprises:

[0085] generating an intensity histogram of the image; and

[0086] for each pixel within the initial color image having an intensity at least equal to one-half of the maximum intensity:

[0087] identifying in the intensity histogram a nearest local maximum having an intensity greater than one-half of the maximum intensity within the image; and

[0088] normalizing the intensity of the pixel based on a difference between the maximum intensity and the intensity of the local maximum.

[0089] According to some applications of the present invention, there is also provided an apparatus for use with a blood sample, the apparatus comprising:

[0090] a microscope configured to acquire three images of a microscopic imaging field of the blood sample, each of the images acquired using a respective different imaging condition;

[0091] an output device; and

[0092] at least one computer processor configured to generate on the output device an artificial color microscopic image of the microscopic imaging field by:

[0093] mapping each of the images to a respective different channel within an additive color model to generate an initial color image, and

[0094] generating a normalized version of the initial color image so as to remove pixels within the image having an intensity below a threshold.

[0095] In some applications, wherein the computer processor is configured to generate an artificial color microscopic image of the microscopic imaging field comprises using a neural network.

[0096] In some applications, wherein the computer processor is configured to generate an artificial color microscopic image of the microscopic imaging field comprises using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

[0097] In some applications, wherein the computer processor is configured to generate a normalized version of the initial color image by:

[0098] determining a maximum intensity within the initial color image; and

[0099] removing all pixels having an intensity less than one-half of the maximum intensity.

[0100] In some applications, wherein the computer processor is configured to generate the normalized version of the initial color image by:

[0101] generating an intensity histogram of the image, and

[0102] for each pixel within the initial color image having an intensity at least equal to half of the maximum intensity:

[0103] identifying in the intensity histogram the nearest local maximum having an intensity greater than half of the maximum intensity within the image, and

[0104] normalizing the intensity of the pixel based on the difference between the maximum intensity and the intensity of the local maximum.

[0105] The application will be more fully understood from the following detailed description of the embodiments thereof, taken together with the drawings in which: BRIEF DESCRIPTION OF DRAWINGS

[0107] Figure 1 is a block diagram showing components of a biological sample analysis system according to some applications of the application;

[0108] Figure 2A , Figure 2B and Figure 2C are schematic diagrams of an optical measurement unit according to some applications of the application;

[0109] Figure 3A , Figure 3B and Figure 3C are schematic diagrams of respective views of a sample carrier for performing both microscopic measurements and optical density measurements according to some applications of the application; and

[0110] Figure 4A , Figure 4B , Figure 4C and Figure 4D are flowcharts showing steps of a method performed according to some applications of the application.

[0111] DETAILED DESCRIPTION OF THE EMBODIMENTS

[0112] Reference will now be made to Figure 1which is a block diagram showing components of a biological sample analysis system 20 according to some applications of the present application. Typically, a biological sample (e.g., a blood sample) is placed into a sample carrier 22. While the sample is in the sample carrier, one or more optical measurement devices 24 are used to make optical measurements of the sample. For example, the optical measurement devices can include microscopes (e.g., digital microscopes), spectrophotometers, photometers, spectrometers, cameras, spectral cameras, hyperspectral cameras, fluorometers, fluorescence spectrophotometers, and / or photodetectors (such as photodiodes, photoresistors, and / or phototransistors). For some applications, the optical measurement devices include dedicated light sources (such as light emitting diodes, incandescent light sources, etc.) and / or optical elements (such as lenses, diffusers, filters, etc.) for manipulating light collection and / or light emission.

[0113] A computer processor 28 typically receives and processes the optical measurements made by the optical measurement devices. Also, typically, the computer processor controls acquisition of the optical measurements made by the one or more optical measurement devices. The computer processor is in communication with a memory 30. A user (e.g., a laboratory technician or an individual from whom the sample was drawn) sends instructions to the computer processor via a user interface 32. For some applications, the user interface includes a keyboard, a mouse, a joystick, a touch screen device (such as a smartphone or tablet computer), a touchpad, a trackball, a voice command interface, and / or other types of user interfaces known in the art. Typically, the computer processor generates output via an output device 34. Also, typically, the output device includes a display, such as a monitor, and the output includes output displayed on the display. For some applications, the processor generates output on different types of visual, textual, graphical, haptic, audio, and / or video output devices (e.g., a speaker, headphones, a smartphone or tablet computer). For some applications, the user interface 32 serves as both an input interface and an output interface, i.e., it serves as an input / output interface. For some applications, the processor generates output on a computer readable medium (e.g., a non-transitory computer readable medium) such as a disk or a portable USB drive, and / or generates output on a printer.

[0114] Reference is now made to Figure 2A , Figure 2B and Figure 2C , Figure 2A , Figure 2B and Figure 2C are schematic illustrations of an optical measurement unit 31 according to some applications of the present application; Figure 2A showing an oblique view of the exterior of the fully assembled device, while Figure 2B and Figure 2CRespective angled views of the device are shown, in which the cover has been made transparent so that components within the device are visible. For some applications, one or more optical measurement devices 24 (and / or computer processor 28 and memory 30) are housed within an optical measurement unit 31. To perform optical measurements on a sample, the sample carrier 22 is placed within the optical measurement unit. For example, the optical measurement unit can define a slot 36 via which the sample carrier is inserted into the optical measurement unit. Typically, the optical measurement unit includes a stage 64 configured to support the sample carrier 22 within the optical measurement unit. For some applications, a screen 63 on the cover of the optical measurement unit (e.g., a screen on the front cover of the optical measurement unit, as shown) functions as the user interface 32 and / or output device 34.

[0115] Typically, the optical measurement unit includes a microscope system 37 (shown in Figure 2B - Figure 2C ) configured to perform microscopic imaging of a portion of the sample. For some applications, the microscope system includes a set of brightfield light sources 65 (which typically includes a set of brightfield light sources (e.g., light emitting diodes) configured for brightfield imaging of the sample), a set of fluorescence light sources 66 (which typically includes a set of fluorescence light sources (e.g., light emitting diodes) configured for fluorescence imaging of the sample), and a camera 67 (e.g., a CCD camera or a CMOS camera) configured to image the sample. Typically, the optical measurement unit also includes a light density measurement unit 39 (shown in Figure 2C ) configured to perform light density measurements (e.g., light absorption measurements) of a second portion of the sample. For some applications, the light density measurement unit includes a set of light density measurement light sources (e.g., light emitting diodes) and a light detector configured for light density measurements of the sample. For some applications, each of the aforementioned sets of light sources (i.e., the set of brightfield light sources, the set of fluorescence light sources, and the set of light density measurement light sources) includes more than one light source (e.g., more than one light emitting diode), where each light source is configured to emit light at a respective wavelength or at a respective wavelength band.

[0116] Reference is now made to Figure 3A and Figure 3B , Figure 3A and Figure 3B are schematic views of respective views of a sample carrier 22 according to some applications of the present application. Figure 3A A top view of the sample carrier is shown (for illustrative purposes, the top cover of the sample carrier is shown as being opaque in Figure 3A ), and Figure 3B a bottom view is shown (where the bottom cover of the sample carrier is shown as being opaque in Figure 3AThe sample carrier has been rotated about its short edge (as shown in the view). Generally, the sample carrier includes one or more sample chambers of a first set 52 for microscopic analysis of a sample and sample chambers of a second set 54 for optical density measurements of a sample. Generally, the sample chambers of the sample carrier are filled with a bodily sample, such as blood, via a sample inlet aperture 38. For some applications, the sample chambers define one or more outlet apertures 40. The outlet apertures are configured to assist in filling the sample chambers with the bodily sample by allowing air present in the sample chambers to be released from the sample chambers. Generally, as shown, the outlet apertures are positioned longitudinally opposite (with respect to the sample chambers of the sample carrier) the inlet apertures. For some applications, the outlet apertures thus provide a more effective air escape mechanism than if the outlet apertures were placed closer to the inlet apertures.

[0117] Reference is made to Figure 3C which shows an exploded view of a sample carrier 22 according to some applications of the present application. For some applications, the sample carrier includes at least three components: a molded component 42, a glass layer 44 (e.g., a glass plate), and a bonding layer 46 configured to bond the glass layer to the underside of the molded component. The molded component is generally made of a polymer (e.g., plastic) that is molded (e.g., via injection molding) to provide sample chambers having a desired geometry. For example, as shown, the molded component is generally molded to define an inlet aperture 38, an outlet aperture 40, and a channel 48 around a central portion of each sample chamber. The channel generally assists in filling the sample chambers with a bodily sample by allowing air to flow toward the outlet aperture and / or by allowing the bodily sample to flow around the central portion of the sample chamber.

[0118] For some applications, in performing a complete blood count on a blood sample, a sample carrier as shown in Figure 3A - Figure 3C is used. For some such applications, the sample carrier is used with an optical measurement unit 31, which is generally configured as shown and described with reference to Figure 2A - Figure 2C For some applications, a first portion of the blood sample is placed within the sample chambers of the first set 52 (which are used, for example, to perform microscopic analysis of the sample using a microscope system 37 (shown in Figure 2B - Figure 2C ) and a second portion of the blood sample is placed within the sample chambers of the second set 54 (which are used, for example, to perform optical density measurements of the sample using an optical density measurement unit 39 (shown in Figure 2CThe first portion of the blood sample is typically diluted relative to the second portion of the blood sample. For example, the diluent can include a pH buffer, a stain, a fluorescent stain, an antibody, sphering agents, a lysing agent, etc. Typically, the second portion of the blood sample placed within the sample chamber of the second set 54 is native, undiluted blood sample. Alternatively or additionally, the second portion of the blood sample can be a sample that has undergone some modification including one or more of, for example, dilution (e.g., dilution in a controlled manner), addition of components or reagents, or fractionation.

[0119] For some applications, the first portion of the blood sample (placed within the sample chambers of the first set 52) is stained using one or more staining substances prior to microscopic imaging of the sample. For example, the staining substances can be configured to stain DNA preferentially over staining of other cellular components. Alternatively, the staining substances can be configured to stain all cellular nucleic acids preferentially over staining of other cellular components. For example, the sample can be stained with acridine orange reagent, Hoechst reagent, and / or any other staining substance configured to preferentially stain DNA and / or RNA in the blood sample. Optionally, the staining substances are configured to stain all cellular nucleic acids, but the staining of DNA and RNA individually is more visibly apparent under some illumination and filtering conditions, as is known for, e.g., acridine orange. Images of the sample can be acquired using imaging conditions that allow detection of cells (e.g., brightfield) and / or imaging conditions that allow visualization of stained corpuscles (e.g., appropriate fluorescent illumination). Typically, the first portion of the sample is stained with acridine orange reagent and Hoechst reagent. For example, the first (diluted) portion of the blood sample can be prepared using techniques as described in Pollak US 9,329,129, which is incorporated by reference herein, and which describes methods for preparing a blood sample for analysis that include a dilution step that facilitates identification and / or counting of components within a microscopic image of the sample. For some applications, the first portion of the sample is stained with one or more stains that make platelets within the sample visible under brightfield imaging conditions and / or under fluorescent imaging conditions, e.g., as described above. For example, the first portion of the sample can be stained with methylene blue and / or Romanowsky stains.

[0120] Also with reference to Figure 2BGenerally, the sample carrier 22 is supported within the optical measurement unit by a stage 64. Also generally, the stage has a forked design such that the sample carrier is supported by the stage around the edges of the sample carrier, but such that the stage does not interfere with the visibility of the sample chambers of the sample carrier to the optical measurement device. For some applications, the sample carrier is held within the stage such that the molded assembly 42 of the sample carrier is placed above the glass layer 44, and such that the objective lens 66 of the microscope unit of the optical measurement unit is placed below the glass layer of the sample carrier. Generally, at least some of the light sources 65 used during microscopic measurements of the sample (e.g., light sources used during brightfield imaging) illuminate the sample carrier from above the molded assembly. Also generally, at least some additional light sources (not shown) illuminate the sample carrier from below the sample carrier (e.g., via the objective lens). For example, light sources used to excite the sample during fluorescence microscopy can illuminate the sample carrier from below the sample carrier (e.g., via the objective lens).

[0121] Generally, prior to performing microscopic imaging, the first portion of blood (placed in the sample chambers of the first set 52) is allowed to settle, such as to form a monolayer of cells, e.g., using techniques as described in Pollak, US 9,329,129, which is incorporated by reference herein. For some applications, the first portion of blood is a cell suspension, and the chambers belonging to the first set 52 each define a cavity 55 (shown in Figure 3C Generally, the cells in the cell suspension are allowed to settle on the base surface of the sample chamber of the carrier to form a monolayer of cells on the base surface of the sample chamber. After the cells have been allowed to settle on the base surface of the sample chamber (e.g., by having been allowed to settle for a predetermined time interval), at least one microscopic image of at least a portion of the monolayer is generally acquired. Generally, more than one image of the monolayer is acquired, each image corresponding to an imaging field located in a respective different region within the imaging plane of the monolayer. Generally, the depth level at which the microscope is focused in order to image the monolayer is determined, e.g., using techniques as described in Greenfield, US 10,176,565, which is incorporated by reference herein. For some applications, the respective imaging fields have different optimal depth levels from one another.

[0122] Note that, in the context of the present application, the term monolayer is used to refer to a layer of cells that have settled, such as placed in a single focus level of a microscope. Some cell overlap can exist within the monolayer, such that two or more overlapping layers of cells exist within certain regions. For example, red blood cells can overlap one another within the monolayer, and / or platelets can overlap or be placed above red blood cells within the monolayer.

[0123] For some applications, microscopic analysis of the first portion of the blood sample is performed on a cell monolayer. Typically, the first portion of the blood sample is imaged under brightfield imaging, i.e., under illumination from one or more light sources (e.g., one or more light-emitting diodes, which typically emit light at respective spectral bands). Also, typically, the first portion of the blood sample is additionally imaged under fluorescence imaging. Typically, fluorescence imaging is performed by directing light of known excitation wavelengths (i.e., wavelengths at which it is known that a stained object emits fluorescence if excited with light of these wavelengths) toward the sample to excite stained objects (i.e., objects that have absorbed a stain) within the sample, and detecting fluorescence. Typically, for fluorescence imaging, a separate set of light sources (e.g., one or more light-emitting diodes) is used to illuminate the sample at the known excitation wavelengths.

[0124] As described with reference to US 2019 / 0302099 to Pollak (which is incorporated by reference herein), for some applications, the sample chambers belonging to group 52 (for microscopy measurements) have different heights from one another in order to assist in using microscopic images of the respective sample chambers to measure different measurands, and / or different sample chambers are used for microscopic analysis of different sample types. For example, if a blood sample and / or a monolayer formed from the sample has a relatively low density of red blood cells, measurements can be performed in a sample chamber of the sample carrier having a relatively large height (i.e., a sample chamber of the sample carrier having a relatively large height relative to a different sample chamber having a relatively lower height), such that there is sufficient cell density, and / or such that there is sufficient cell density within a monolayer formed from the sample to provide statistically reliable data. Such measurements can include, for example, red blood cell density measurements, measurements of other cell properties (such as counts of abnormal red blood cells, counts of red blood cells including inclusions (e.g., pathogens, Howel- Jolly bodies), etc.), and / or hemoglobin concentration. Conversely, if a blood sample and / or a monolayer formed from the sample has a relatively high density of red blood cells, such measurements can be performed on a sample chamber of the sample carrier having a relatively lower height, e.g., such that there are sufficient sparse cells, and / or such that there are sufficient sparse cells within a cell monolayer formed from the sample such that cells can be identified within a microscopic image. For some applications, such a method can be performed even without precise knowledge of the height variation between the sample chambers belonging to group 52.

[0125] For some applications, the sample chamber within the sample carrier on which the optical measurement is performed is selected based on the measurement objects being measured. For example, sample chambers of the sample carrier having a larger height can be used for performing a white blood cell count (e.g., to reduce statistical errors that can be caused by low counts in shallower areas), white blood cell differentiation, and / or detecting more rare forms of white blood cells. Conversely, for determining mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), red blood cell distribution width (RDW), red blood cell morphological features, and / or red blood cell abnormalities, microscopic images can be obtained from sample chambers of the sample carrier having a relatively lower height, as in such sample chambers, cells are relatively sparsely distributed across the area of the chamber, and / or form a monolayer in which cells are relatively sparsely distributed. Similarly, for counting platelets, for classifying platelets, and / or for extracting any other property of platelets (such as volume), microscopic images can be obtained from sample chambers of the sample carrier having a relatively lower height, as within such sample chambers, red blood cells are less (fully or partially) overlaid with platelets in the microscopic image and / or in the monolayer.

[0126] According to the above-described embodiments, it is preferred to use sample chambers of the sample carrier having a lower height for performing optical measurements for measuring some measurement objects within a sample, such as a blood sample, and to use sample chambers of the sample carrier having a larger height for performing optical measurements for measuring other measurement objects within such a sample. Thus, for some applications, a first measurement object within a sample is measured by performing a first optical measurement (e.g., by taking a microscopic image thereof) on a portion of the sample placed within a first sample chamber belonging to the group 52 of sample carriers, and a second measurement object of the same sample is measured by performing a second optical measurement (e.g., by taking a microscopic image thereof) on a portion of the sample placed within a second sample chamber of the group 52 of sample carriers. For some applications, the first measurement object and the second measurement object are normalized with respect to each other, e.g., using techniques as described in US 2019 / 0145963 to Zait, which is incorporated herein by reference.

[0127] Generally, for performing optical density measurements on a sample, it is desirable to know as precisely as possible the optical path length, volume, and / or thickness of the portion of the sample on which the optical measurement is performed. Generally, a second portion of the sample, which is typically placed in the sample chambers of the second group 54 in undiluted form, is subjected to optical density measurements. For example, the concentration and / or density of a component can be measured by performing optical absorption, transmission, fluorescence, and / or luminescence measurements on the sample.

[0128] Also with reference to Figure 3BFor some applications, the sample chamber belonging to group 54 (for optical density measurements) generally defines at least a first region 56 (which is generally deeper) and a second region 58 (which is generally shallower), the height of the sample chamber varying between the first region and the second region in a predefined manner, for example as described in Pollak US 2019 / 0302099, which is incorporated herein by reference. The height of the first region 56 and the second region 58 of the sample chamber is defined by the lower surface defined by the glass layer and the upper surface defined by the molded component. The upper surface at the second region is stepped relative to the upper surface at the first region. The step between the upper surfaces at the first region and the second region provides a predetermined height difference Ah between the regions, such that even if the absolute height of the regions does not reach sufficient accuracy (for example due to tolerances in the manufacturing process), but the height difference Ah does reach sufficient accuracy for determining parameters of the sample using the techniques described herein and as described in Pollak US 2019 / 0302099, which is incorporated herein by reference. For some applications, the height of the sample chamber varies from the first region 56 to the second region 58, and then the height likewise varies from the second region to a third region 59, such that along the sample chamber, the first region 56 defines a maximum height region, the second region 58 defines an intermediate height region, and the third region 59 defines a minimum height region. For some applications, further variations in height occur along the length of the sample chamber, and / or the height gradually varies along the length of the sample chamber.

[0129] As described above, optical measurements of the sample are taken using one or more optical measurement devices 24 when the sample is placed in the sample carrier. Typically, the sample is viewed by the optical measurement devices via the glass layer, which is transparent at least to the wavelengths typically used by the optical measurement devices. Typically, the sample carrier is inserted into an optical measurement unit 31 housing the optical measurement devices when the optical measurements are taken. Typically, the optical measurement unit houses the sample carrier such that the molded layer is placed above the glass layer, and such that the optical measurement unit is placed below the glass layer of the sample carrier, and is able to take optical measurements of the sample via the glass layer. The sample carrier is formed by adhering the glass layer to the molded component. For example, the glass layer and the molded component can be bonded to one another during manufacturing or assembly (for example using thermal bonding, solvent-assisted bonding, ultrasonic welding, laser welding, thermal staking, adhesive, mechanical clamping, and / or another substance). For some applications, the glass layer and the molded component are bonded to one another during manufacturing or assembly using an adhesive layer 46.

[0130] For some microscopy applications, more than one different imaging modality is used to acquire microscopic images of an imaging field. For example, as described above, brightfield images can be acquired under illumination of the sample at several respectively different wavelength bands. Brightfield images can be acquired with the cells (e.g., a cell monolayer) in focus or out of focus. Alternatively or additionally, fluorescent imaging is acquired by directing light at the sample at a known excitation wavelength (i.e., the wavelength at which a stained object is known to emit fluorescence if excited with light at those wavelengths), exciting stained objects (i.e., objects that have absorbed a stain) within the sample, and detecting fluorescence. Respective fluorescent images are acquired by exciting the sample with light at a respective different wavelength band, or by exciting the sample with light at a particular wavelength band, and then using an emission filter to filter light of respective wavelength bands that is emitted from the sample.

[0131] Typically, a computer processor analyzes the microscopic images and / or other data (e.g., optical absorption measurements) related to the sample in order to determine characteristics of the sample. For some applications, the computer processor additionally outputs images of the sample to a user via output device 34. However, it can be challenging for a human observer to extract useful information from the images, particularly if the information is contained in an overlap between images acquired using respective different imaging modalities, and the images are overlaid on each other as black and white or grayscale images. For example, to verify that a feature is an intracellular parasite, it can be helpful to observe a single image in which a parasite candidate is visible and a red blood cell is visible. Red blood cells are typically visible in brightfield images (e.g., brightfield images acquired under violet light illumination), while parasites are typically visible in fluorescent images. Thus, it is helpful to observe such images overlaid on each other, but in which features from respective imaging modalities are visible without interfering with each other. Similarly, to observe morphological features of white blood cells (which can aid in classifying a feature as a white blood cell and / or a particular type of white blood cell), it is typically helpful to observe respective fluorescent images acquired under respective fluorescent illumination conditions overlaid on each other.

[0132] According to some applications of the present application, therefore, more than one image of a microscopic imaging field of a blood sample is acquired, each of the images acquired using a respective different imaging condition. Typically, at least one of the images is a brightfield image acquired under violet light illumination conditions (e.g., under illumination with light at a wavelength in the range of 400 nm to 450 nm). For some applications, the brightfield image is an out-of-focus image acquired under violet light illumination conditions. In addition, typically, at least one of the images is a fluorescent image. A computer processor combines data from each of the more than one images in order to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image. Typically, the artificial color microscopic image is generated using one or more color models, such as RGB, CIE, HSV, and / or combinations thereof.

[0133] Typically, the image acquired under brightfield violet light illumination conditions is mapped to the red channel of the artificial color microscopic image. In addition, typically, the image is converted to a negative contrast image prior to being mapped to the red channel. For some applications, the result of mapping to the negative contrast image of the image acquired under brightfield violet light conditions is that red blood cells have an appearance similar to the appearance of red blood cells in a color smear image (e.g., similar to the appearance produced using Giemsa or Wright-Romanowsky smear staining). Typically, the brightfield image acquired under violet light illumination conditions is used in the above-described manner because violet light is strongly absorbed by hemoglobin and, therefore, red blood cells appear red after the image is inverted to negative and the image is mapped to the red channel.

[0134] For some applications, three images are acquired in respective imaging modalities. For example, two fluorescent images can be acquired in addition to an image acquired under brightfield violet illumination conditions. For example, two fluorescent images can be acquired after exciting the blood sample with light of respective wavelength bands. Alternatively, two fluorescent images can be acquired after exciting the sample with light of the same wavelength band but using respective different emission filters. Typically, the second image is mapped to a second color channel of the artificial color microscopic image, and the third image is mapped to a third color channel of the artificial color microscopic image. For example, when using an RGB color model, the first image can be mapped to a red color channel (as described above), the second image can be mapped to a green color channel, and the third image can be mapped to a blue color channel. For some applications, one of the second and third images is acquired when exciting the sample with light (e.g., UV light) that causes the nuclei (e.g., the DNA of the nuclei) to fluoresce. Alternatively or additionally, the second of the second and third images is acquired when exciting the sample with light (e.g., blue light) that causes RNA and / or cytoplasm to fluoresce. For some applications, the imaging modalities used are similar to those used in images generated using Giemsa or Wright-Romanowsky smear staining.

[0135] For some applications, each of the fluorescent images is acquired using a relatively long exposure time. For example, this can be used to observe reticulocytes and platelets. Alternatively, one of the fluorescent images can be acquired using a relatively long exposure time, and the other of the fluorescent images can be acquired using a relatively short exposure time. The long-exposure and short-exposure fluorescent images typically contain different information. The image acquired using the short exposure is typically optimized to provide data related to white blood cells and other high-intensity objects, while the image acquired using the long exposure time is typically optimized to provide data related to low-intensity objects such as reticulocytes, platelets, parasites, ghost cells, etc.

[0136] For some applications, the short-exposure-time image is combined with the long-exposure-time image into a single fluorescent image (e.g., by replacing overexposed regions in the long-exposure-time image with corresponding regions in the short-exposure-time image). For some applications, the resulting composite image (and / or a composite image generated using a different composite image generation technique) is mapped to one of the channels of the artificial color image, e.g., using the techniques described above.

[0137] For some applications, a neural network is used to generate the artificial color image. In some cases, the artificial color image generated using the methods described above can have characteristics that are different from the types of images typically used in the art. For example, such images can differ from standard images in color, intensity resolution, shading, etc. For some applications, a convolutional neural network is used to generate an image that is more similar to a standard image in the field of view, such that the image has an appearance that is similar to the appearance of a color smear image (e.g., similar to an image generated using Giemsa or Wright-Romanowsky smear staining).

[0138] For some applications, one or more images mapped to the color image are normalized. For example, the images can be normalized by dividing the images by a background map. Alternatively or additionally, a function of the images, such as optical density, can be used for the color image. For some applications, the displayed color image is normalized such that relevant features have similar magnitudes in all channels. For some applications, one or more of the original images and / or the displayed color image are normalized by determining a maximum intensity within the image, and removing all pixels having an intensity that is less than a certain proportion of the maximum intensity (e.g., less than half of the maximum intensity), and re-normalizing the pixel intensities as described below. For some applications, one or more of the original images and / or the displayed color image are normalized in the following manner. An intensity histogram of the image is generated. For each pixel within the image having an intensity that is at least equal to half of the maximum intensity, the nearest local maximum having an intensity that is greater than half of the maximum intensity within the image is identified in the intensity histogram. The intensity of the pixel is then normalized based on the difference between the maximum intensity and the intensity of the local maximum. For example, a particular pixel can be assigned an intensity based on the following equation:

[0139] For V 最小 <=Ip<=V 最大 , INp=N*(Ip-V 最小 ) / (V 最小 -V 最大 );

[0140] For Ip>V 最大 , INp=N;

[0141] For Ip<V 最小 , INp=0

[0142] where:

[0143] INp is the normalized intensity of the pixel,

[0144] N is an integer (e.g., 255),

[0145] Ip is the raw intensity in pixels,

[0146] V 最大 is the maximum intensity within the image, and

[0147] V 最小 is the intensity of the closest maximum having an intensity greater than half the maximum intensity within the image.

[0148] Reference is now made to Figure 4A - Figure 4D , Figure 4A - Figure 4D is a flowchart showing steps of a method according to some applications of the present application, according to the technique described above.

[0149] Reference is now made to Figure 4A , for some applications, in step 100, more than one image of a microscopic imaging field of a blood sample is acquired using each different imaging condition. Subsequently, in step 102, data from each of the more than one images is combined in order to generate an artificial color microscopic image of the microscopic imaging field that appears like a color smear image. Step 102 is typically performed by computer processor 28.

[0150] Reference is now made to Figure 4B , for some applications, in step 110, three images of a microscopic imaging field of a blood sample are acquired using a microscope, each using a different imaging condition, and a first one of the three images is acquired under violet light brightfield imaging. Subsequently, in step 112, an artificial color microscopic image of the microscopic imaging field is generated by mapping the first one of the three images to a red color channel of the artificial color microscopic image (sub-step 114), mapping a second one of the three images to a second color channel of the artificial color microscopic image (sub-step 116), and mapping a third one of the three images to a third color channel of the artificial color microscopic image (sub-step 118). Steps 112 and sub-steps 114-118 are typically performed by computer processor 28.

[0151] Reference is now made to Figure 4C , for some applications, in step 120, three images of a microscopic imaging field of a blood sample are acquired using a microscope, each using a different imaging condition. Subsequently, in step 122, an artificial color microscopic image of the microscopic imaging field is generated by generating a normalized version of each of the images in order to remove pixels within the images having an intensity below a threshold (sub-step 124), and mapping the normalized version of each of the images to a different channel within an additive color model (sub-step 126). Steps 122 and sub-steps 124-126 are typically performed by computer processor 28.

[0152] Reference is made to Figure 4D For some applications, in step 130, three images of a microscopic imaging field of the blood sample are acquired using a microscope, each of the images acquired using a respective different imaging condition. Subsequently, in step 132, an artificial color microscopic image of the microscopic imaging field is generated by mapping each of the images to a respective different channel within an additive color model to generate an initial color image (sub-step 134), and generating a normalized version of the initial color image so as to remove pixels within the image having an intensity below a threshold value (sub-step 136). Steps 132 and sub-steps 134-136 are typically performed by computer processor 28.

[0153] For some applications, the devices and methods described herein are applied, mutatis mutandis, to biological samples, such as blood, saliva, semen, sweat, sputum, vaginal fluid, fecal matter, breast milk, bronchoalveolar lavage fluid, gastric lavage fluid, tears, and / or nasal discharge. The biological sample can be from any organism, and is typically from a warm-blooded animal. For some applications, the biological sample is a sample from a mammal, e.g., a sample from a human. For some applications, the sample is taken from any domesticated animal, zoo animal, and farm animal, including but not limited to dogs, cats, horses, cows, and sheep. Alternatively or additionally, the biological sample is taken from an animal used as a disease vector, including deer or rats.

[0154] For some applications, the devices and methods described herein are applied to non- bodily samples. For some applications, mutatis mutandis, the sample is an environmental sample, such as a water (e.g., ground water) sample, a surface swab, a soil sample, an air sample, or any combination thereof. In some embodiments, the sample is a food sample, such as a meat sample, a dairy sample, a water sample, a washing liquid sample, a beverage sample, and / or any combination thereof.

[0155] For some applications, the sample as described herein is a sample comprising blood or a component thereof (e.g., a diluted or undiluted whole blood sample, a sample comprising primarily red blood cells, or a diluted sample comprising primarily red blood cells), and parameters related to components in the blood, such as platelets, white blood cells, abnormal white blood cells, circulating tumor cells, red blood cells, reticulocytes, Howel-Jolly bodies, etc., are determined.

[0156] The applications of the application described herein can take the form of computer program products that are accessible from a computer-usable or computer-readable medium (e.g., non-transitory computer-readable medium) providing program code for use by or in connection with a computer or any instruction execution system such as the computer processor 28. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can include, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Typically, the computer-usable or computer readable medium is a non-transitory computer-usable or non-transitory computer readable medium.

[0157] Examples of computer-readable media include semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disc that is fixedly and non-removably attached to a computer, and an optical disc (e.g., a compact disc, CD, or a Blu-ray Disc™). Current examples of optical discs include compact disc - read only memory (CD-ROM), compact disc - read / write (CD-R / W), and DVD.

[0158] A data processing system suitable for storing and / or executing program code will include at least one processor (e.g., computer processor 28) coupled, directly or indirectly, to memory elements (e.g., memory 30) through a system bus. The memory elements can include local memory of the processor, bulk storage, and cache memory, which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. The system can read the instructions of the application on the program storage device and follow these instructions to execute the method of the application implementation.

[0159] Network adapters can be coupled to the processor to enable the processor to become coupled to other processors or remote printers or storage devices through intervening private or public networks. Modems, cable modems, and Ethernet cards are just a few of the currently available types of network adapters.

[0160] Computer program code for carrying out operations of the application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the C programming language or similar programming languages.

[0161] It should be appreciated that the algorithms described herein can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer (e.g., computer processor 28) or other programmable data processing apparatus, create means for implementing the functions / acts specified in the algorithms described in this application. These computer program instructions can also be stored in a computer readable medium (e.g., a non-transitory computer readable medium) that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the functions / acts specified in the algorithms described in this application. The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the algorithms described in this application.

[0162] Computer processor 28 is typically a hardware device that is programmed with computer program instructions to produce a special purpose computer. For example, when programmed to perform the algorithms described herein, computer processor 28 typically functions as a special purpose artificial image generation computer processor. Generally, the operations described herein as being performed by computer processor 28 transform the physical state of memory 30, which is a real physical article, according to the technology of the memory used.

[0163] The devices and methods described herein can be used in conjunction with the devices and methods described in any of the following patents or patent applications, all of which are incorporated by reference herein:

[0164] US 9,522,396 to Bachelet;

[0165] US 10,176,565 to Greenfield;

[0166] US 10,640,807 to Pollak;

[0167] US 9,329,129 to Pollak;

[0168] US 10,093,957 to Pollak;

[0169] US 10,831,013 to Yorav Raphael;

[0170] US 10,843,190 to Bachelet;

[0171] US 10,482,595 to Yorav Raphael;

[0172] US 10,488,644 to Eshel;

[0173] WO 17 / 168411 to Eshel;

[0174] US 2019 / 0302099 to Pollak;

[0175] US 2019 / 0145963 to Zait; and

[0176] WO 19 / 097387 to Yorav-Raphael.

[0177] Those skilled in the art will appreciate that the application is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the application includes both combinations and sub-combinations of the various features described hereinabove, as well as variations and modifications thereof that will occur to those skilled in the art upon reading the foregoing description, which are included within the scope of the application.

Claims

1. A method for use with a blood sample, the method comprising: Using a microscope, acquiring three images of the microscopic imaging field of the blood sample, wherein each of the images is acquired using a respective different imaging condition, and a first image of the three images is acquired under ultraviolet bright field imaging; and Using at least one computer processor, generating an artificial color microscopic image of the microscopic imaging field by the following steps: Mapping a first image of the three images to a red channel of the artificial color microscopy image, wherein mapping the first image of the three images to the red channel of the artificial color microscopy image comprises generating a negative contrast image of the first image of the three images, and mapping the negative contrast image to the red channel of the artificial color microscopy image; mapping a second image of the three images to a second color channel of the artificial color microscopy image; and A third image of the three images is mapped to a third color channel of the artificial color microscopy image.

2. The method according to claim 1, wherein the first image of the three images is an image acquired under defocused, ultraviolet bright field imaging conditions.

3. The method of claim 1, wherein generating the artificial color microscopic image of the microscopic imaging field of view comprises using a neural network to generate the artificial color microscopic image of the microscopic imaging field of view.

4. The method of claim 2, wherein generating the artificial color microscopic image of the microscopic imaging field comprises using a neural network to generate the artificial color microscopic image of the microscopic imaging field.

5. The method of any one of claims 1-4, wherein generating the artificial color microscopic image of the microscopic imaging field comprises using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

6. A device for use with a blood sample, the device comprising: a microscope configured to acquire three images of a microscopic imaging field of the blood sample, each of the images being acquired using respective different imaging conditions, and a first image of the three images being acquired under ultraviolet bright field imaging; output device; and at least one computer processor configured to generate an artificial color microscopic image of the microscopic imaging field on the output device by: mapping a first image of the three images to a red channel of the artificial color microscopy image, wherein the computer processor is configured to map the first image of the three images to the red channel of the artificial color microscopy image by generating a negative contrast image of the first image of the three images and mapping the negative contrast image to the red channel of the artificial color microscopy image, mapping a second of the three images to a second color channel of the artificial color microscopy image, and A third image of the three images is mapped to a third color channel of the artificial color microscopy image.

7. The apparatus of claim 6, wherein the microscope is configured to acquire the first of the three images as an image acquired under defocused, ultraviolet brightfield imaging conditions.

8. The apparatus of claim 6, wherein the computer processor is configured to generate an artificial color microscopic image of the microscopic imaging field using a neural network.

9. The apparatus of claim 6, wherein the computer processor is configured to generate the artificial color microscopic image of the microscopic imaging field using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

10. A method for use with a blood sample, the method comprising: Using a microscope, acquiring more than one image of the microscopic imaging field of the blood sample, each of the images being acquired using a respective different imaging condition, wherein a first image of the more than one images is acquired under ultraviolet bright field imaging; and Using at least one computer processor, data from each of the more than one images is combined to generate an artificial color microscopic image of the microscopic imaging field that appears to be a color smear image, wherein, also using at least one computer processor, a first image of the more than one images is mapped to a red channel of the artificial color microscopic image, and wherein mapping the first image of the more than one images to the red channel of the artificial color microscopic image includes generating a negative contrast image of the first image of the more than one images and mapping the negative contrast image to the red channel of the artificial color microscopic image.

11. The method of claim 10, wherein combining data from each of the more than one images to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image comprises using a neural network to combine data from each of the more than one images to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image.

12. A method according to claim 10 or claim 11, wherein combining data from each of the more than one images to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image includes using a color model selected from the group consisting of: RGB, CIE, HSV and combinations thereof.

13. A device for use with a blood sample, the device comprising: a microscope configured to acquire more than one image of a microscopic imaging field of view of the blood sample, each of the images being acquired using respective different imaging conditions, wherein a first image of the more than one images is acquired under ultraviolet bright field imaging; output device; and at least one computer processor configured to combine data from each of the more than one images to generate an artificial color microscopic image of the microscopic imaging field that appears to be a color smear image on the output device, wherein the at least one computer processor is also used to map a first image of the more than one images to a red channel of the artificial color microscopic image, and wherein mapping the first image of the more than one images to the red channel of the artificial color microscopic image includes generating a negative contrast image of the first image of the more than one images and mapping the negative contrast image to the red channel of the artificial color microscopic image.

14. The apparatus of claim 13, wherein the computer processor is configured to combine data from each of the more than one images using a neural network to generate an artificial color microscopic image of the microscopic imaging field that looks like a color smear image.

15. The apparatus of claim 13, wherein the computer processor is configured to combine data from each of the more than one images to generate an artificial color microscopic image of the microscopic imaging field that appears like a color smear image using a color model selected from the group consisting of: RGB, CIE, HSV, and combinations thereof.

Images