Multi-segment darkfield imaging

Abstract

Methods and apparatus for multi-segment darkfield imaging. An example method for multi-segment darkfield imaging includes receiving an image set including 2n first darkfield images of a sample acquired with an optical microscope by sequentially activating different ones of 2n angular segments of a light source around a numerical-aperture cone of a microscope objective, where n is an integer greater than one. The method further includes computing n pairwise differences between the first darkfield images in the image set corresponding to opposing ones of the 2n angular segments. The method also includes computing a second darkfield image of the sample based on the n pairwise differences.

Description

MULTI-SEGMENT DARKFIELD IMAGINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 679,151 filed on August 4, 2024, the contents of which are incorporated herein by reference.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under R01 CA276653 awarded by the National Institutes of Health. The government has certain rights in the invention.FIELD

[0003] Various example embodiments relate to optical microscopy and, more specifically but not exclusively, to darkfield imaging of biological samples.BACKGROUND

[0004] Darkfield optical microscopy is a technique that creates a bright image of a specimen against a dark background by illuminating the corresponding sample with a hollow cone of light. This oblique illumination causes the un-scattered light to be excluded from collection by the microscope’s objective whereas the light scattered off the specimen is collected and manifests itself in the captured image in the form of bright pixels on a generally dark, almost black background. Darkfield optical microscopy can be particularly useful for observing live, unstained biological samples and for visualizing small, substantially transparent structures.BRIEF SUMMARY OF SOME SPECIFIC EMBODIMENTS

[0005] Various examples provide methods and apparatus for multi-segment darkfield imaging. In some examples, the disclosed multi-segment darkfield imaging can beneficially be used to enhance studies of subcellular structures in living cells, offering improved quantification of organelle content compared to conventional darkfield imaging. In some use cases, the disclosed multisegment darkfield imaging can beneficially be applied to the sample of interest together with other imaging techniques, such as quantitative phase imaging, using the same optical microscope, to generate a multidimensional picture of living cells in real-time.

[0006] In one example, an optical apparatus comprises: an objective configured to project an image of a sample onto a pixelated light detector; a light source configured to illuminate the sample from outside a numerical-aperture cone of the objective to create a dark field condition in the image; and a computing device configured to: control the light source to sequentially activate different ones of 2n angular segments thereof around the numerical-aperture cone and cause the pixelated lightdetector to capture a corresponding sequence of 2n first darkfield images (e.g., OF,) of the sample, where n is an integer greater than one; and compute a second darkfield image (e.g., EDF) of the sample based on n pairwise differences between the first darkfield images of the sequence corresponding to opposing ones of the 2n angular segments.

[0007] In another example, an automated method performed via a computing device for providing support to an optical microscope comprises: receiving an image set including 2n first darkfield images (e.g., OF / ) of a sample acquired with the optical microscope by sequentially activating different ones of 2n angular segments of a light source around a numerical-aperture cone of a microscope objective, where n is an integer greater than one; computing n pairwise differences between the first darkfield images in the image set corresponding to opposing ones of the 2n angular segments; and computing a second darkfield image (e.g., EDF) of the sample based on the n pairwise differences.

[0008] According to yet another example, provided is a non-transitory computer-readable medium storing instructions that, when executed by an electronic processor, cause the electronic processor to perform operations comprising the above method.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Other aspects, features, and benefits of various disclosed embodiments will become more fully apparent, by way of example, from the following detailed description and the accompanying drawings, in which:

[0010] FIG. 1 is a schematic diagram illustrating an optical microscope according to some examples.

[0011] FIGS. 2A-2D are schematic diagrams illustrating a first set of illumination patterns that can be used in the optical microscope of FIG. 1 according to some examples.

[0012] FIGS. 3A-3H are schematic diagrams illustrating a second set of illumination patterns that can be used in the optical microscope of FIG. 1 according to some additional examples.

[0013] FIG. 4 is a flowchart illustrating an image-processing method implemented in or in conjunction with the optical microscope of FIG. 1 according to some examples.

[0014] FIGS. 5A-5B pictorially illustrate imaging improvements achieved with the optical microscope of FIG. 1 and the image-processing method of FIG. 4 according to some examples.

[0015] FIG. 6 is a block diagram illustrating a computing device that can be used in or in conjunction with the optical microscope of FIG. 1 according to some examples.DETAILED DESCRIPTION

[0016] Measuring dynamic reorganization in cellular structure and organelles is important for the study of disease progression and response to treatment. As cells switch phenotypes in response to environmental or genetic signals, corresponding changes are observed in cellular componentsincluding mitochondria, endoplasmic reticulum, Golgi apparatus, melanosomes, microtubules, and the plasma membrane. These changes can be related to certain cellular events, such as necrosis, senescence, and the emergence of drug resistance. The quantification of intracellular dynamics, therefore, can serve as an indicator of a cell’s health, behavior, and its response to therapeutic interventions.

[0017] Imaging approaches can be used to measure changes in subcellular structure. For example, electron microscopy is commonly used to study details of organelle structure in fixed cells but cannot quantify changes in living cells in real time. Fluorescence microscopy is used to study cell structure, including measurement of damage to plasma membranes, measurements of melanosome maturation, localization of nuclei, and tracking lysosomes during autophagy and other cell states. However, the need for a fluorescent molecule that binds to the specific target or for a cell to express a fluorescently tagged protein can be a limiting factor for such applications of fluorescence microscopy. In addition, labels can affect cell behavior and the need for high-intensity illumination can cause phototoxicity, especially during live cell imaging.

[0018] As used herein, the term “real time” refers to a computer-based process that controls or monitors a corresponding sample or environment by receiving data, processing the received data, and generating a response sufficiently quickly to affect or characterize the environment without significant delay. In the context of control or processing software, real-time responses are often understood to be on the order of milliseconds, or sometimes microseconds. In the context of optical microscopy, “real-time” updates mean that the experimental data and measurement results derived therefrom sufficiently accurately represent the state of the sample or system at any point in time. In some cases, data-acquisition and / or processing delays of several seconds may still be considered to be within “real time” or “near real time” for at least some samples, systems, and / or scientific instruments.

[0019] In some examples, intrinsic scattering and autofluorescence can also be used for label- free imaging of cell structures. Side scatter measurements in flow cytometry can be used to quantify changes in subcellular structure without the need for fluorescent labels. Although forward scatter measurements predominantly measure cell size, side scatter reflects the granularity of the internal contents of the cell. However, flow cytometry is unsuitable for tracking changes in the same cell over time. Raman spectroscopy utilizes the weak inelastic scattering of light that is dependent on molecular composition to identify organelles and molecular composition within the cell, such as nucleic acids, mitochondria, and endoplasmic reticulum. However, the Raman signal is typically rather weak, which prompts the use of high-powered lasers or long exposure times to produce sufficient signal-to-noise ratios. Light scattering spectroscopy (LSS) uses elastic backscattering that is dependent on both wavelength and particle size to measure the concentration and size of organelles. For example, the ability of LSS to measure the size of organelles and changes instructure enable detection of precancerous and malignant cells in multiple cancer types. However, LSS is not an imaging technique and, as such, is challenging to use for studies in which localization of organelles inside cells is a factor. Autofluorescence is the natural phenomena of proteins emitting light when excited using a suitable wavelength without the use of labels. However, challenging limitations of autofluorescence include the low signal that organelles produce, the need for a specific wavelength of light for each organelle, and the limited number of organelles and structures that are naturally fluorescent without labeling.

[0020] Computational microscopy provides methods to measure the intrinsic contrast caused by refractive index variation within cells. Multiple organelles, including lysosomes and mitochondria, differ slightly in density and refractive index from the surrounding cytoplasm. This difference can be quantified using quantitative phase imaging (QPI) which measures phase shift as light passes through the cell. In some examples, QPI can be used to track lysosomes in living cells without the use of fluorescent tags. However, tracking organelles can be difficult with two-dimensional QPI due to the signal from overlapping cell components. Three-dimensional computational methods such as Fourier ptychography, holography, and three-dimensional differential phase contrast can be used to obtain sufficient information for resolving density or refractive index changes in each 3D voxel within the cells. However, such methods typically involve either tens of images per field of view, limiting their temporal resolution and their ability to conduct high throughput experiments in multi-well plates, or the use of relatively more complex optical systems, hindering integration with other techniques to provide multidimensional orthogonal data about the biological sample.

[0021] Additional techniques to study changes in cellular structure are based on brightfield and darkfield imaging. Brightfield signal correlates with absorption which is usable in studying organelles, such as melanosomes that absorb light, but may be limited for other cell structures. On the other hand, many subcellular features scatter light due to variations in the refractive index between organelles and cytoplasm. For cellular organelles (0.1 to 10 pm) imaged using visible light (e.g., 380 nm to 700 nm), this scattering can be understood using Mie scattering theory, and features within this size range can be detected using darkfield imaging. Example uses of darkfield imaging include detection of contaminants in blood samples and differentiation of cell types due to the inherent relatively high contrast even between objects whose refractive index is close to that of the media used. Additionally, darkfield imaging can be used, for example, to quantitatively measure the size of red blood cells, to track in real-time respiratory syncytial virus infecting cells, to measure nanoparticle distribution in lung cells, and to visualize and count sub-micron particles in suspension. When performing darkfield imaging using transillumination, larger features, such as the boundaries of cells, tend to have stronger signals. This tendency is due to more directional refraction of light into the objective from large features. As the feature size gets smaller, the light starts to scatter in a cone, thereby bending more of the scattered light away from the objective and decreasing thecorresponding detected signal. For a given wavelength, this cone keeps growing as the feature size gets smaller leaving only a miniscule amount of light to be collected by the objective. This change is commonly used for sizing particles. In addition, the difference in directionality between larger and smaller objects means that illumination from different directions can potentially resolve different features based on size.

[0022] At least some of the above-indicated problems in the state of the art can beneficially be addressed using various embodiments directed to multi-segment darkfield imaging disclosed herein. In one example, a programmable LED array is used to vary the direction of illumination to encode in the captured optical signal additional information about the feature size within cells. The corresponding sequence of darkfield images of the sample is acquired and subjected to postacquisition image processing to generate an enhanced darkfield image of the sample. Some examples of this approach are capable of successfully resolving small cellular features without interference from larger structures, thereby producing more consistent results during cell-shape changes than conventional darkfield imaging. In some applications, multi-segment darkfield imaging can beneficially be used to enhance the study of subcellular structures in living cells, e.g., via improved quantification of organelle content.

[0023] FIG. 1 is a schematic diagram illustrating an optical microscope 100 according to some examples. In the example shown, the microscope 100 is equipped with a programmable light source 110 comprising a light-emitting diode (LED) array 112 (also see FIGS. 2-3). Each LED in the LED array 112 is individually addressable and can be used to illuminate a sample S from a corresponding direction. In one example (e.g., see FIGS. 2-3), the LED array 112 is a planar rectangular array in which 256 individual LEDs are arranged in sixteen rows and sixteen columns. In other examples, the LED array 112 may have other numbers of individual LEDs and / or other geometrical patterns in which the LEDs are arranged. Such other examples include, but are not limited to, the following LED arrays: (i) an 8x8 green LED array; (ii) a 32x32 red-green-blue LED array; and (iii) a round LED array in which the LEDs are arranged in concentric circles.

[0024] In additional examples of the microscope 100, other suitable programmable or reconfigurable light sources can also be used to implement the light source 110. In one additional example, a conventional microscope illuminator is outfitted with a slider configured to removably insert a selected spatial filter (e.g., a desirably shaped mask, aperture, or light stop) into the optical path of the illumination beam generated by the illuminator. For darkfield imaging, the selected spatial filter will only pass oblique light and stop (block) all other light, thereby creating a dark field condition in the corresponding image. Differently shaped spatial filters (e.g., differently shaped masks, apertures, or light stops) can then be inserted with the slider into the beam’s optical path to capture a sequence of darkfield images used in the multi-segment darkfield imaging described in more detail below. In another additional example, a digital micromirror device (DMD) can be used as areconfigurable spatial filter to controllably create a variety of oblique illumination patterns by spatially filtering and / or suitably redirecting the illumination beam generated by a microscope illuminator. In yet another additional example, a spatial light modulator, such as an LCOS modulator, can be used to appropriately beamform the illumination beam generated by a microscope illuminator.

[0025] For illustration purposes and without any implied limitations, example embodiments are described below in reference to an example embodiment in which the LED array 112 is used to implement the microscope’s light source. Based on the provided description, a person of ordinary skill in the pertinent art will be able to make and use other embodiments employing other programmable or reconfigurable light sources including, but not limited to, the programmable / reconfigurable light sources mentioned above.

[0026] The microscope 100 also includes an electronic controller 190 configured to control the programmable light source 110 via a control signal 192. In response to the control signal 192, the programmable light source 110 can individually turn ON and OFF each LED of the LED array 112, thereby providing a plurality of various selectable / customizable illumination patterns. For example, a brightfield imaging mode is achieved by turning ON the LEDs in the middle portion of the LED array 112, whereas a darkfield imaging mode uses the LEDs corresponding to the incidence angles that are outside the angular acceptance range set by the numerical aperture (NA) of a microscope objective 120. For illustration purposes, FIG. 1 illustrates a hollow-cone illumination pattern 114 that can be used in the microscope 100 for darkfield imaging in some examples. In general, the programmability of the light source 110 via the control signal 192 beneficially enables the microscope 100 to generate and utilize various additional illumination patterns as well, e.g., as described in more detail below. In some examples, the electronic controller 190 can be used to configure the microscope 100 to operate in different imaging modalities, e.g., selected from the group consisting of darkfield imaging, brightfield imaging, and quantitative phase imaging.

[0027] In the example shown, the sample S is located in a well of a multi-well plate 116 placed at the focal plane of the objective 120, with the LED array 112 being positioned at the Fourier plane thereof. In operation, the objective 120 collects the light received within the collection angle corresponding to its NA and directs the collected light towards a microscope’s tube lens 130. Together, the objective 120 and the tube lens 130 operate to form an image of the sample S on a pixelated detector 140 of a microscope’s camera 150. The pixelated detector 140 operates to capture the formed image, which is then read-out as a corresponding electrical readout signal and transferred via a communication link 152 to the electronic controller 190 for further processing therein, e.g., as described in more detail below.

[0028] In one example, the objective 120 is a 0.25 NA, 10* objective, Model PLN 10* commercially available from Olympus, Japan. The camera 150 is a monochrome 1920*1200 pixels CMOS camera, Model GS3-U3-23S6M-C, commercially available from Teledyne FLIR, UnitedStates. The tube lens 130 is a 180 mm lens. In other examples, other suitable components may similarly be used to implement the microscope 100.

[0029] FIGS. 2A-2D are schematic diagrams illustrating a first set of illumination patterns 202- 208 that can be used in the microscope 100 according to some examples. The illumination patterns 202-208 can be generated, using the appropriately configured control signal 192, by turning ON the corresponding subsets of LEDs in the LED array 112, as indicated in FIGS. 2A-2D. When combined together, the illumination patterns 202-208 represent an approximately circular (or “ring”) band 212 that is concentric with and outside of an intersection circle 210 of the NA acceptance cone of the objective 120 on the emitting side of the LED array 112 (also see the hollow-cone illumination pattern 114, FIG. 1). The illumination pattern 202 represents a first (illustratively top left) quadrant of the ring band 212. The illumination pattern 204 represents a second (illustratively top right) quadrant of the ring band 212. The illumination pattern 206 represents a third (illustratively bottom left) quadrant of the ring band 212. The illumination pattern 208 represents a fourth (illustratively bottom right) quadrant of the ring band 212. For each of the illumination patterns 202-208, the camera 150 of the microscope 100 is operated to capture a respective pixelated image of the sample S. Because each of the illumination patterns 202-208 provides illumination of the sample S from the outside of the acceptance cone of the objective 120 defined by its NA, each of the respective captured images is a respective darkfield image.

[0030] FIGS. 3A-3H are schematic diagrams illustrating a second set of illumination patterns 302- 316 that can be used in the microscope 100 according to some additional examples. The illumination patterns 302-316 can be produced by further subdividing each of the illumination patterns 202-208 into two respective sub-patterns. For example, each of the illumination patterns 302, 304 represents a respective half of the illumination pattern 202 (FIG. 2A). Each of the illumination patterns 306, 308 represents a respective half of the illumination pattern 206 (FIG. 2C), and so on. More generally, each of the illumination patterns 302-316 represents a different respective octant of the ring band 212 described above in reference to FIGS. 2A-2D. For each of the illumination patterns 302-316, the camera 150 of the microscope 100 is operated to capture a respective image of the sample S, with each of the respective captured images being a respective darkfield image.

[0031] In some examples, a set of illumination patterns for the microscope 100 can be obtained by: (i) subdividing the ring band 212 into 2n angular segments, where n is an integer greater than 1 ; and (ii) sequentially turning ON the subsets of LEDs of the LED array 112 located within the different angular segments of the ring band 212 resulting from the subdivision. Under this approach, the first set of illumination patterns 202-208 corresponds to n-2. Similarly, the second set of illumination patterns 302-316 corresponds to n=4. In general, the above-indicated subdivision process may yield n pairs of opposing angular segments, located in diametrically opposite parts of the ring band 212. In some examples, the respective LED subsets corresponding to the two opposing segments of sucha pair are centrosymmetric with respect to the geometric center of the LED array 112 and include the same number of individual LEDs. In some examples, one pair of opposing segments and another pair of opposing segments may have different respective total numbers of individual LEDs included therein. In some examples, a first pair of opposing segments and an adjacent second pair of opposing segments may have one or more LEDs in common. In some other examples, a pair of opposing segments and an immediately adjacent pair of opposing segments may have no LEDs in common.

[0032] FIG. 4 is a flowchart of an image-processing method 400 that can be implemented using the electronic controller 190 of the microscope 100 according to some examples. For illustration purposes and without any implied limitations, some aspects of the method 400 are described in reference to the first (quadrant-based) set of illumination patterns 202-208 illustrated in FIGS. 2A- 2D. Based on the provided description, a person of ordinary skill in the pertinent art will be able to adapt the method 400 to other illumination patterns, without any undue experimentation.

[0033] A block 402 of the method 400 includes the electronic controller 190 receiving, from the camera 150, 2n darkfield images corresponding to a selected set of illumination patterns. In an example corresponding to the first set of illumination patterns 202-208 (FIGS. 2A-2D), the electronic controller 190 receives four darkfield images, hereafter denoted as TL, TR, BL, and BR, respectively. The TL image corresponds to the top-left illumination pattern 202. The TR image corresponds to the top-right illumination pattern 204. The BL image corresponds to the bottom-left illumination pattern 206. The BR image corresponds to the bottom-right illumination pattern 208. The first pair of images consists of the images TL and BR. The second pair of images consists of the images BL and TR.

[0034] A block 404 of the method 400 includes the electronic controller 190 applying one or more preprocessing operations to the 2n darkfield images received in the block 402. In one example, the preprocessing operations include background adjustment for each of the 2n darkfield images, which adjustment may be performed as follows. First, a received image is segmented into a first portion and a second portion. The first portion includes images of various objects present in the sample S within the field of view of the objective 120. The images of such objects are typically represented in the corresponding image frame by relatively “bright” pixels, e.g., pixels whose intensity is greater than a selected threshold value. Depending on the sample S, the first portion may or may not be contiguous. The second portion is the darkfield portion of the image and has a complementary shape to the first portion. In some examples, the shape of the second portion is determined by excluding from the corresponding image frame the images of the objects. Next, a mean darkfield intensity value is computed using the pixel intensities in the second (darkfield) portion of the image frame. The background-adjusted image is then computed by subtracting the mean darkfield intensity from the entire image. This set of background adjustment operations may be applied to each of the 2n darkfield images received in the block 402, thereby resulting in 2n background-adjusted images.

[0035] In some examples, operations of the block 404 may also include one or more additional preprocessing operations. Such additional preprocessing operations may include, but are not limited to, image segmentation, object tracking, polynomial fitting, interpolation, extrapolation, masking, scaling, dynamic range up-mapping or down-mapping, color correction, pixel binning, averaging, smoothing, spatial filtering, intensity clipping, and tone mapping.

[0036] A block 406 of the method 400 includes the electronic controller 190 computing a cumulative darkfield image, hereafter denoted as DF, e.g., as follows:DF = ^ DFi (1) where DF, is the preprocessed (e.g., background-adjusted) darkfield image corresponding to the illumination of the sample S using the / -th angular segment of the ring band 212. Eq. (1) can be understood to represent a sum of the preprocessed darkfield images corresponding to the opposing segments of the ring band 212 used for illumination of the sample S in accordance with the selected set of illumination patterns. For example, for the first set of illumination patterns 202-208 (FIGS. 2A- 2D), Eq. (1) can equivalently be presented as follows:DF = TL' + TR' + BR' + BL' (2) where the “prime” indicates that the corresponding image has been subjected to the preprocessing in the block 404.

[0037] A block 408 of the method 400 includes the electronic controller 190 computing an edge image, E, as follows:E = S”=il^ - ^+nl (3)Eq. (3) can be understood to represent a sum of pairwise differences of the preprocessed darkfield images corresponding to the opposing segments of the ring band 212 used for illumination of the sample S in accordance with the selected set of illumination patterns. For example, for the first set of illumination patterns 202-208 (FIGS. 2A-2D), Eq. (3) can equivalently be presented as follows:E = \TL' - BR'\ + \BL' - TR'\ (4)As explained previously, the images TL and BR correspond to the first pair of opposing quadrants of the ring band 212, while the images BL and TR correspond to the second pair of opposing quadrants of the ring band 212.

[0038] A block 410 of the method 400 includes the electronic controller 190 computing an enhanced darkfield image, EDF, as follows:EDF = c x DF — E (5) where c is a constant or system-specific scaling factor. In various examples, the constant c is in the range between approximately 0.8 and 1.0, depending on the specific implementation of the microscope 100. Eq. (5) can be understood to represent a difference between the scaled cumulative darkfield image computed in the block 406 and the edge image computed in the block 408.

[0039] Note that image additions and subtractions represented by Eqs. (1)-(5) are performed pixelwise.

[0040] FIGS. 5A-5B pictorially illustrate improvements achievable with the microscope 100 and the method 400 according to some examples. More specifically, FIG. 5A shows a conventional darkfield image 502 of a melanoma patient-derived xenograft (PDX) cell (MTG021) acquired with the microscope 100. FIG. 5B shows an enhanced darkfield image 504 of the same melanoma PDX cell as in FIG. 5A obtained with the microscope 100 using the first set of illumination patterns 202-208 (FIGS. 2A-2D) and the corresponding embodiment of the image-processing method 400 (FIG. 4).

[0041] For living cells, such as the melanoma PDX cell imaged in FIGS. 5A-5B, there are typically two main types of light scattering, which are based on the relative size of the cellular feature with respect to the wavelength of light. For features much larger than the wavelength, such as the outer cell membrane, the path of light can be well predicted by classical optics (Snell’s Law, refraction at nonplanar surfaces and / or interfaces, etc.). For features close in size to the wavelength, such as sub-cellular puncta or organelles, the path of light can be explained using the Mie scattering theory. Features of both sizes tend to appear in conventional darkfield images, such as the image 502 (FIG. 5A). However, in such images, the larger features (e.g., cell boundaries) tend to obscure the smaller features (e.g., organelles), especially near the cell edges. In contrast, multi-segment darkfield imaging is able to parse the darkfield signal based on directionality of light, thereby enabling substantial rejection, through image processing, of the darkfield signal originating from the cell boundaries and further enabling cleaner imaging of the sub-cellular features, e.g., as evident from a comparison of the images 502 and 504. One specific benefit of the multi-segment darkfield imaging exemplified by the image 504 is that the total darkfield signal is not affected by the signal from the cell boundary, which enables quantification of total scattering from the smaller features. Another specific benefit of the multi-segment darkfield imaging exemplified by the image 504 is that, in many cases, small features near the cell boundary can be resolved with multi-segment darkfield imaging, but not with conventional darkfield imaging.

[0042] FIG. 6 is a block diagram illustrating a computing device 600 one or more instances of which can be used in or in conjunction with the microscope 100 according to some examples. In one example, the computing device 600 is used to implement the electronic controller 190. In another example, the computing device 600 can be network connected to the electronic controller 190 and configured to run image-processing software implementing the method 400.

[0043] The computing device 600 of FIG. 6 is illustrated as having a number of components, but any one or more of these components may be omitted or duplicated, as suitable for the application and setting. In some embodiments, some or all of the components included in the computing device 600 may be attached to one or more motherboards and enclosed in a housing. In some embodiments, some of those components may be fabricated onto a single system-on-a-chip (SoC)(e.g., the SoC may include one or more electronic processing devices 602 and one or more storage devices 604). Additionally, in various embodiments, the computing device 600 may not include one or more of the components illustrated in FIG. 6, but may include interface circuitry for coupling to the one or more components using any suitable interface (e.g., a Universal Serial Bus (USB) interface, a High-Definition Multimedia Interface (HDMI) interface, a Controller Area Network (CAN) interface, a Serial Peripheral Interface (SPI) interface, an Ethernet interface, a wireless interface, or any other appropriate interface). For example, the computing device 600 may not include a display device 610, but may include display device interface circuitry (e.g., a connector and driver circuitry) to which an external display device 610 may be coupled.

[0044] The computing device 600 includes a processing device 602 (e.g., one or more processing devices). As used herein, the terms “electronic processor device” and “processing device” interchangeably refer to any device or portion of a device that processes electronic data from registers and / or memory to transform that electronic data into other electronic data that may be stored in registers and / or memory. In various embodiments, the processing device 602 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), server processors, or any other suitable processing devices.

[0045] The computing device 600 also includes a storage device 604 (e.g., one or more storage devices). In various embodiments, the storage device 604 may include one or more memory devices, such as random-access memory (RAM) devices (e.g., static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive- bridging RAM (CBRAM) devices), hard drive-based memory devices, solid-state memory devices, networked drives, cloud drives, or any combination of memory devices. In some embodiments, the storage device 604 may include memory that shares a die with the processing device 602. In such an embodiment, the memory may be used as cache memory and include embedded dynamic random-access memory (eDRAM) or spin transfer torque magnetic random-access memory (STT- MRAM), for example. In some embodiments, the storage device 604 may include non-transitory computer readable media having instructions thereon that, when executed by one or more processing devices (e.g., the processing device 602), cause the computing device 600 to perform any appropriate ones of the methods disclosed herein below or portions of such methods.

[0046] The computing device 600 further includes an interface device 606 (e.g., one or more interface devices 606). In various embodiments, the interface device 606 may include one or more communication chips, connectors, and / or other hardware and software to govern communications between the computing device 600 and other computing devices. For example, the interface device 606 may include circuitry for managing wireless communications for the transfer of data to and from the computing device 600. The term “wireless” and its derivatives may be used to describe circuits,devices, systems, methods, techniques, communications channels, etc., that may communicate data via modulated electromagnetic radiation through a nonsolid medium. The term does not imply that the associated devices do not contain any wires, although in some embodiments they might not. Circuitry included in the interface device 606 for managing wireless communications may implement any of a number of wireless standards or protocols, including but not limited to Institute for Electrical and Electronic Engineers (IEEE) standards including Wi-Fi (IEEE 802.11 family), IEEE 802.16 standards, Long-Term Evolution (LTE) project along with any amendments, updates, and / or revisions (e.g., advanced LTE project, ultramobile broadband (UMB) project (also referred to as “3GPP2”), etc.). In some embodiments, circuitry included in the interface device 606 for managing wireless communications may operate in accordance with a Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE network. In some embodiments, circuitry included in the interface device 606 for managing wireless communications may operate in accordance with Enhanced Data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some embodiments, circuitry included in the interface device 606 for managing wireless communications may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution-Data Optimized (EV-DO), and derivatives thereof, as well as any other wireless protocols that are designated as 3G, 4G, 5G, and beyond. In some embodiments, the interface device 606 may include one or more antennas (e.g., one or more antenna arrays) configured to receive and / or transmit wireless signals.

[0047] In some embodiments, the interface device 606 may include circuitry for managing wired communications, such as electrical, optical, or any other suitable communication protocols. For example, the interface device 606 may include circuitry to support communications in accordance with Ethernet technologies. In some embodiments, the interface device 606 may support both wireless and wired communication, and / or may support multiple wired communication protocols and / or multiple wireless communication protocols. For example, a first set of circuitry of the interface device 606 may be dedicated to shorter-range wireless communications such as Wi-Fi or Bluetooth, and a second set of circuitry of the interface device 606 may be dedicated to longer-range wireless communications such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV- DO, or others. In some other embodiments, a first set of circuitry of the interface device 606 may be dedicated to wireless communications, and a second set of circuitry of the interface device 606 may be dedicated to wired communications.

[0048] The computing device 600 also includes battery / power circuitry 608. In various embodiments, the battery / power circuitry 608 may include one or more energy storage devices (e.g.,batteries or capacitors) and / or circuitry for coupling components of the computing device 600 to an energy source separate from the computing device 600 (e.g., to AC line power).

[0049] The computing device 600 also includes a display device 610 (e.g., one or multiple individual display devices). In various embodiments, the display device 610 may include any visual indicators, such as a heads-up display, a computer monitor, a projector, a touchscreen display, a liquid crystal display (LCD), a light-emitting diode display, or a flat panel display.

[0050] The computing device 600 also includes additional input / output (I / O) devices 612. In various embodiments, the I / O devices 612 may include one or more data / signal transfer interfaces, audio I / O devices (e.g., microphones or microphone arrays, speakers, headsets, earbuds, alarms, etc.), audio codecs, video codecs, printers, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, etc.), image capture devices (e.g., one or more cameras), human interface devices (e.g., keyboards, cursor control devices, such as a mouse, a stylus, a trackball, or a touchpad), etc.

[0051] Depending on the specific embodiment, various components of the interface devices 606 and / or I / O devices 612 can be configured to output suitable control signals, receive suitable control / telemetry signals, and receive and transmit data streams. In some examples, the interface devices 606 and / or I / O devices 612 include one or more analog-to-digital converters (ADCs) for transforming received analog signals into a digital form suitable for operations performed by the processing device 602 and / or the storage device 604. In some additional examples, the interface devices 606 and / or I / O devices 612 include one or more digital-to-analog converters (DACs) for transforming digital signals provided by the processing device 602 and / or the storage device 604 into an analog form suitable for being transmitted through a communication channel.

[0052] According to an example embodiment disclosed above, e.g., in the summary section and / or in reference to any one or any combination of some or all of FIGS. 1-6, provided is an optical apparatus comprising: an objective configured to project an image of a sample onto a pixelated light detector; a light source configured to illuminate the sample from outside a numerical-aperture cone of the objective to create a dark field condition in the image; and a computing device configured to: control the light source to sequentially activate different ones of 2n angular segments thereof around the numerical-aperture cone and cause the pixelated light detector to capture a corresponding sequence of 2n first darkfield images (e.g., DFi) of the sample, where n is an integer greater than one; and compute a second darkfield image (e.g., EDF) of the sample based on n pairwise differences between the first darkfield images in the sequence corresponding to opposing ones of the 2n angular segments.

[0053] In some embodiments of the above apparatus, n < 32.

[0054] In some embodiments of any of the above apparatus, the different ones of the 2n angular segments do not overlap.

[0055] In some embodiments of any of the above apparatus, the 2n angular segments form a contiguous band around the numerical-aperture cone.

[0056] In some embodiments of any of the above apparatus, the light source comprises an array of individually controllable light-emitting diodes (LEDs); and wherein the different ones of the 2n angular segments have different respective subsets of the LEDs.

[0057] In some embodiments of any of the above apparatus, a first subset of LEDs and a second subset of LEDs corresponding to a pair of opposing angular segments are centrosymmetric with respect to a geometric center of the array.

[0058] In some embodiments of any of the above apparatus, a first pair of opposing angular segments has a first total number of LEDs; and wherein a second pair of opposing angular segments has a different second total number of LEDs.

[0059] In some embodiments of any of the above apparatus, a first pair of opposing angular segments and an immediately adjacent second pair of opposing angular segments have no LEDs in common.

[0060] In some embodiments of any of the above apparatus, the computing device is further configured to compute the second darkfield image based on third darkfield image (e.g., E) computed as a sum of the n pairwise differences.

[0061] In some embodiments of any of the above apparatus, the computing device is further configured to compute the second darkfield image based on a fourth darkfield image (e.g., DF) computed as a sum of the 2n first darkfield images.

[0062] In some embodiments of any of the above apparatus, the computing device is further configured to: apply a scaling factor to the fourth darkfield image; and compute the second darkfield image based on a difference between the scaled fourth darkfield image and the third darkfield image.

[0063] In some embodiments of any of the above apparatus, the optical system is switchable via the computing device to operate in an imaging modality selected from the group consisting of darkfield imaging, brightfield imaging, and quantitative phase imaging.

[0064] According to another example embodiment disclosed above, e.g., in the summary section and / or in reference to any one or any combination of some or all of FIGS. 1-6, provided is an automated method performed via a computing device for providing support to an optical microscope, the method comprising: receiving an image set including 2n first darkfield images (e.g., DF / ) of a sample acquired with the optical microscope by sequentially activating different ones of 2n angular segments of a light source around a numerical-aperture cone of a microscope objective, where n is an integer greater than one; computing n pairwise differences between the first darkfield images in the image set corresponding to opposing ones of the 2n angular segments; and computing a second darkfield image (e.g., EDF) of the sample based on the n pairwise differences.

[0065] In some embodiments of the above method, the method further comprises computing a third darkfield image (e.g., E) using a sum of the n pairwise differences, wherein the second darkfield image is computed further based on the third darkfield image.

[0066] In some embodiments of any of the above methods, the method further comprises computing a fourth darkfield image (e.g., DF) computed using a sum of the 2n first darkfield images, wherein the second darkfield image is computed further based on the fourth darkfield image.

[0067] In some embodiments of any of the above methods, the method further comprises applying a scaling factor to the fourth darkfield image, wherein computing the second darkfield image comprises computing a difference between the scaled fourth darkfield image and the third darkfield image.

[0068] In some embodiments of any of the above methods, the method further comprises displaying one or more of the first, second, third, and fourth darkfield images on a display device.

[0069] In some embodiments of any of the above methods, the method further comprises applying one or more preprocessing operations to at least some of the 2n first darkfield images prior to the computing, the one or more preprocessing operations being selected from the group consisting of background adjustment, image segmentation, object tracking, polynomial fitting, interpolation, extrapolation, masking, scaling, dynamic range up-mapping or down-mapping, color correction, pixel binning, averaging, smoothing, spatial filtering, intensity clipping, and tone mapping.

[0070] In some embodiments of any of the above methods, the method further comprises applying a set of preprocessing operations to at least some the 2n first darkfield images prior to the computing, the set of preprocessing operations including: applying image segmentation to segment an image into a first portion and a second portion, the first portion including one or more images of objects present in the sample within a field of view, the second portion being a darkfield portion; computing a mean intensity corresponding to the second portion of the image; and adjusting pixel intensities in the image using the mean intensity.

[0071] According to yet another example embodiment disclosed above, e.g., in the summary section and / or in reference to any one or any combination of some or all of FIGS. 1-6, provided is a non-transitory computer-readable medium storing instructions that, when executed by an electronic processor, cause the electronic processor to perform operations comprising any one of the above methods.

[0072] With regard to the processes, systems, methods, heuristics, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of processesherein are provided for the purpose of illustrating certain embodiments and should in no way be construed so as to limit the claims.

[0073] Accordingly, it is to be understood that the above description is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent upon reading the above description. The scope should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the technologies discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the application is capable of modification and variation.

[0074] All terms used in the claims are intended to be given their broadest reasonable constructions and their ordinary meanings as understood by those knowledgeable in the technologies described herein unless an explicit indication to the contrary is made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary.

[0075] The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments incorporate more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in fewer than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.

[0076] While this disclosure includes references to illustrative embodiments, this specification is not intended to be construed in a limiting sense. Various modifications of the described embodiments, as well as other embodiments within the scope of the disclosure, which are apparent to persons skilled in the art to which the disclosure pertains are deemed to lie within the principle and scope of the disclosure, e.g., as expressed in the following claims.

[0077] Some embodiments may be implemented as circuit-based processes, including possible implementation on a single integrated circuit.

[0078] Some embodiments can be embodied in the form of methods and apparatuses for practicing those methods. Some embodiments can also be embodied in the form of program code recorded in tangible media, such as magnetic recording media, optical recording media, solid state memory, floppy diskettes, CD-ROMs, hard drives, or any other non-transitory machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, suchas a computer, the machine becomes an apparatus for practicing the patented invention(s). Some embodiments can also be embodied in the form of program code, for example, stored in a non- transitory machine-readable storage medium including being loaded into and / or executed by a machine, wherein, when the program code is loaded into and executed by a machine, such as a computer or a processor, the machine becomes an apparatus for practicing the patented invention(s). When implemented on a general-purpose processor, the program code segments combine with the processor to provide a unique device that operates analogously to specific logic circuits.

[0079] Unless explicitly stated otherwise, each numerical value and range should be interpreted as being approximate as if the word “about” or “approximately” preceded the value or range.

[0080] The use of figure numbers and / or figure reference labels in the claims is intended to identify one or more possible embodiments of the claimed subject matter in order to facilitate the interpretation of the claims. Such use is not to be construed as necessarily limiting the scope of those claims to the embodiments shown in the corresponding figures.

[0081] Although the elements in the following method claims, if any, are recited in a particular sequence with corresponding labeling, unless the claim recitations otherwise imply a particular sequence for implementing some or all of those elements, those elements are not necessarily intended to be limited to being implemented in that particular sequence.

[0082] Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments necessarily mutually exclusive of other embodiments. The same applies to the term “implementation.”

[0083] Unless otherwise specified herein, the use of the ordinal adjectives “first,” “second,” “third,” etc., to refer to an object of a plurality of like objects merely indicates that different instances of such like objects are being referred to, and is not intended to imply that the like objects so referred-to have to be in a corresponding order or sequence, either temporally, spatially, in ranking, or in any other manner.

[0084] Unless otherwise specified herein, in addition to its plain meaning, the conjunction “if” may also or alternatively be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” which construal may depend on the corresponding specific context. For example, the phrase “if it is determined” or “if [a stated condition] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event].”

[0085] Also, for purposes of this description, the terms “couple,” “coupling,” “coupled,” “connect,” “connecting,” or “connected” refer to any manner known in the art or later developed in which energyis allowed to be transferred between two or more elements, and the interposition of one or more additional elements is contemplated, although not required. Conversely, the terms “directly coupled,” “directly connected,” etc., imply the absence of such additional elements.

[0086] As used herein in reference to an element and a standard, the term compatible means that the element communicates with other elements in a manner wholly or partially specified by the standard and would be recognized by other elements as sufficiently capable of communicating with the other elements in the manner specified by the standard. The compatible element does not need to operate internally in a manner specified by the standard.

[0087] The functions of the various elements shown in the figures, including any functional blocks labeled as “processors” and / or “controllers,” may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and nonvolatile storage. Other hardware, conventional and / or custom, may also be included. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.

[0088] As used in this application, the terms “circuit,” “circuitry” may refer to one or more or all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and / or digital circuitry); (b) combinations of hardware circuits and software, such as (as applicable): (i) a combination of analog and / or digital hardware circuit(s) with software / firmware and (ii) any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions); and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (e.g., firmware) for operation, but the software may not be present when it is not needed for operation.” This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor (or multiple processors) or portion of a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the particular claim element, a baseband integrated circuit or processor integrated circuit for a mobiledevice or a similar integrated circuit in server, a cellular network device, or other computing or network device.

[0089] It should be appreciated by those of ordinary skill in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

[0090] Any numerical range recited herein includes all values from the lower value to the upper value. For example, if a range is stated as 1 % to 50%, it is intended that the narrower ranges thereof, such as 2% to 40%, 10% to 30%, 1 % to 3%, etc., are expressly enumerated by said statement. These specific examples represent only a limited subset of what is intended to be covered, and all possible combinations of numerical values between and including the lowest value and the highest value of the enumerated range are to be considered to be expressly stated in this application. Concentration ranges, pH ranges, and other ranges of specific parameters are intended to be interpreted in a manner similar to the “%” example.

[0091] The modifier “about” or “approximately” used in connection with a quantity is inclusive of the stated value and has the meaning dictated by the context (for example, it includes at least the degree of error associated with the measurement of the particular quantity). The modifier “about” or “approximately” should also be considered as disclosing the range defined by the absolute values of the two endpoints. For example, the expression “from about 2 to about 4” also discloses the range “from 2 to 4.” The term “about” may refer to plus or minus 10% of the indicated number. For example, “about 10%” may indicate a range of 9% to 11%, and “about 1” may mean from 0.9-1.1. Other meanings of “about” may be apparent from the context, such as rounding off, so that, for example, “about 1” may also mean from 0.5 to 1.4.

[0092] For purposes of this disclosure, the chemical elements are identified in accordance with the Periodic Table of the Elements, CAS version, Handbook of Chemistry and Physics, 75th Ed., inside cover, and specific functional groups are generally defined as described therein. Additionally, the present disclosure relies on general principles of organic chemistry, inorganic chemistry, and material science, as accepted in the pertinent arts. For example, specific functional moieties and reactivity in accordance with some of such principles are described in Organic Chemistry, Thomas Sorrell, University Science Books, Sausalito, 1999; Smith and March, March's Advanced Organic Chemistry, 5th Edition, John Wiley & Sons, Inc., New York, 2001 ; Larock, Comprehensive Organic Transformations, VCH Publishers, Inc., New York, 1989; Carruthers, Some Modern Methods of Organic Synthesis, 3rd Edition, Cambridge University Press, Cambridge, 1987, the entire contents of each of which are incorporated herein by reference.

[0093] “BRIEF SUMMARY OF SOME SPECIFIC EMBODIMENTS” in this specification is intended to introduce some example embodiments, with additional embodiments being described in “DETAILED DESCRIPTION” and / or in reference to one or more drawings. “BRIEF SUMMARY OF SOME SPECIFIC EMBODIMENTS” is not intended to identify essential elements or features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter.

Claims

026389-0055-W001CLAIMSWhat is claimed is:

1. An optical system, comprising: an objective configured to project an image of a sample onto a pixelated light detector; a light source configured to illuminate the sample from outside a numerical-aperture cone of the objective to create a dark field condition in the image; and a computing device configured to: control the light source to sequentially activate different ones of 2n angular segments thereof around the numerical-aperture cone and cause the pixelated light detector to capture a corresponding sequence of 2n first darkfield images of the sample, where n is an integer greater than one; and compute a second darkfield image of the sample based on n pairwise differences between the first darkfield images of the sequence corresponding to opposing ones of the 2n angular segments.

2. The optical system of claim 1 , wherein n < 32.

3. The optical system of claim 1, wherein the different ones of the 2n angular segments do not overlap.

4. The optical system of claim 1 , wherein the 2n angular segments form a contiguous band around the numerical-aperture cone.

5. The optical system of claim 1 , wherein the light source comprises an array of individually controllable light-emitting diodes (LEDs); and wherein the different ones of the 2n angular segments have different respective subsets of the LEDs.

6. The optical system of claim 5, wherein a first subset of LEDs and a second subset of LEDs corresponding to a pair of opposing angular segments are centrosymmetric with respect to a geometric center of the array.

7. The optical system of claim 5, wherein a first pair of opposing angular segments has a first total number of LEDs; and026389-0055-W001 wherein a second pair of opposing angular segments has a different second total number of LEDs.

8. The optical system of claim 5, wherein a first pair of opposing angular segments and an immediately adjacent second pair of opposing angular segments have no LEDs in common.

9. The optical system of claim 1 , wherein the computing device is further configured to compute the second darkfield image based on a third darkfield image computed as a sum of the n pairwise differences.

10. The optical system of claim 9, wherein the computing device is further configured to compute the second darkfield image based on a fourth darkfield image computed as a sum of the 2n first darkfield images.

11. The optical system of claim 10, wherein the computing device is further configured to: apply a scaling factor to the fourth darkfield image; and compute the second darkfield image based on a difference between the scaled fourth darkfield image and the third darkfield image.

12. The optical system of claim 1 , wherein the optical system is switchable via the computing device to operate in an imaging modality selected from the group consisting of darkfield imaging, brightfield imaging, and quantitative phase imaging.

13. An automated method performed via a computing device for providing support to an optical microscope, the method comprising: receiving an image set including 2n first darkfield images of a sample acquired with the optical microscope by sequentially activating different ones of 2n angular segments of a light source around a numerical-aperture cone of a microscope objective, where n is an integer greater than one; computing n pairwise differences between the first darkfield images in the image set corresponding to opposing ones of the 2n angular segments; and computing a second darkfield image of the sample based on the n pairwise differences.

14. The automated method of claim 13, further comprising computing a third darkfield image using a sum of the n pairwise differences, wherein the second darkfield image is computed further based on the third darkfield image.

15. The automated method of claim 14, further comprising computing a fourth darkfield image computed using a sum of the 2n first darkfield images, wherein the second darkfield image is computed further based on the fourth darkfield image.

16. The automated method of claim 15, further comprising applying a scaling factor to the fourth darkfield image, wherein computing the second darkfield image comprises computing a difference between the scaled fourth darkfield image and the third darkfield image.

17. The automated method of claim 15, further comprising displaying one or more of the first, second, third, and fourth darkfield images on a display device.

18. The automated method of claim 13, further comprising applying one or more preprocessing operations to at least some of the 2n first darkfield images prior to the computing, the one or more preprocessing operations being selected from the group consisting of background adjustment, image segmentation, object tracking, polynomial fitting, interpolation, extrapolation, masking, scaling, dynamic range up-mapping or down-mapping, color correction, pixel binning, averaging, smoothing, spatial filtering, intensity clipping, and tone mapping.

19. The automated method of claim 13, further comprising applying a set of preprocessing operations to at least some the 2n first darkfield images prior to the computing, the set of preprocessing operations including: applying image segmentation to segment an image into a first portion and a second portion, the first portion including one or more images of objects present in the sample within a field of view, the second portion being a darkfield portion; computing a mean intensity corresponding to the second portion of the image; and adjusting pixel intensities in the image using the mean intensity.

20. A non-transitory computer-readable medium storing instructions that, when executed by an electronic processor, cause the electronic processor to perform operations comprising the method of any one of claims 13-19.

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