Method and system for on-demand image intensity correction

Abstract

A method and system are provided for correcting intensity variations in raw images acquired via a microscope imaging system. In one example, the method includes acquiring a reference image, constructing an intensity model based on the reference image, the intensity model comprising a predetermined number of model parameters obtained through a curve that fits a single analytical function or a linear combination of multiple analytical functions, embedding the model parameters of the intensity model of the reference image into the raw image for subsequent image processing, and correcting the raw image on demand based on the intensity model of the reference image. In another example, the method includes using several parameters of the intensity model as a hardware signature to indicate whether several optical system parameters are maintained.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims priority to U.S. Patent Application No. 18 / 193,548, titled "METHOD AND SYSTEMS FOR ON DEMAND IMAGE INTENSITY CORRECTION," filed on March 30, 2023. The entire content of the application listed above is incorporated herein by reference for all purposes.

[0002] This specification generally relates to methods and systems for flat - field correction in fluorescence microscopes.

[0003] Background / Summary Images obtained with fluorescence microscopes may exhibit some degree of non - uniformity in image intensity. The intensity non - uniformity can be due to vignetting of the optical system, excitation intensity roll - off of the illumination source, and / or optical train misalignment. In intensity - sensitive applications, images may be post - processed to reduce the intensity roll - off and improve image quality. To reduce intensity variations due to intensity roll - off in raw images, flat - field correction procedures can be implemented. Flat - field correction procedures utilize a reference image to reduce the intensity roll - off of raw images.

[0004] However, flat-field correction procedures can be computationally intensive and may be undesirable for speed-sensitive applications. Furthermore, storing reference images for offline correction may increase the frequency of errors as a result of storing large amounts of image data. This disclosure recognizes and attempts to address the shortcomings of the existing flat-field correction procedures described above. For example, a flat-field correction procedure may include embedding information about the reference image in the metadata field of the raw image and later performing flat-field correction only on a portion of the raw image that has a significant intensity rolloff. In this way, the flat-field correction procedure may be less computationally intensive for applications with large amounts of image data and reduce image acquisition and storage times. Furthermore, information about the reference image may be used as a hardware signature for the microscope imaging system. Deviations from the hardware signature may indicate changes to the optical system that prevent the hardware signature from being used for quality control. Thus, the reference image may not be available when performing flat-field correction.

[0005] For example, the above problem may be addressed by a method for on-demand intensity correction of a microscope imaging system, which includes: acquiring a reference image; constructing an intensity model based on the reference image, the intensity model comprising a predetermined number of model parameters obtained through a curve that fits a single analytical function or a linear combination of multiple analytical functions; embedding the model parameters of the intensity model of the reference image into the raw image for subsequent image processing; and correcting the raw image on demand based on the intensity model of the reference image.

[0006] It should be understood that the above summary is provided in a simplified form to introduce a selection of concepts that will be further explained in the detailed description. It is not intended to identify any important or essential features of the claimed subject matter, whose scope is uniquely defined by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to any implementation that solves any defects described above or in any part of this disclosure. [Brief explanation of the drawing]

[0007] [Figure 1] A schematic diagram of a quantitative microscope assembly is shown. [Figure 2] A perspective view of a multi-detector quantitative microscope system is shown. [Figure 3] This diagram shows a flowchart of the method for constructing an intensity model based on reference images. [Figure 4] This shows a flowchart of the method for on-demand image intensity correction. [Figure 5A] An example of image intensity modeling is shown. [Figure 5B] An example of image intensity modeling is shown. [Figure 5C] An example of image intensity modeling is shown. [Figure 5D] An example of image intensity modeling is shown. [Figure 6A] An example of image intensity correction is shown. [Figure 6B] An example of image intensity correction is shown. [Figure 6C] An example of image intensity correction is shown. [Figure 6D] An example of image intensity correction is shown. [Figure 7] This shows a flowchart of the method for hardware signing. [Figure 8] This diagram shows the timing of when hardware signatures are used. [Modes for carrying out the invention]

[0008] The following description relates to systems and methods for high-throughput quantitative microscopy. Quantitative microscopes can be used to extract information from digital images by illuminating a sample with light of a desired wavelength or a range of wavelengths. In one example, the wavelength may be selected to induce fluorescence from the sample, which can then be measured by a detector to provide a quantitative analysis of the sample's properties. Exemplary microscope imaging systems are described with respect to Figures 1 and 2. The microscope imaging system may be a multi-detector quantitative microscope system. An example of a quantitative microscope assembly is depicted as a schematic diagram in Figure 1. The assembly shown in Figure 1 may be included in each of the four blades of a multi-detector quantitative microscope system, as shown in Figure 2.

[0009] Multiple model parameters of the intensity model of a reference image acquired via a quantitative microscope assembly can be obtained according to the method described in Figure 3. A method for performing flat-field correction on raw images acquired via a quantitative microscope assembly to reduce intensity variations in the raw images is illustrated in Figure 4. Figures 5A to 5D illustrate examples of image intensity modeling by the method described herein. Similarly, Figures 6A to 6D show examples of performing flat-field correction on raw images acquired via a quantitative microscope assembly according to the method described herein. Multiple model parameters can be used as a hardware signature for the imaging system according to the method described in Figure 7. A timing diagram of when the hardware signature is used as a quality control means for the quantitative microscope assembly is shown in Figure 8.

[0010] Referring here to Figure 1, a schematic diagram of a quantitative microscope assembly 100 (hereinafter, assembly 100) is shown. In one example, assembly 100 may be configured as a fluorescence microscope assembly. However, various other types of analytical microscope imaging techniques are possible, including but not limited to emission and colorimetric methods. Assembly 100 in Figure 1 includes a light source 102 that provides incident light to components arranged in the path of the incident light, as indicated by arrow 104. The light source 102 may be a mercury lamp, a xenon arc lamp, a laser, or one or more light-emitting diodes (LEDs).

[0011] Incident light can be directed to a filter cube (or filter block) 106. The filter cube 106 may house components that filter the incident light so that a target wavelength is transmitted to a target to be analyzed, for example, one or more samples supported on a sample holder 108. In one example, the sample holder 108 may be a microplate. Three filtering components, including an excitation filter 110, a dichroic filter 112, and an emission filter 114, are arranged within the filter cube 106. The incident light may first pass through the excitation filter 110, which filters the light to allow only a selected wavelength, for example, a target wavelength, to continue passing through the excitation filter 110. The target wavelength may be a wavelength that excites electrons in a particular fluorophore or fluorescent dye, resulting in the emission of photons when the excited electrons relax to the ground state. The excitation light, for example, the light filtered by the excitation filter 110, then strikes a dichroic filter (or dichroic beam splitter) 112, as indicated by arrow 116. The dichroic filter 112 may be, for example, a mirror positioned at a 45-degree angle to the optical path of assembly 100 and at a 45-degree angle to the incident light path, as indicated by arrow 104.

[0012] The surface of the dichroic filter 112 may include a coating that reflects excitation light, for example, light filtered by the excitation filter 110, but allows fluorescence emitted from the sample in the sample holder 108 to pass through. The reflected excitation light passes through the objective lens 118, as indicated by arrow 116, and illuminates the sample holder 108. If the sample emits light, the light is emitted, generating emitted light, for example, as indicated by arrow 120, and is collected by the objective lens 118. The emitted light passes through the dichroic filter 112 and continues to the emission filter 114, which blocks undesirable excitation wavelengths. The filtered emitted light is received by the detector 122. In one example, the detector 122 may be a camera, such as a charge-coupled device (CCD) camera. In other examples, the detector 122 may be another type of camera, such as a CMOS camera or a photomultiplier tube.

[0013] The detector 122 can convert emitted light into electronic data. For example, if the detector 122 is a CMOS camera, the detector 122 may include a photosensor configured as a transistor on an integrated circuit. The photons of the emitted light may be incident on the photosensor and generate charges that are converted into electronic data representing the photon pattern of the emitted light captured within the camera's field of view (FOV). The electronic data may be stored in the camera's memory, such as random-access memory, and can be retrieved by the controller 124.

[0014] The controller 124 may be a computer including various components such as a processor, input / output ports, an electronic storage medium for executable programs and calibration values, random access memory, and a data bus. The electronic storage medium can be programmed with computer-readable data representing instructions executable by the processor to perform the methods described below, as well as other expected but not specifically listed variations. The controller 124 may be coupled with various accessory devices, including input devices such as a keyboard and a mouse.

[0015] The controller 124 may be communicatively coupled to components of the assembly 100. For example, the controller 124 may be configured to instruct the activation / deactivation of the light source 102 when prompted based on user input. In another example, the controller 124 may command the adjustment of the position of the sample holder 108 and focus the excitation light onto different areas of the sample holder. The controller 124 may instruct the operation of a motor 126 coupled to the sample holder 108 to vary the position of the sample holder 108 relative to the objective lens 118 and the excitation light, and may provide instructions on how the sample holder position should be corrected. In some examples, a position sensor 128 may monitor the actual position of the sample holder 108 and may be communicatively coupled to the controller 124 to relay the sample holder position to the controller 124.

[0016] The controller 124 can also be communicatively coupled to the detector 122. Thus, the electronic data collected by the detector 122 can be retrieved by the controller 124 for further processing and display on an interface such as a computer monitor, in accordance with the methods described herein with respect to Figures 3, 4, and 7. It will be understood that the controller 124 can be further coupled to other sensors and actuators of the assembly 100. In one example, communication between the controller 124 and the sensors and actuators of the assembly 100 can be enabled by various electronic cables, e.g., hardwiring. In another example, the controller 124 can communicate with the sensors and actuators via wireless protocols such as Wi-Fi, Bluetooth, or Long-Term Evolution (LTE).

[0017] Assembly 100 may further include an autofocus system 130 communicatively coupled to controller 124. The autofocus system 130 may utilize a sensor configured with a light source and optical elements to modify a light beam from the light source and direct it towards sample holder 108. An image may be generated based on the reflection of the light beam and used by controller 124 to determine a suitable adjustment of the objective lens and / or sample holder 108 to align the focus of the objective lens with the target interface of sample holder 108. In one example, the autofocus system 130 may rapidly focus assembly 100 on a desired region of the sample depending on a laser beam and autofocus algorithm implemented in controller 124.

[0018] It will be understood that the assembly 100 depicted in FIG. 1 is a non-limiting example of a quantitative microscopy assembly. Other examples may include variations in the quantities of individual components, such as the number of dichroic, excitation, and emission filters, the configuration of the light source, the relative positioning of the components, etc. Specific examples of ways in which a quantitative microscopy assembly may be arranged to increase throughput while providing high-resolution results are described herein. In one example, a quantitative microscopy assembly, such as assembly 100 of FIG. 1, may be used for high-throughput screening of biological samples.

[0019] In one example, as shown in FIG. 2, the multi-detector system 200 can be formed from four individual blades 202 arranged in an X-shaped configuration. The multi-detector system 200 is depicted in FIG. 2, and the upper part of the system and the part of the housing 220 of the multi-detector system 200 are omitted for clarity. The upper part may include an upper plate 207 of the housing 220 of the multi-detector system 200, as well as a sample receiving assembly 211. A set of reference axes 201 is provided that indicates the y-axis, x-axis, and z-axis. In one example, the z-axis can be parallel to the direction of gravity. Further, the central axis 204 of the multi-detector system 200 can be parallel to the z-axis. Each blade 202 of the multi-detector system 200 can include at least the components depicted in the assembly 100 of FIG. 1, and the blades 202 can operate simultaneously to collect image data in parallel. Thus, each blade 202 forms an individual quantitative microscopy assembly. The components arranged on each of the blades 202 can be arranged to optimize both the FOV of the objective lens of each blade and the magnification / resolution of the resulting image. Thus, the components can be arranged in a vertical orientation, for example, as a stack along each blade.

[0020] In the view shown in FIG. 2, the side walls 205 of the housing 220 of the multi-detector system 200 are omitted to show two of the blades 202, and two of the blades 202 are surrounded by the housing 220. The housing 220 includes an upper plate 207 coupled to the upper ends of two pairs of side walls 205 oriented in opposite directions. The upper plate 207 can be a rigid plate having a square shape and can have dimensions similar to those of the base 222. The upper plate 207, the base, and the side walls 205 form a substantially rectangular prism for surrounding the components of the multi-detector system 200.

[0021] For example, each blade 202 of the multi-detector system 200 may be similarly configured, including a vertically oriented plate 206 that supports various components. Each blade 202 may have a front surface 210 and a back surface 212. An objective lens 208, which may be an embodiment of the objective lens 118 in Figure 1, may be positioned above each blade 202, along with other components of each blade 202 that are positioned below the objective lens 208 with respect to the z-axis.

[0022] In one example, the objective lenses of blade 202 are clustered around a central axis 204. The objective lens 208 may be mounted on plate 206 by an objective lens module 228. The objective lens module 228 may include an objective lens mover, which may be a motor that adjusts the position of the objective lens 208 along the z-axis, as well as a position sensor (not shown) that monitors the position of the objective lens along the z-axis. The objective lens mover may be controlled, for example, activated / deactivated, by a controller such as controller 124 in Figure 1.

[0023] In the upper region of the blade 202, the objective lens 208 may be mounted inward along the plate 206 such that the objective lens 208 is aligned parallel to the inner edge of the plate 206 along the x-axis (for example, toward the central axis 204 shown in Figure 2). In this way, the objective lens 208 may be located at the upper left corner of the blade 202 (when viewing the front surface 210 of the blade 202) and may protrude above the upper end of the plate 206. Furthermore, the objective lens 208 may be adjustable to translate along the z-axis to enable focusing of the image projected by the objective lens 208. Translation of the position of the objective lens 208 along the z-axis may be enabled by a motor, for example, as included in the objective lens module 228, or it may be adjusted manually.

[0024] The objective lens 208 may be a substantially cylindrical component formed of multiple lenses enclosed within a barrel, configured to provide a target magnification for the image. Thus, the objective lens 208 may have a specific magnification and numerical aperture (NA), where NA is a value indicating the range of angles from which the objective lens 208 can accept or emit light. The objective lens 208 is a component of a quantitative microscope assembly positioned closest to (and below) the sample, capable of collecting and focusing light from the sample to produce an image. As the magnification provided by the objective lens 208 increases, the field of view (FOV) of the objective lens 208 decreases. In one example, the objective lens 208 may include a fixed-focus lens and therefore be used at a specific working distance, such as between 0.5 and 2 mm.

[0025] The objective lens 208 may be attached to the plate 206 by an objective lens module 228 extending between the objective lens and a projection on the plate 206. The objective lens module may include a bracket that permanently connects the objective lens 208 to the objective lens module 228, an objective lens mover which may be a motor that adjusts the position of the objective lens 208 along the z-axis, and a position sensor that monitors the position of the objective lens along the z-axis. The objective lens mover may be controlled, e.g., activated / deactivated, by a controller such as the controller 124 in Figure 1. The objective lens module 228 may be attached to the plate 206 by a plurality of fasteners which may also be used to secure other components to the plate 206.

[0026] The blade 202 may also include a light source 248, which may be an embodiment of the light source 102 in Figure 1, surrounded by a cover secured to the plate 206 by a plurality of fasteners. The cover of the light source 248 may occupy a portion of the width of the blade 202, and the cover of the light source 248 may have a rectangular outer geometric shape. The light source 248 may be positioned in the central region of the plate 206 such that the light source 248 (and cover) is spaced apart from all edges of the plate 206.

[0027] In one example, the front surface 210 of plate 206 may include a first optical passage 232 extending vertically (for example, along the z-axis) directly below the objective lens 208. The first optical passage 232 may enclose microscope components, such as tube lenses, light-emitting filters, and selection mirrors, and may be an embodiment of the filter cube 106 in Figure 1. A camera 238, which may be an embodiment of the detector 122 in Figure 1, may be coupled to plate 206 directly below the first optical passage 232. A second optical passage 246 may be coupled to plate 206 adjacent to the first optical passage 232. In one example, the first optical passage 232 and the second optical passage 246 are optically coupled. The blade 202 may also include a light source 248, which may be an embodiment of the light source 102 in Figure 1, enclosed by a cover 249 fixed to the front surface 210 of plate 206.

[0028] In one example, camera 238 may be a CCD camera configured to use a CCD to convert electrical signals into optical images or video. In another example, camera 238 may be a complementary metal-oxide-semiconductor (CMOS) camera that uses a metal-oxide-semiconductor to convert light into electrical signals. Camera 238 may be positioned to receive emitted light from an illuminated sample and enable analysis of the image produced by the camera, the objective lens 208, and the emitted light delivered from the sample to camera 238 via the first optical passage 232. Thereafter, camera 238 is optically coupled to the objective lens 208 by the first optical passage 232. Camera 238 may be mounted at the lower end of the first optical passage 232 and held in place by a bracket or some other support mechanism.

[0029] In one example, the front surface 210 of the plate 206 of the blade 202 may support a laser autofocus sensor (LAF sensor) 242. In one example, the LAF sensor 242 may emit a laser beam into the optical path of the quantitative microscope assembly so that the LAF sensor 242 can use the laser beam as an optical probe to determine the focus of the quantitative microscope assembly. In one example, the LAF sensor 242 may be configured to generate a 785 nm red laser beam from a light source such as a laser diode. The laser beam may be reflected from the surface of the sample or the surface of the sample holder (e.g., a microplate) and returned to the LAF sensor 242 as an optical signal that can be used to evaluate the focus of the quantitative microscope assembly.

[0030] The laser autofocus controller (LAF controller) 260 may be located below, for example, the z-axis, light source, and LAF sensor 242. The LAF controller 260 may include various electronic components for signal processing, operation of the LAF sensor 242, monitoring of the status of the LAF sensor 242, etc., and may be fitted to a connector 262 to allow connection of a cable to the LAF sensor 242. The cable may allow communication between the LAF sensor 242 and the LAF controller 260. The LAF controller 260 may include multiple ports along the axis to allow connection of the LAF controller 260 to a system controller, such as controller 124 in Figure 1. The LAF controller 260 and LAF sensor 242 may be referred to as an LAF system. In one example, the LAF system may be an embodiment of the autofocus system 130 in Figure 1.

[0031] The connectivity between the LAF controller 260 and the system controller may allow the position of the objective lens 208 to be adjusted based on the alignment of the focus of the quantitative microscope assembly with the target focal plane, as detected by the LAF sensor 242. For example, the LAF controller 260 may include computer-readable instructions for executing an automatic focusing routine to adjust the position of the objective lens 208 relative to an object placed in the well 221 of the microplate 215. In response to information from the LAF controller 260, the system controller may instruct the adjustment of the objective lens 208 by activating the objective lens mover of the objective lens module and, accordingly, correcting the position of the objective lens 208 along the z-axis.

[0032] The LAF sensor 242 is oriented parallel to and spaced apart from a first optical path 232, which extends along a portion of the height of the plate 206. The LAF sensor 242 may include multiple optical elements, including a light source for generating a laser beam and a detector for receiving the reflected laser beam, which can be processed by the LAF controller 260 (as described below). In one example, the detector may be configured as a CCD or CMOS detector. The multiple optical elements may further include a focal plane array, one or more lenses, an aperture stop, a beam splitter, and the like. The laser beam may be directed and shaped by the multiple optical elements to generate an optical signal used to align the focus of a quantitative microscope assembly.

[0033] The LAF sensor 242 may transmit a laser beam at its upper end in a direction perpendicular to the optical path of the first optical passage 232. For example, a second optical passage 246 may extend horizontally, for example, along the x-axis, between the upper end of the LAF sensor 242 and the first optical passage 232, and may merge with the first optical passage 232 at an intersection. The second optical passage 246 may be surrounded by a casing having a plane, the casing being attached to the plate 206 by a plurality of fasteners or other fastening devices, and may continuously merge with the first optical passage 232 so that the first optical passage 232 and the second optical passage 246 are optically coupled. The laser beam emitted by the LAF sensor 242 may thereby pass through the second optical passage 246 and merge with the excitation light. As the laser beam passes through the second optical passage 246, the laser beam may interact with components enclosed within the first optical passage 232 and the second optical passage 246, which facilitate the deflection and transmission of the laser beam and excitation light, as will be further described below.

[0034] The plate 206 may further include a plurality of openings positioned between the LAF controller 260 and the lower end of the plate 206. The plurality of openings may be aligned along an axis and separated into two groups, each having three openings. Fasteners may be inserted through the plurality of openings to attach the plate 206 to a mounting device such as a bracket, which is used to connect the plate 206 to the base of the multi-detector system 200, for example, the base 222 in Figure 2.

[0035] The multi-detector system 200 is positioned on the upper plate 207 and may further include a sample receiving assembly 211 attached to the upper plate 207. In one example, the sample receiving assembly 211 may include a plate holder 213 coupled to the stage 217. The plate holder 213 may include a portion that extends over and covers the stage 217, forming a bracket 219. The bracket 219 may be fixed to the stage 217 via fasteners, thereby fixing the stage 217 to the plate holder 213. The stage 217 is positioned laterally to the plate holder 213 (except for the bracket 219 on the plate holder 213) so that the stage 217 is positioned laterally to the microplate 215 when the microplate 215 is placed in the opening 223 of the plate holder 213. The stage 217, configured to adjust the position of the plate holder 213 along the upper plate 207, is coupled to the plate holder 213.

[0036] In one example, the objective lens 208 may extend upward from the blade 202 and into the space between the upper plate 207 and the plate holder 213, but without contact with the plate holder 213 or the microplate 215 supported by the plate holder. The plate holder 213 can be translated along the xy plane without any obstructions hindering its movement. The distance at which the plate holder 213 is perpendicularly separated from the objective lens 208 may be configured to allow the objective lens 208 to be positioned at a target distance from the microplate 215 located above and a target distance from the tube lens located below. By positioning the objective lens 208 at target distances from the microplate 215 and the tube lens, the maximum FOV and resolution can be obtained from the quantitative microscope assembly of the multi-detector system 200.

[0037] The stage 217 may be a biaxial stage composed of bearings such as mechanical bearings and pneumatic bearings to allow the plate holder 213 to translate along the x and y axes, respectively, relative to the objective lens 208. In some examples, the movement of the stage, and therefore the movement of the plate holder 213, may be controlled by a motor. In other examples, the relative position of the stage 217 may be adjusted manually. Adjusting the position of the stage 217 allows for correction of the FOV of the objective lens 208 relative to the microplate 215, enabling complete imaging of the microplate 215.

[0038] Each blade 202, clustered around a central axis 204, forms a region of space where a central fan 225 may be positioned. The multi-detector system 200 is shown to be mounted on a chassis 227. The chassis 227 may support peripheral components such as electronic devices coupled to the multi-detector system 200. The multi-detector system 200 may also include a vibration isolator 230 coupled to the base 222 of the housing 220. The vibration isolator 230 may be a support on which the housing 220 of the multi-detector system 200 is seated.

[0039] Turning our attention first to the first optical passage 232, the emission filter may be positioned within the first optical passage 232, close to the intersection of the first optical passage 232 and the second optical passage 246. The emission filter may be an example of the emission filter 114 in Figure 1, and is configured to remove undesirable wavelengths from the emitted light traveling from the objective lens 208 to the camera 238. Materials from which the emission filter can be formed include colored glass, glass with dielectric optical coatings for specific wavelengths, acrylic, and the like, and may be configured for long-pass or band-pass transmission.

[0040] An emission filter can be directly coupled to a tube lens so that the light filtered by the emission filter immediately passes through the tube lens. In one example, the emission filter may be attached to the tube lens to form a single unit. For example, the emission filter may include a frame having fittings configured to have press-fit or threaded connections for mating with similar connections on the tube lens.

[0041] The tube lens may have optical properties configured to complement the optical properties of the objective lens 208. The tube lens is separated and spaced apart from the objective lens 208 at a distance. In one example, the distance may be between 50 and 200 mm. In a second example, the distance may be between 85 and 90 mm. However, other distances are also possible. The tube lens can be separated from the objective lens 208 to allow for a variable pairing of the tube lens's focal length and the objective lens's magnification, thereby achieving a desired balance between resolution and field of view (FOV).

[0042] The lower end of the tube lens may be connected to a camera mount that couples the camera 238 to the tube lens and maintains the camera 238's position. For example, the camera 238 may have a screw engagement with the camera mount. The camera mount may also be coupled to the tube lens and may have a length configured to dissipate heat between the tube lens and the camera 238. Thus, the camera mount may also be a thermal insulator and can assist in thermal management of the camera 238. For example, any heat generated by the interaction of emitted light with the emission filter and / or tube lens may be absorbed by the camera mount. The camera mount may be formed of a plastic with insulating properties.

[0043] The light source 248 includes light-emitting diodes (LEDs) arranged around a set of dichroic mirrors. The LEDs include a first LED, a second LED, a third LED, and a fourth LED. Each LED may be configured to emit light of a different wavelength. For example, the first LED may have a center wavelength of 630 nm, the second LED may have a center wavelength of 470 nm, the third LED may have a center wavelength of 390 nm, and the fourth LED may have a center wavelength of 555 nm. However, other center wavelengths may be possible. During sample imaging, each LED may be individually activated to illuminate the sample with light of a different wavelength, depending on which of the LEDs is activated. Separate images of the sample may be obtained from each illumination channel of the LEDs, providing images that depict variations in the fluorescence of the sample depending on the center wavelength of the incident light.

[0044] By directly coupling the light source 248 to the blade 202, the illumination provided by the light source can be brighter than if the light source were placed outside the blade 202 and distal to other imaging components supported on the plate 206. As a result, exposure time can be shortened and imaging speed can be improved. In other examples, the light source 248 may instead be coupled by an optical cable, fiber optic cable, etc., but such as these may provide less bright illumination.

[0045] A set of dichroic mirrors may be positioned in the region between groups of LEDs and may include a first dichroic mirror, a second dichroic mirror, and a third dichroic mirror. The position and angle of the set of dichroic mirrors relative to the LEDs may be configured to allow a target set of wavelengths to be transmitted to the sample while reflecting shorter wavelengths. Each of the set of dichroic mirrors may be a long-pass (LP) dichroic mirror configured beyond a certain wavelength threshold through which light with sufficiently long wavelengths can pass. Thus, the set of dichroic mirrors may be positioned such that each of the set of dichroic mirrors is located in a suitable LED path of the LEDs. The light produced by each LED interacts with at least one of the dichroic mirrors before passing through the output light guide.

[0046] The transmitted / reflected light from each LED (e.g., excitation light) can be reflected from a first dichroic mirror to a first selection mirror via an output light guide along a linear path parallel to the z-axis. The top of the output light guide can be aligned, for example, with the top of the emission filter along the x-axis. The first selection mirror is positioned in a second optical path and can be oriented at an angle similar to that of the set of dichroic mirrors, for example, 45 degrees, with respect to the x-axis.

[0047] Excitation light from the LED can be reflected 90 degrees by a first selection mirror. The reflected excitation light travels parallel to the x-axis in a second optical path from the first selection mirror to the second selection mirror. The first selection mirror may consist of a coating that reflects light with wavelengths below a threshold wavelength, while light above the threshold wavelength can be transmitted through the first selection mirror without interference or obstruction. For example, the threshold wavelength of the first selection mirror may be 700 nm. Thus, while the laser beam from the LAF sensor 242 (e.g., a 785 nm red laser) passes through the first selection mirror, all excitation light from the LED is reflected by the first selection mirror.

[0048] In the second selection mirror, the excitation light and the first portion of the laser beam are reflected at a 90-degree angle and converge along a common linear path upward along the z-axis to the objective lens 208 through the first optical passage 232. The beam formed from the excitation light and the first portion of the laser beam may continue through the objective lens to the sample where the excitation light induces fluorescence in the sample. The second selection mirror may be positioned within the first optical passage 232 at the intersection of the first optical passage 232 and the second optical passage 246. The second selection mirror may be oriented with respect to the x-axis at a similar angle to the first selection mirror, for example, at the angle of a pair of dichroic mirrors. The first portion of the laser beam reflected by the second selection mirror may represent the majority of the laser beam, e.g., more than 50% of the laser beam photons. The second portion of the laser beam is smaller than the first portion and may be transmitted through the second selection mirror and continue along the x-axis, attenuated in the housing of the first optical passage 232. The LAF sensor 242 can be preferentially tuned to minimize the second portion of the laser beam.

[0049] When irradiated with excitation light, the sample may emit light at wavelengths different from the wavelength that induces fluorescence. The emitted light, for example, can travel along a linear path from the sample to the camera through the first optical channel 232. Therefore, a second selection mirror may be positioned in the path of the emitted light and configured to allow the emitted light to pass through it without obstruction. Thus, the second selection mirror may be fitted with a coating that reflects the wavelengths generated by the LED and LAF sensor 242 but transmits the expected wavelengths of the emitted light. For example, the second selection mirror may allow transmission of wavelengths between 400 and 700 nm. Then, as described above, the transmitted light is filtered as it passes through the emission filter.

[0050] The light source 248 may be powered and controlled by a printed circuit board assembly (PCBA). An example of a PCBA 203 coupled to the back surface 212 of blade 202 is depicted. The PCBA 203 may include various electronic components coupled to a printed circuit board (PCB) 209. For example, the LED of the light source 248 may be directly coupled to the PCB 209 via soldering. The various electronic components may further include diodes, capacitors, resistors, switches, inductors, and the like.

[0051] As shown in Figure 3, a method 300 for obtaining a reference image offline and constructing an intensity model based on the reference image. Instructions configured, stored, and executed in at least one memory by at least one processor of a computing device (e.g., a controller) can cause the computing device to store multiple model parameters defining the intensity model in the non-volatile memory of the imaging system's hardware. By storing the constructed intensity model in the hardware's non-volatile memory, the multiple model parameters can be accessed on demand according to instructions during the flat-field correction process.

[0052] In 302, method 300 includes obtaining a reference image offline for flat-field correction. The reference image may be obtained offline when fast image acquisition (DAQ) is not actively being performed by the imaging system imaging a uniform fluorescent sample at a predetermined optical alignment of the microscope imaging system, such as the operating wavelength or optical path. In particular, the reference image may be generated by imaging a uniform fluorescent sample, such as a dye solution or slide impregnated with uniformly distributed emitters. In some embodiments, the reference image may be the same size as the raw image.

[0053] In 304, method 300 includes modeling the intensity of a reference image. As described with respect to Figure 1, the optical system may be rotationally symmetric about the optical axis, and therefore the intensity of the reference image can be modeled as a surface produced by rotating an analysis curve about its central axis. Some examples of analysis curves may include Gaussian curves, Lorentz curves, polynomial functions, and the like. In some embodiments of the present disclosure, the intensity model of the reference image may be based on one analysis function or a linear combination of multiple analysis functions. The selection of a suitable combination of multiple analysis functions may be based on the intensity roll-off characteristics of the optical system.

[0054] In one embodiment, the Gaussian curve can be rotated around its central axis to generate a surface that serves as an intensity model of a reference image. The Gaussian curve can be represented by the following equation:

number

[0055] In another embodiment, the polynomial function may be rotated around its central axis to generate a surface that serves as an intensity model of a reference image. The polynomial function may be expressed by the following equation:

number

number

[0056] In one example, the intensity model may consist of a single sixth-degree polynomial function. Therefore, the intensity model may consist of nine parameters. In another example, the intensity model may be a linear combination of multiple polynomial functions, each of which may consist of different polynomial coefficients, radial distances, and orders. Specifically, one example of an intensity model may consist of a first polynomial function having a first degree, a first set of polynomial coefficients, and a first radial distance, and a second polynomial function having a second degree, a second set of polynomial coefficients, and a second radial distance.

[0057] It can be understood that other embodiments may utilize analytical functions, combinations of analytical functions, and modeling methods other than those described herein as intensity models of reference images, without departing from the scope of this disclosure. In particular, the intensity model may be determined by numerical techniques such as non-uniform rational basis splines (NURBs).

[0058] In 306, method 300 includes obtaining model parameters through a fitting process of a curve fitting algorithm. The reference image may include low spatial frequencies, and therefore the reference image may be reduced to a subset of parameters based on an analytical function as described herein. The subset of parameters may be based on a single analytical function and / or a combination of multiple analytical functions. In particular, a combination of multiple analytical functions may be defined by a total number of parameters such that the total number of parameters in the combination does not exceed a predetermined value (e.g., 100). The parameters may be obtained by a variety of curve fitting algorithms, including but not limited to the Levenberg-Marquardt algorithm. The best-fitting intensity model with the lowest residual may be determined through a variety of curve fitting algorithms.

[0059] In 308, Method 300 includes storing the model parameters in the non-volatile memory of the imaging system's hardware. The model parameters may be stored in the firmware of the microscope imaging system. Alternatively, the model parameters may be stored in the memory of at least one computing device within the optical system, such as the non-volatile memory of the circuitry within the optical system. Thus, the model parameters can be accessed on demand to embed the model parameters into the raw image for flat-field correction. Method 300 then terminates.

[0060] Figure 4 illustrates method 400 for image intensity correction. High-content analytical microscopy may require high-speed image acquisition. In addition to utilizing high-speed cameras, high-speed moving stages, and automatic focusing techniques, expanding the field of view (FOV) of the imaging optics can increase the speed of the image acquisition process in proportion to the square of the increase in the linear range. However, a large FOV may exceed the uniformity specifications of the illumination and imaging optics, resulting in intensity roll-off at the edges of the image. The imaging optics may include finite or infinite conjugate objective lenses and tube lenses.

[0061] Method 400 utilizes an intensity model of a reference image obtained via Method 300 in Figure 3, in conjunction with a flat-field correction procedure, to correct for intensity rolloff in the raw image. Instructions configured, stored, and executed in at least one memory by at least one processor of a computing device cause the microscope imaging system to perform data acquisition and data analysis. During data acquisition, the raw image may be acquired, model parameters may be retrieved from the hardware, and the model parameters may be embedded in the metadata field of the raw image. During data analysis, the raw image may be read, model parameters may be retrieved from the metadata field of the raw image, the reference image may be reconstructed from the embedded model parameters, and flat-field correction may be performed.

[0062] In 402, Method 400 includes obtaining a raw image via an imaging system. The raw image may be obtained via the imaging system during data acquisition, as in the system described above with respect to Figures 1 and 2. The raw image may be obtained while the system is actively performing Fast Image Data Acquisition (DAQ). In some embodiments, the raw image may include a biological sample. In 404, Method 400 includes retrieving model parameters from the hardware of the imaging system. In some embodiments, the model parameters may be retrieved by instructions configured, stored, and executed in at least one memory by at least one processor of a computing device in order to access the non-volatile memory of the hardware of the imaging system.

[0063] In 406, method 400 includes embedding model parameters in raw images. The raw images may include multiple metadata fields, such as metadata fields for sample information, imaging technique, image acquisition settings, and image structure information, as some examples. This can improve the quality of image reproduction, image processing, and image analysis. For example, model parameters can be embedded in raw images by including them in the metadata fields of the raw images in a specific image storage format during image acquisition. In this way, the model parameters can be accessed later on demand during data analysis.

[0064] Each raw image obtained from multiple raw images using an existing optical system may have the same reference image model parameters embedded in the raw image metadata field. These embedded model parameters can be used during image processing of the raw image and / or multiple raw images. In this way, the matching relationship can be permanently maintained through the parameter embedding process. The matching relationship refers to the connection between the hardware conditions under which the image is acquired and the resulting image data. In particular, the hardware conditions include the objective lens, tube lens, and camera mounting conditions. Furthermore, the hardware conditions include the LED aging conditions, LED position, and illumination optics such as all optics within the illumination branch. The model parameters cannot be used to correct the image data when the aforementioned hardware conditions are changed, but they can be used to correct the image data when the aforementioned hardware conditions are not changed.

[0065] In 408, method 400 includes loading a raw image into an image processing unit of the imaging system. The image processing unit may be communicatively coupled to the hardware of the imaging system, where the raw image is stored in at least one memory. During data analysis, instructions configured, stored, and executed in at least one memory by at least one processor of the hardware of the imaging system may cause the raw image to be uploaded to the image processing unit of the imaging system. In some embodiments, the uploaded image may be displayed on a display device, such as a computer screen. In 410, method 400 includes retrieving embedded model parameters from the metadata fields of the raw image. Further instructions configured, stored, and executed in at least one memory by at least one processor of the hardware of the imaging system may cause the embedded model parameters in the metadata fields of the raw image to be retrieved for data analysis. Depending on the time sensitivity of the image acquisition, the embedding may be completed when the system is not actively performing Fast Image Data Acquisition (DAQ).

[0066] In 412, method 400 includes reconstructing a reference image based on embedded model parameters. As described herein, model parameters may be embedded in the metadata fields of the raw images during image acquisition. After obtaining a number of raw images and embedding the model parameters, instructions configured, stored, and executed in at least one memory by at least one processor of the computing device may cause the computing device to access the model parameters from the specified metadata fields and reconstruct a reference image based on the model parameters for data analysis. Depending on the time sensitivity of the image acquisition, the reconstruction may be completed when the system is not actively performing Fast Image Data Acquisition (DAQ).

[0067] In this way, the reference image can be used to perform a flat-field correction procedure without storing a large amount of data (e.g., tens of millions of values) in memory. In some embodiments, reducing the reference image dataset can reduce the amount of data processed or stored during the image acquisition process. This, in turn, can increase the speed of the image acquisition process.

[0068] In 414, method 400 includes performing flat-field correction to remove intensity fluctuations and obtaining a corrected image. Flat-field correction can be calculated via the following formula:

number

[0069] The corrected image can be obtained by performing flat-field correction on the entire raw image or on a specific region of the raw image. A specific region of the raw image can be corrected by identifying the specific region and matching it to a correlated region on a reference image. In this way, regions of interest, such as biological samples, or regions with undesirable intensity roll-offs can be corrected, which in turn reduces the computational load and increases the computational efficiency of the flat-field correction procedure. Method 400 then ends.

[0070] An example of intensity correction modeling is illustrated in Figures 5A to 5D. Figures 5A and 5B include multiple images illustrating various aspects of the image intensity modeling process described above with respect to Figure 2, such as obtaining a reference image and reconstructing the intensity model based on model parameters. Furthermore, Figure 5C illustrates the residuals between the reconstructed intensity model and the corrected image. Figure 5D shows a plot of multiple radial intensity profiles. The multiple radial intensity profiles illustrate the difference in image intensity between the raw image, the reconstructed intensity model, and the corrected image.

[0071] Referring to Figure 5A, the reference image 500 may be obtained according to the method described herein (e.g., method 300 in Figure 3). The reference image 500 may be obtained by imaging a uniform target through a microscope imaging system such as the system illustrated in Figures 1 and 2. In particular, the reference image 500 is a fluorescence microscope slide that can match a specified fluorescence channel. The illumination optical system of the reference image 500 may be aligned with the optical axis of the imaging optical system within a predetermined tolerance (e.g., 50% of the field of view (FOV)).

[0072] In some embodiments of this disclosure, including a reference image 500, the sample slide may be subjected to a Kohler illumination scheme. In particular, the excitation of the sample slide may follow a Kohler illumination scheme. Thus, the sample slide may not be precisely positioned at the focal plane, and rather, by intentionally shifting the sample slide by about 5-10 μm, the influence of defects or dust particles on the sample slide on the uniformity of the reference image 500 may be reduced. Kohler illumination can provide a constant illumination pattern for the small focal scattering range described above.

[0073] Figure 5B illustrates a reproduced intensity model 501 based on the model parameters of the intensity model. The reproduced intensity model 501 is based on polynomial rotations (e.g., from the second to the ninth order) by the method described herein. Curve fitting algorithms, such as the curve fitting algorithm described above with respect to Figure 3, can model intensity variations by determining multiple parameters, such as 5 to 12 parameters, through a fitting process. Intensity variations of the sample slide can be reproduced through multiple parameters to generate the reproduced intensity model 501. The intensity variation of the reproduced intensity model 501 due to the dark current of the sensor may be several orders of magnitude smaller than that of other intensity variation sources. Thus, the dark frame of the flat-field correction can be ignored.

[0074] Both Figure 5A and Figure 5B include multiple intensity regions, including a first intensity region 502, a second intensity region 504, a third intensity region 506, a fourth intensity region 508, and a fifth intensity region 510. Figures 5A and 5B further include arrows 512 indicating the direction of decreasing intensity. The first intensity region 502 is centered on both the reference image 500 and the reproduced intensity model 501. The second intensity region 504 is adjacent to the first intensity region 502 and the third intensity region 506. The third intensity region 506 is adjacent to the second intensity region 504 and the fourth intensity region 508. The fourth intensity region 508 is adjacent to the third intensity region 506 and the fifth intensity region 510. When comparing multiple intensity regions, the intensity of the first intensity region 502 exceeds the intensity of the second intensity region 504, the intensity of the second intensity region 504 exceeds the intensity of the third intensity region 506, the intensity of the third intensity region 506 exceeds the intensity of the fourth intensity region 508, and the intensity of the fourth intensity region 508 exceeds the intensity of the fifth intensity region 510.

[0075] A visual comparison between the reference image 500 in Figure 5A and the reproduced intensity model 501 in Figure 5B shows that the first intensity region 502, second intensity region 504, third intensity region 506, and fourth intensity region 508 of the reproduced intensity model 501 are higher than those of the reference image 500. The fifth intensity region 510 shows that the intensity of the reproduced intensity model 501 is equivalent to the intensity of the reference image 500.

[0076] Figure 5C illustrates a residual plot 503 based on the percentage deviation between the corrected image and the reproducible intensity model 501, with a deviation range of approximately 5% to -6%. The residual plot 503 includes a first intensity variation region 514a, a second intensity variation region 514b, a third intensity variation region 516a, and a fourth intensity variation region 516b, a fifth intensity variation region 518, a sixth intensity variation region 520a, and a seventh intensity variation region 520b. In some embodiments of this disclosure, a positive percentage deviation may refer to a region where the image intensity of the corrected image is greater than that of the reproducible intensity model. Similarly, a negative percentage deviation may refer to a region where the image intensity of the corrected image is less than that of the reproducible intensity model.

[0077] The first intensity variation region 514a and the second intensity variation region 514b include percentage deviations in the range of approximately 2% to 5%. The third intensity variation region 516a and the fourth intensity variation region 516b include percentage deviations in the range of approximately 1% to 2%. The percentage deviation in the fifth intensity variation region 518 is in the range of approximately 1% to -1%. The sixth intensity variation region 520a and the seventh intensity variation region 520b include percentage deviations in the range of approximately -1% to -6%.

[0078] After correction of the raw image, the image intensity may be constant within the corrected image. In some embodiments, the residual plot 503 may provide insight into the quality of the flat-field correction. In particular, the residual plot 503 may demonstrate the ability of the intensity model to correct for non-uniform image intensity in the raw image. Thus, the uniformity of image intensity in the raw image may be increased by replacing the intensity model with a more sophisticated model to reduce non-uniform image intensity. For example, an intensity model based on a sixth-degree polynomial may result in an undesirable percentage deviation of image intensity between the reproduced intensity model 501 and the corrected image. As an example, the residual between the reproduced intensity model 501 and the corrected image may be further reduced by using an intensity model based on a seventh-degree polynomial instead of a sixth-degree polynomial.

[0079] Figure 5D illustrates the radial intensity plot 505 normalized by the average intensity over 360° of the raw image, the reproduced intensity model 501, and the corrected image. The radial intensity plot 505 includes multiple radial intensity profiles, such as the first radial intensity profile 522, the second radial intensity profile 524, and the third radial intensity profile 526. The multiple radial intensity profiles illustrate the average intensity from the center of the image to the periphery of the image.

[0080] The first radial intensity profile 522 illustrates the intensity percentage of the raw image from the center to the periphery. The second radial intensity profile 524 illustrates the intensity percentage of the reproduced intensity model 501 from the center to the periphery. The third radial intensity profile 526 illustrates the intensity percentage of the corrected image from the center to the periphery. The radial intensity plot 505 further includes dotted lines 528, 530, and solid line 532. Dotted line 528 intersects the vertical axis of the radial intensity plot 505 at maximum intensity.

[0081] The dotted line 530 intersects the vertical axis of the radial intensity plot 505 at the minimum intensity. The solid line 532, extending from the maximum intensity to the minimum intensity on the vertical axis, shows the percentage of intensity roll-off between the raw image and the reproduced intensity model 501. Based on the solid line 532, the raw image experienced approximately 60% intensity roll-off before correction of the raw image. After correction, the third radial intensity profile 526 is relatively flat and shows a low percentage of intensity roll-off (e.g., approximately 1%).

[0082] Figures 6A to 6D illustrate an example of the image intensity correction process described above with respect to Figure 4. In particular, the image intensity correction process is performed on biological samples by reproducing the intensity model and correcting the image intensity by performing flat-field correction on the raw image using the intensity model. The intensity model may be the same as the intensity model used above with respect to Figures 5A to 5D.

[0083] Figure 6A shows the raw image 600 of the biological sample, and Figure 6C shows the corrected image 603 of the biological sample after flat-field correction of the entire image has been performed. Figures 6A and 6C include multiple regions, such as a first region 602, a second region 604, and a third region 606. The first region 602 may be the region of interest of the biological sample. The region of interest may contain scientifically relevant information about the biological sample. The second region 604 may be the non-peripheral central region of the raw image 600 and the corrected image 603. The third region 606 may be the peripheral region of the raw image 600 and the corrected image 603.

[0084] Referring to Figure 6A, several model parameters of the reference image may be stored in the metadata field of the raw image 600. The raw image 600 exhibits significant intensity rolloff, which can reduce image quality. In particular, a significant intensity rolloff is present in the third or peripheral region of the raw image 600. The quality of the raw image 600 can be improved by implementing intensity correction according to the systems and methods described herein.

[0085] Figure 6B shows a reproduction intensity model 601 based on a sixth-degree polynomial. The reproduction intensity model 601 was formed via several model parameters of the intensity model embedded in the metadata field of the raw image 600. The reproduction intensity model 601 includes a first region 602 and arrow 608. The first region 602 in the reproduction intensity model 601 may correspond to the first region 602 in the raw image 600 and the corrected image 603. Arrow 608 indicates the direction of decreasing intensity within the reproduction intensity model 601. Thus, the image intensity is highest at the center of the reproduction intensity model 601 and lowest along the periphery of the reproduction intensity model 601.

[0086] The corrected image 603 shown in Figure 6C can be obtained by performing flat-field correction on the raw image 600 via the reproducible intensity model 601. A visual comparison of the corrected image 603 and the raw image 600 shows that the intensity roll-off is reduced in the corrected image 603. In particular, a visual comparison of the intensity of the first region 602, second region 604, and third region 606 of the raw image 600 and the corrected image 603 shows that the intensity of the second region 604 and the overall image quality of the corrected image 603 are higher than those of the raw image 600. The parts of the image in the first region 602, second region 604, and third region 606 in the corrected image 603 are clearer and more detailed than the parts of the image in the second region 604 of the raw image 600. In particular, the darkness at the edges and center of the corrected image 603 is significantly less compared to the raw image 600.

[0087] In other embodiments of this disclosure, a portion of the image may be subjected to flat-field correction, such as a first region 602. Thus, the first region 602 within the reproducible intensity model 601 may be used to correct a region of interest in the raw image 600 instead of the entire image. Choosing to correct only a portion of the image may reduce the computational cost associated with flat-field correction and enable faster, on-demand intensity correction. In this way, higher-quality images may be obtained with increased frequency.

[0088] Figure 6D illustrates a radial intensity plot 605 normalized by the average intensity over 360° of the raw image, the reproduced intensity model 601, and the corrected image 603. The radial intensity plot 605 includes multiple radial intensity profiles, such as the first radial intensity profile 610, the second radial intensity profile 612, and the third radial intensity profile 614. The multiple radial intensity profiles illustrate the average intensity from the center of the image to the periphery of the image.

[0089] The first radial intensity profile 610 illustrates the intensity percentage of the raw image 600 from the center to the periphery. The second radial intensity profile 612 illustrates the intensity percentage of the reproduced intensity model 601 from the center to the periphery. The third radial intensity profile 614 illustrates the intensity percentage of the corrected image 603 from the center to the periphery of the raw image 600. The radial intensity plot 605 further includes dotted lines 616, 618, and solid line 620. Dotted line 616 intersects the vertical axis of the radial intensity plot 605 at the averaged maximum intensity. Dotted line 618 intersects the vertical axis of the radial intensity plot 605 at the averaged minimum intensity.

[0090] The solid line 620, extending from the averaged maximum intensity to the averaged minimum intensity on the vertical axis, represents the percentage of intensity roll-off between the raw image 600 and the reproduced intensity model 601. Based on the solid line 620, the raw image 600 experienced approximately 60% intensity roll-off before correction. After correction, the third radial intensity profile 614 oscillates irregularly around a value of 1.0 (e.g., 100% intensity), indicating the presence of a biological sample. The centerline of the third radial intensity profile 614 is roughly leveled by the correction. In comparison, the third radial intensity profile 614 is flattened compared to the raw image 600 and the reproduced intensity model 601.

[0091] A method 700 for configuring and verifying the optical system of a microscope imaging system, as shown in Figure 7. The reference image depends on the optical alignment of the microscope imaging system and the illumination system. Therefore, deformation of the optical system can be determined offline during the installation of a new system or during regularly scheduled maintenance, for example, by examining the reference image used as a hardware signature. Changes or disturbances in the optical alignment (e.g., the imaging optics or illumination optics) can be detected by comparing such a hardware signature (i.e., the reference image) with a reference image recreated using model parameters embedded in the raw image. In some embodiments, the hardware signature can be obtained from a previously obtained reference image during the instrument manufacturing stage or through a scheduled calibration process. In this way, a user or computing system can identify differences between the hardware signature and the operating model parameters.

[0092] Thus, a microscope imaging system can be configured based on various parameters of the optical system. A default reference image can be obtained through the configured microscope imaging system to construct an intensity model based on the default reference image. Various model parameters defining the intensity model can be obtained and used to validate the optical system of the microscope imaging system. Validation of the optical system of the microscope imaging system can ensure that the desired optical alignment and optical path are maintained. Instructions configured, stored, and executed in at least one memory by at least one processor of a computing device can perform method 700.

[0093] In 702, Method 700 includes configuring the optical system of a microscope imaging system. As described above, the reference image depends on the optical alignment of the microscope imaging system and the illumination system. In particular, the reference image may be wavelength-dependent and optical system-dependent. Therefore, different optical system settings may utilize various operating wavelengths and optical system parameters, such as excitation or illumination schemes, to obtain high-quality raw images. The optical system of a microscope imaging system may be configured by setting a plurality of optical system parameters, including the light source wavelength, the alignment of the light source optics, and the alignment of the imaging optics and the camera. The optical system parameters may be configured according to instructions configured, stored, and executed in at least one memory by at least one processor of a computing device. In some embodiments, the computing device may be a controller communicatively coupled to the microscope imaging system.

[0094] Depending on the application (e.g., a specific biological sample), different sets of multiple optical system parameters may be selected for the microscope imaging system to reduce intensity roll-off and increase image quality. By setting multiple optical system parameters, multiple reference images may be obtained through a microscope imaging system configured according to different sets of multiple optical system parameters. Thus, a first reference image of multiple reference images, which may be a default reference image, may be used to construct a first intensity model to obtain a first set of model parameters for the first intensity model, according to the systems and methods described herein (e.g., Figure 3).

[0095] In some embodiments of this disclosure, a first set of model parameters may be stored in the firmware of the microscope imaging system. Alternatively, the first set of model parameters may be stored in the memory of at least one computing device in the optical system, such as the non-volatile memory of the circuitry in the optical system. Each set of model parameters for multiple reference images may similarly be stored to allow access to the model parameters when diagnosing the current state of the optical system. Furthermore, each set of model parameters may be considered a hardware key for the microscope imaging system.

[0096] In 704, method 700 includes constructing an intensity model using an acquired reference image and obtaining model parameters based on a newly acquired reference image. The acquired reference image may be a second reference image obtained by an optical system configured according to a plurality of optical system parameters used to obtain a first reference image (or default reference image). The second reference image is obtained separately from the first reference image. The second intensity model may be constructed to obtain a second set of model parameters for the second reference image according to the systems and methods described herein (e.g., Figure 3). To ensure a direct comparison between the first and second reference images, the second intensity model is constructed using the same modeling equations used to construct the first intensity model. In some embodiments, the second set of model parameters may be stored in at least one memory (e.g., volatile memory) of a computing device and accessed by the firmware of the microscope imaging system.

[0097] In 706, method 700 includes comparing a first reference image with a second reference image. In various embodiments of the present disclosure, the first reference image and the second reference image may be visually compared and / or compared by at least one processor of the computing device of the microscope imaging system via instructions configured, stored and executed in at least one memory. In one embodiment, a user may visually observe the first reference image and the second reference image to detect differences between the first reference image and the second reference image.

[0098] In another embodiment, a second set of model parameters for a second reference image may be used to evaluate the optical system of the microscope imaging system. As described herein, a first set of model parameters may be stored in memory to function as a hardware key. The first reference image and the first set of model parameters may constitute a hardware signature of the microscope imaging system and may indicate the alignment status of the optical system. The values ​​of the second set of model parameters for the second reference image may be compared with the values ​​of the first set of model parameters for the first reference image.

[0099] The comparison between a first set of model parameters and a second set of model parameters may include firmware for a microscope imaging system that accesses at least one memory where the values ​​of the first set of model parameters and the second set of model parameters are stored. The firmware may include instructions configured, stored, and executed in at least one memory by at least one processor to evaluate the difference in values ​​between the values ​​of the first set of model parameters and the second set of model parameters and to determine whether the difference in values ​​is statistically significant. The model equations based on the first set of model parameters and the second set of model parameters must be the same.

[0100] In 708, method 700 includes determining whether a hardware key matches a second set of model parameters. As described above, instructions may be configured, stored, and executed in the memory of a computing device of a microscope imaging system. In some embodiments, instructions may calculate the difference in values ​​between a first set of model parameters and a second set of model parameters. The difference in values ​​between the first set of model parameters and the second set of model parameters may be compared to a predetermined threshold.

[0101] If the values ​​of a second set of model parameters do not differ from the values ​​of a hardware key (e.g., a first set of model parameters) by a predetermined threshold, the microscope imaging system may not have deviated from a predetermined optical alignment and optical path. Thus, the hardware key may match the second set of model parameters, and the command may further alert the user in response to a default model parameter that matches the model parameter. Thus, method 700 includes, in 714, acquiring a raw image and performing flat-field correction based on an intensity model. The raw image and flat-field correction may be performed according to systems and methods described herein, such as the microscope imaging system illustrated in Figure 1 and the flat-field correction method described above with respect to Figure 4. Since the microscope imaging system did not deviate significantly from a predetermined optical alignment and optical path, a raw image is obtained and flat-field correction with intensity correction is performed using a default reference image (e.g., a first reference image) and a first set of model parameters of a first intensity model.

[0102] If the values ​​of a second set of model parameters differ from the values ​​of a hardware key (e.g., a first set of model parameters) by a predetermined threshold, the microscope imaging system may deviate from a predetermined optical alignment and optical path. Therefore, the hardware key may not match the second set of model parameters, and the command may further warn the user in response to default model parameters that do not match the model parameters. Thus, method 700 optionally includes reconfiguring the optical system of the microscope imaging system in 710. In some embodiments of the present disclosure, a first set of multiple optical system parameters may be reset to ensure that the microscope imaging system is configured with a desired optical alignment and optical path. In other embodiments, the optical system of the microscope imaging system may not be reconfigured. Rather, multiple optical system parameters used when obtaining a second reference image may be maintained. In this case, the second reference image may be established as a new default reference image.

[0103] In 712, method 700 includes constructing an intensity model and regenerating model parameters based on a new reference image. A new first reference image may be acquired via a reconstructed optical system of a microscope imaging system. A new first intensity model may be constructed, and a new first set of model parameters may be obtained according to the systems and methods described herein. Thus, the new first reference image may function as a hardware signature of the microscope imaging system, and the new first set of model parameters may function as a hardware key. Thus, the new first set of model parameters may be stored in and accessed in the memory of at least one computing device of the optical system.

[0104] In some embodiments, the modeling equations for the new first intensity model may be the same as or similar to those for the original first intensity model of the first reference image (the original default reference image). The modeling equations for the new first intensity model may include additional parameters, such as additional parameters resulting from modeling the new first reference image based on a seventh-degree polynomial instead of a sixth-degree polynomial. In other embodiments, the modeling equations for the new first intensity model may differ from those for the original first intensity model of the original first reference image (the original default reference image). The modeling equations for the new first intensity model may include additional or different parameters, for example, resulting from modeling the new first reference image based on a Gaussian curve instead of a sixth-degree polynomial. In another example, the modeling equations for the new first intensity model may include additional or different parameters as a result of modeling the new first reference image based on multiple polynomials. The multiple polynomials may be in different orders.

[0105] As described herein, if the optical system is not reconfigured and multiple optical system parameters are maintained, a second reference image may be established as a new default reference image or a new first reference image. Thus, a second intensity model previously constructed for the second reference image and a second set of previously obtained model parameters may be considered a new hardware signature for the microscope imaging system. In this way, the second set of model parameters may function as a new hardware key for the optical system. However, in some embodiments, the user may decide to obtain a new first reference image different from the second reference image, so as not to utilize the second reference image and the second set of model parameters as a hardware signature and hardware key, regardless of whether the optical system is reconfigured or not.

[0106] In 714, the method includes acquiring a raw image and performing flat-field correction based on an intensity model. The raw image and flat-field correction may be performed according to systems and methods described herein, such as the microscope imaging system illustrated in Figures 1 and 2, and the flat-field correction method described above with respect to Figure 4. Because the microscope imaging system deviates significantly from a given optical alignment and optical path, at least one of a second reference image or a new first reference image may serve as the default reference image and hardware signature. Thus, either the second set of model parameters or the new first set of model parameters may serve as the hardware key and is used to acquire a raw image and perform flat-field correction with intensity correction. Method 700 then terminates.

[0107] As shown in Figure 8, a timing sequence 800 is implemented in which a method for configuring and verifying the optical system of a microscope imaging system is implemented, as described above with respect to Figure 7. The timing sequence 800 may be implemented for multiple scenarios, including during the manufacturing process to ensure quality control, after the user receives the microscope imaging system from the manufacturer, after verifying that the optical system has maintained alignment tolerances throughout the shipping process, and during the debugging process of the daily use of the microscope imaging system to assist the user.

[0108] At time t1, the user of the microscope imaging system (e.g., Figure 1) sets the optical system parameters. Different applications of the microscope imaging system may utilize different optical system settings to obtain high-quality images. Thus, each application may have a unique set of optical system parameters that constitute the optical system of the microscope imaging system. The optical system parameters may constitute the optical alignment and optical path of the microscope imaging system. A first reference image can be considered a hardware signature of the optical system of the microscope imaging system. In particular, the first reference image may function as one of several default reference images for various optical system settings. A first intensity model based on the first reference image may be constructed for a particular set of optical system parameters, and therefore, a first set of corresponding model parameters of the first intensity model may function as a hardware signature of the optical system. To enable on-demand intensity correction, the hardware signature may be stored and accessed in the memory of at least one of the computing devices of the microscope imaging system.

[0109] At time t2, the user may verify the optical system of the microscope imaging system by acquiring a second reference image. As described herein, each set of optical system parameters correlates to a particular optical assembly and remains valid as long as the optical system is maintained. Deviations from the hardware signature and hardware key may indicate that the microscope imaging system is malfunctioning and requires maintenance to restore its performance. Verification of the microscope imaging system may be performed in response to adjusting or replacing optical components to evaluate the validity of the hardware signature and hardware key. In particular, verification may be performed as part of the manufacturing process, the shipping process, and the routine use of the microscope imaging system.

[0110] A second reference image may be obtained during the initial configuration and verification of the microscope imaging system by using a specific set of optical system parameters used to obtain the first reference image. In this way, the second reference image can be directly compared with the first reference image, and existing deviations in the hardware signature can be identified. A second intensity model may be constructed based on the second reference image at time t3. The second intensity model may be constructed using the model equation(s) used to construct the first intensity model via the systems and methods described herein. A second set of model parameters may be obtained during the modeling process.

[0111] In some embodiments, more detailed verification may include accessing a hardware key in memory (e.g., a first set of model parameters) and comparing a second set of model parameters with the hardware key (e.g., the first set of model parameters) stored in at least one memory. To enable the comparison between the hardware key and the second set of model parameters, the second set of model parameters may be temporarily stored in the memory of at least one memory of the computing device of the microscope imaging system at time t4. Thus, instructions configured, stored, and executed in at least one memory by at least one processor of the microscope imaging system may access the memory location where the hardware key is stored and the memory location where the second set of model parameters is stored and compare the values.

[0112] At time t5, a second set of model parameters, unlike hardware keys, indicates that the current configuration of the optical system differs from the original configuration of the optical system. Thus, the user of the microscope imaging system may receive an alert via an instruction informing the user that image quality may be reduced and that a remedial measure may be implemented to restore the performance of the microscope imaging system. The remedial measure may be performed at time t6. In some embodiments, the remedial measure may include resetting several operating system parameters to reconfigure the optical system of the microscope imaging system and obtaining another reference image for verification. In all embodiments of this disclosure, the remedial measure includes regenerating at least one model parameter from the existing optical system configuration based on an existing default reference image that is initially used for verification, and the reconfigured optical system based on a recently acquired reference image.

[0113] At time t7, after regenerating multiple model parameters, multiple raw images may be acquired, where the multiple model parameters may be embedded in the metadata fields of the multiple raw images. As long as the optical system settings are maintained, multiple default reference images may be used to perform flat-field correction on demand via the multiple model parameters embedded in the multiple raw images. In this way, the time to complete the image acquisition and correction process may be reduced, and image quality may be increased through on-demand intensity correction.

[0114] The Disclosure also provides a method for flat-field correction of a raw image, comprising: acquiring a reference image; constructing an intensity model based on the reference image, wherein the intensity model includes a predetermined number of model parameters obtained through a curve that fits a single analytical function or a linear combination of multiple analytical functions; embedding the model parameters of the intensity model of the reference image into the raw image for subsequent image processing; and correcting the raw image on demand, supported by the intensity model of the reference image. In a first example of this method, the reference image is obtained by imaging a uniform sample offline via a microscope imaging system.

[0115] In a second example of the method, which optionally includes the first example, the intensity model of the reference image includes a model surface formed by the rotation of at least one analysis function or a linear combination of multiple analysis functions around a rotation axis. In a third example of the method, which optionally includes one or both of the first and second examples, the intensity model includes a numerical model such as a non-uniform rational B-spline (NURB) model. In a fourth example of the method, which optionally includes one or more of each of the first through third examples, the analysis function includes a set of multiple model parameters obtained via a curve fitting algorithm stored in the metadata field of the raw image. In a fifth example of the method, which optionally includes one or more of each of the first through fourth examples, the hardware signature includes a hardware key containing a default set of model parameters for a default reference image. In a sixth example of the method, which optionally includes one or more of each of the first through fifth examples, correcting the raw image on demand based on the intensity model of the reference image offline includes performing array division over the raw image, intensity model, and intensity values ​​of the dark frame using flat-field correction.

[0116] The disclosure also provides support for a method for configuring and verifying an optical system, which includes setting a plurality of optical system parameters; obtaining a first reference image with an optical system configured according to the plurality of optical system parameters; constructing a first intensity model and obtaining a first set of model parameters for the first intensity model based on the first reference image; and storing the first set of model parameters in the memory of at least one computing device of the optical system. In a first example of the method, the method further includes obtaining a second reference image using an optical system configured according to a plurality of optical system parameters; constructing a second intensity model and obtaining a second set of model parameters for the second intensity model based on the second reference image; comparing the first set of model parameters for the first reference image with the second set of model parameters for the second reference image; performing a remedial action on the optical system in response to the second set of model parameters not matching the first set of model parameters; and not performing a remedial action on the optical system in response to the second set of model parameters matching the first set of model parameters.

[0117] In a second example of the method, optionally including the first example, performing a corrective action on an optical system in response to a first set of model parameters that does not match a second set of model parameters includes, optionally, configuring the optical system of a microscope imaging system by resetting a plurality of optical system parameters; obtaining a new first reference image using the optical system configured according to the plurality of optical system parameters; constructing a new first intensity model; regenerating a first set of model parameters for the first intensity model based on the new first reference image; and storing the first set of model parameters in the memory of at least one computing device in the optical system. In a third example of the method, optionally including one or both of the first and second examples, the first reference image is a default reference image, and the first set of model parameters is a hardware key.

[0118] In a fourth example of the method, which optionally includes one or more of the first to third examples, the second reference image is obtained separately from the first reference image. In a fifth example of the method, which optionally includes one or more of the first to fourth examples, the memory of at least one computing device in the optical system stores a set of multiple default reference images and corresponding model parameters. In a sixth example of the method, which optionally includes one or more of the first to fifth examples, each of the multiple default reference images is obtained via a different set of multiple optical system parameters. In a seventh example of the method, which optionally includes one or more of the first to sixth examples, the multiple optical system parameters include a predetermined operating wavelength.

[0119] The disclosure also provides support for a multi-detector microscope system, comprising a set of microscope assemblies arranged in an x ​​configuration, each of which comprises an objective lens and a stage configured to support an object to be imaged, and a computing device, wherein instructions configured, stored, and executed in memory by at least one processor of the computing device, when executed, cause the computing device to configure an optical system by setting a plurality of optical system parameters, to acquire a plurality of default reference images, to construct a default intensity model for each default reference image, to obtain default model parameters based on the default intensity models, to store the default model parameters in at least one memory of the computing device, to embed the default model parameters for the corresponding default reference images in the metadata fields of the raw images, and to perform flat-field correction on the raw images among the plurality of raw images via the default model parameters.

[0120] In the first example of the system, the instruction further causes the computing device to acquire a reference image from among several reference images using multiple optical system parameters used to acquire multiple default reference images, to construct an intensity model for each reference image, to obtain model parameters based on the intensity model, to compare the default model parameters with the model parameters, to warn the user in response to the default model parameters matching the model parameters, and to warn the user in response to the default model parameters not matching the model parameters. In the second example of the system, which optionally includes the first example, the object is positioned on a stage, and the stage is positioned above a set of microscope assemblies. In the third example of the system, which optionally includes one or both of the first and second examples, multiple default reference images and multiple raw images are acquired by a camera coupled to an imaging optical system including a finite conjugate objective lens or an infinite conjugate objective lens and a tube lens. In the fourth example of the system, which optionally includes one or more or each of the first through third examples, multiple optical system parameters include the wavelength of the light source, the alignment of the light source optical system, and the alignment of the imaging optical system and the camera.

[0121] It should be noted that the exemplary control and estimation routines contained herein can be used with a variety of system configurations. The control methods and routines disclosed herein may be stored as executable instructions in non-temporary memory and may be executed by a control system including a controller in combination with various sensors, actuators, and other system hardware. A particular routine described herein may represent one or more of any number of processing strategies, such as event-driven, interrupt-driven, multitasking, and multithreading. Accordingly, the various actions, operations, and / or functions illustrated may be in parallel or, in some cases, omitted in the illustrated sequence. Similarly, the order of processing is not necessarily required to achieve the features and benefits of the exemplary embodiments described herein, but is provided for the sake of illustration and explanation. One or more of the illustrated actions, operations, and / or functions may be executed repeatedly depending on the particular strategy used. Furthermore, the described actions, operations, and / or functions may be graphically represented by code programmed into non-temporary memory of a computer-readable storage medium within the control system, and the described actions may be executed by executing instructions in a system including various hardware components in combination with an electronic controller.

[0122] It will be understood that the configurations and routines disclosed herein are essentially illustrative and numerous variations are possible, and therefore these particular embodiments should not be considered restrictively. For example, the above techniques can be applied to other types of microscopes. Furthermore, unless expressly stated otherwise, terms such as “first,” “second,” and “third” are not intended to indicate any order, position, quantity, or importance, but rather are used solely as labels to distinguish one element from another. The subject matter of this disclosure includes all novel and non-trivial combinations and subcombinations of the various systems and configurations disclosed herein, as well as other features, functions, and / or properties.

[0123] Where used herein, the term “approximately” shall be interpreted as meaning plus or minus 5 percent of the range unless otherwise specified.

[0124] The following claims specifically point to certain combinations and subcombinations that are considered novel and non-obvious. These claims may refer to “one” element or “first” element or equivalent thereof. Such claims should be understood to include the incorporation of one or more such elements, and do not require or exclude two or more such elements. Other combinations and subcombinations of the disclosed features, functions, elements, and / or properties may be claimed through modifications to these claims or through the presentation of new claims in this application or related applications. Such claims shall also be considered to be included within the subject matter of this disclosure, whether broader, narrower, equal to, or different in scope from the original claims.

Claims

1. A method for flat-field correction of raw images, The process involves acquiring a reference image and constructing an intensity model based on the reference image, wherein the intensity model includes a predetermined number of model parameters obtained through a curve that fits a single analytical function or a linear combination of multiple analytical functions. For subsequent image processing, the model parameters of the intensity model of the reference image are embedded in the raw image, A method comprising correcting the raw image on demand based on the intensity model of the reference image.

2. The method according to claim 1, wherein the reference image is obtained by imaging a uniform sample offline via a microscope imaging system.

3. The method according to claim 1, wherein the intensity model of the reference image includes a model surface formed by the rotation of at least one analytical function or the linear combination of multiple analytical functions around a rotation axis.

4. The method according to claim 3, wherein the strength model includes a numerical model such as a non-uniform rational B-spline (NURB) model.

5. The method according to claim 3, wherein the analysis function includes a set of the plurality of model parameters obtained via a curve fitting algorithm stored in the metadata field of the raw image.

6. The method according to claim 1, wherein the hardware signature includes a hardware key that includes a set of default model parameters for a default reference image.

7. The method according to claim 1, wherein correcting the raw image on demand based on the intensity model of the reference image offline includes performing array division via the raw image, the intensity model, and the intensity values ​​of the dark frame using the flat-field correction.

8. A multi-detector microscope system, It comprises a set of microscope assemblies arranged in an x ​​configuration, and each of the set of microscope assemblies is An objective lens, and a stage configured to support the object to be imaged. A computing device wherein, when an instruction configured, stored, and executed in memory by at least one processor of the computing device is executed, the computing device, By setting multiple optical system parameters, the optical system is configured. Obtain multiple default reference images, A default intensity model is constructed for each default reference image, and default model parameters are obtained based on the said intensity model. The default model parameters are stored in at least one memory of the computing device. The default model parameters for the corresponding default reference image are embedded in the metadata field of the raw image. A computing device that performs flat-field correction on one of several raw images via the default model parameters, A multi-detector microscope system, including one.

9. The instruction further instructs the computing device to: In the multiple optical system parameters used to acquire the multiple default reference images, a reference image is selected from among the multiple reference images. A strength model is constructed for each reference image, and model parameters are obtained based on the said strength model. The default model parameters are compared with the aforementioned model parameters. In response to the default model parameter matching the model parameter, the user is warned. The multi-detector microscope system according to claim 8, which warns the user in response that the default model parameters do not match the model parameters.

10. The multi-detector microscope system according to claim 8, wherein the object is positioned on the stage, and the stage is positioned above the set of microscope assemblies.

11. The multi-detector microscope system according to claim 8, wherein the plurality of default reference images and the plurality of raw images are acquired by a camera coupled to an imaging optical system including a finite conjugate objective lens or an infinite conjugate objective lens and a tube lens.

12. The multi-detector microscope system according to claim 8, wherein the plurality of optical system parameters include the wavelength of the light source, the alignment of the light source optical system, and the alignment of the imaging optical system and the camera.

Images