Automated prescan artifact detection and validation

Abstract

A method of detecting prescan artifacts includes capturing a prescan of a tissue-containing slide, generating a first intensity profile of the image data of the prescan, retrieving a second intensity profile for a default prescan, generating an intensity ratio profile based on the first intensity profile and the second intensity profile, determining that an average of intensity ratios within a window of pixel positions of the intensity ratio profile is at least one of greater than an upper threshold value and less than a lower threshold value, and, in response to determining that the average of intensity ratios is at least one of greater than the upper threshold value and less than the lower threshold value, determining that the prescan includes an artifact.

Description

[0001] 20292-P-WO-D0485-P15754WO01

[0002] AUTOMATED PRESCAN ARTIFACT DETECTION AND VALIDATION

[0003] FIELD OF THE INVENTION

[0004] The present disclosure relates to automated evaluation and validation of prescan image data and, more particularly, to systems and methods for automated artifact detection in prescan image data and, further, to systems and methods for automated validation of prescan data for use in image correction (e.g. flat-field correction and white balancing).

[0005] BACKGROUND

[0006] Histological image scanners are often equipped with high-resolution image sensors capable of capturing digital images of entire histological slides. Histological slides digitized in this manner can be saved for later review and inspection by a human operator. Histological scanners can also use an objective lens to increase the magnification of images captured of histological slides.

[0007] SUMMARY

[0008] An example of a method of detecting prescan artifacts includes capturing a prescan of a tissue-containing slide, generating a first intensity profile of the image data of the prescan, retrieving a second intensity profile for a default prescan, generating an intensity ratio profile based on the first intensity profile and the second intensity profile, determining that an average of intensity ratios within a window of pixel positions of the intensity ratio profile is at least one of greater than an upper threshold value and less than a lower threshold value, and, in response to determining that the average of intensity ratios is at least one of greater than the upper threshold value and less than the lower threshold value, determining that the prescan includes an artifact. The prescan includes image data of at least a portion of the tissue-containing slide, the image data of the prescan includes a first dimension and a second dimension, and the first intensity profile includes averages of pixel intensities for pixels of the first dimension of the image data of the prescan for each pixel position of the second dimension of the image data of the prescan. The default prescan includes image data of at least a portion of a different slide, the image data of the default prescan includes the first dimension and the second dimension, and the second intensity profile includes averages of pixel intensities for pixels of the first dimension of the default prescan for each pixel position of the second dimension of the default prescan.

[0009] An example of a system for detecting prescan artifacts includes an imaging system, a processor operatively coupled to the imaging system, and at least one memory encoded with instructions. The instructions, when executed, cause the processor to cause the imaging system to capture a prescan of a tissue-containing slide, generate a first intensity profile of the image data of the prescan, retrieve a second intensity profile for a default prescan, generate an intensity ratio profile based on the first intensity profile and the second intensity profile, determine that an average of intensity ratios within a window of pixel positions of the intensity ratio profile is at least one of greater than an upper threshold value and less than a lower threshold value, and, in response to determining that the average is at least one of greater than the upper threshold value and less than the lower threshold value, determine that the prescan includes an artifact. The prescan includes image data of at least a portion of the tissue-containing slide, the image data of the prescan includes a first dimension and a second dimension, and the first intensity profile includes averages of pixel intensities for pixels of the first dimension of the image data of the prescan for each pixel position of the second dimension of the image data of the prescan. The default prescan includes image data of at least a portion of a different slide, the image data of the default prescan includes the first dimension and the second dimension, and the second intensity profile includes averages of pixel intensities for pixels of the first dimension of the default prescan for each pixel position of the second dimension of the default prescan.

[0010] An example of a method of validating a default prescan image includes capturing a first prescan of a first tissue-containing slide and generating an intensity profile of the image data of the first prescan. The first prescan includes image data of at least a portion of the first tissue-containing slide, the image data of the first prescan includes a first dimension and a second dimension, the intensity profile includes averages of pixel intensities for pixels of the first dimension of the first prescan for each pixel position of the second dimension of the first prescan. The method further includes generating an average pixel intensity value for a range of pixel positions of the second dimension, determining that the average pixel intensity is within an acceptable pixel intensity range defined by a maximum acceptable intensity and a minimum acceptable intensity, and, in response to determining that the average pixel intensity is within the acceptable pixel intensity range, fitting a polynomial curve to the intensity profile. The range spans a center of pixel positions of the second dimension. The method further includes determining that a tilt of the polynomial curve is within an acceptable tilt range, determining that a curvature of the polynomial curve is within an acceptable curvature range, determining that a smoothness of the polynomial curve is within an acceptable smoothness range, and, in response to determining that the tilt of the polynomial curve is within the acceptable tilt range, determining that the curvature of the polynomial curve is within the acceptable curvature range, and the smoothness is within the acceptable smoothness range, validating the first prescan as the default prescan image.

[0011] The present summary is provided only by way of example, and not limitation. Other aspects of the present disclosure will be appreciated in view of the entirety of the present disclosure, including the entire text, claims, and accompanying figures.

[0012] BRIEF DESCRIPTION OF THE DRAWINGS

[0013] FIG. 1 is a front view of an example of an image analysis system with an expanded schematic diagram illustrating a system for evaluating prescan images, for selecting default prescan images, and for correcting image scans using prescan data.

[0014] FIG. 2 is a schematic diagram illustrating the system of FIG. 1 with components of an image scanner shown in greater detail.

[0015] FIG. 3 is a schematic depiction of an example of an overview image of a tissue-containing slide as well as an example of a prescan image of a region of the tissuecontaining slide.

[0016] FIG. 4 is an example of a microscope image depicting striping caused by an artifact-containing prescan.

[0017] FIG. 5A is an example of a microscope image of a prescan containing no artifacts.

[0018] FIG. 5B is an example of a microscope image of a prescan containing an artifact.

[0019] FIG. 5C is another example of a microscope image of a prescan containing an artifact.

[0020] FIG. 5D is yet a further example of a microscope image of a prescan containing an artifact.

[0021] FIG. 6 is a flow diagram of an example of a method of determining whether to use a default prescan or a prescan taken from a current slide, as well as of using a prescan to correct a slide scan.

[0022] FIG. 7 is a flow diagram of an example of a method of identifying a prescan suitable for use as a default prescan and, further, of using a default prescan to correct a slide scan.

[0023] FIG. 8 is an intensity profile including a polynomial curve fit to the intensity profile data

[0024] FIG. 9A is a graph of an example of an intensity ratio profile. FIG. 9B is a microscope image of a prescan corresponding to the example of an intensity ratio profile of FIG. 9A.

[0025] FIG. 10A is a graph of another example of an intensity ratio profile.

[0026] FIG. 1 OB is a microscope image of a prescan corresponding to the example of an intensity ratio profile of FIG. 10A.

[0027] FIG. 11 A is a block diagram of an example of a line scan camera having a single linear array.

[0028] FIG. 1 IB is a block diagram of an example of a line scan camera having a color array.

[0029] FIG. 11C is a block diagram of an example of a line scan camera having a time delay integration (TDI) array.

[0030] While the above-identified figures set forth one or more examples of the present disclosure, other examples are also contemplated, as noted in the discussion. In all cases, this disclosure presents the invention by way of representation and not limitation. It should be understood that numerous other modifications and examples can be devised by those skilled in the art, which fall within the scope and spirit of the principles of the invention. The figures may not be drawn to scale, and applications and examples of the present invention may include features and components not specifically shown in the drawings.

[0031] DETAILED DESCRIPTION

[0032] The present disclosure relates to systems and methods for evaluation and validation of prescan image data captured by slide scanners used for digital pathology and / or histology. As referred to herein, a “prescan” refers to image data that is taken separately from (e.g., prior to) a full image scan of a slide and that is used for image correction of the full slide scan, such as corrections to illumination and white-balancing. Defects in illumination uniformity are generally specific to a particular slide imaging apparatus (e.g., image scanner 160 of FIG. 1, discussed subsequently) and can be corrected using a prescan image taken using the same slide imaging apparatus. White-balancing, conversely, can be affected by slide-specific factors, such as slide thickness, amount and color of residual stain, glass composition, etc., in addition to factors specific to the slide imaging apparatus, such as illumination source color temperature, camera spectral response, etc. As such, it is desirable for a prescan to capture a blank portion of the slide that is being imaged using the same imaging apparatus that will be used for the full slide scan, such that the prescan can be used to accurately correct for nonuniformities owed to the camera sensor and, further, to enable slide- specific white-balancing, among other corrections that can be made.

[0033] As will be explained in more detail herein, the present disclosure provides methods of automated evaluation of slide-specific prescans (i.e., prescans taken from a slide for which a full image scan will be performed or is otherwise desired) for artifacts. An artifact in a prescan can introduce significant artifacts during image correction that reduce the ability of human operators to interpret and use the slide image for downstream purposes (e.g., diagnostic purposes). When an artifact is detected by the systems and methods disclosed herein, a default prescan can be used in place of the slide-specific prescan.

[0034] A “default prescan” as referred to herein is a prescan that is suitable to be used in place of a slide-specific prescan. A default prescan is a prescan validated in an individual imaging apparatus as not containing tissue, debris, dust, etc. such that the default prescan will not introduce artifacts or other errors when used for image correction. One type of artifact introduced by image correction using an artifact-containing prescan is the appearance of regularly-spaced white lines, which is discussed in more detail subsequently and particularly with reference to FIG. 4. Further, as will be explained in more detail subsequently, the present disclosure provides a method of also validating that brightness of the prescan is sufficiently uniform such that the prescan can be used to correct slide images for other slides taken with the same imaging apparatus.

[0035] To this extent, it is desirable to use a slide-specific prescan when that prescan is sufficiently artifact- or defect-free, as the slide-specific prescan is able to provide illumination correction and, further, more accurate white-balancing than a default prescan of a different slide. However, when the slide-specific prescan includes a visual artifact, a default prescan can be used for illumination correction and can provide some degree of white-balancing correction without introducing further artifacts or defects to the corrected image. As will be explained in more detail subsequently, the systems and methods described herein advantageously enable the automated detection of artifacts in slidespecific prescans and, further, enable automated determinations of whether to use slidespecific or default prescans based on the presence of artifacts in a slide-specific prescan.

[0036] FIGS. 1-2 are schematic diagrams of image analysis system 10, which is a system for training and using computer-implemented machine-learning models to evaluate the focus of histological images. Image analysis system 10 includes system controller 100, user interface 106, image scanner 160, and sample carousel 170. FIG. 1 illustrates elements of system controller 100, user interface 106, and sample carousel 170 in detail. FIG. 2 illustrates elements of image scanner 160 in detail. FIGS. 1-2 are discussed together herein.

[0037] System controller 100 includes processor 102 and memory 104, and further includes, is operatively connected to, and / or is otherwise in electronic communication with user interface 106. Memory 104 stores image capture module 110, intensity profile generation module 120, artifact detection module 130, default prescan validation module 140, and image correction module 150. Sample carousel 170 includes slides 171. Image scanner 160 is configured to receive a glass slide (e.g., one of glass slides 171) and includes communication bus 172, motion controller 174, interface system 176, stage 178, sample 182, illumination system 184, objective lens 186, optical path 188, focusing optics 190, line scan camera 192, camera 194, field of view 196, objective lens positioner 198, stage positioner 199, and epi-illumination system 200. In the depicted example, image scanner 160 receives glass slide 171A. FIG. 2 also depicts arrows 197, which indicate the general direction which light travels along an optical path from sample 182 to line scan camera 192 and / or camera 194, as well as arrow X, arrow Y, and arrow Z, which indicate X-coordinate, Y-coordinate, and Z-coordinate directions, respectively.

[0038] System controller 100 is operatively connected to and / or is otherwise in electronic communication with image scanner 160 and sample carousel 170, such that system controller 100 is able to control operation of image scanner 160 and sample carousel 170. As will be explained in more detail subsequently, system controller 100 can cause sample carousel 170 to load slides (e.g., glass slide 171 A depicted in FIG. 2) onto stage 178 and to unload slides from stage 178 to sample carousel 170. Further, and as will also be explained in more detail subsequently, system controller 100 can also cause image scanner 160 to image slides located on stage 178.

[0039] Processor 102 can execute software, applications, and / or programs stored on memory 104. Examples of processor 102 can include one or more of a processor, a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other equivalent discrete or integrated logic circuitry. Processor 102 can be entirely or partially mounted on one or more circuit boards.

[0040] Memory 104 is configured to store information and, in some examples, can be described as a computer-readable storage medium. Memory 104, in some examples, is described as computer-readable storage media. In some examples, a computer-readable storage medium can include a non-transitory medium. The term “non-transitory’' can indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non- transitory storage medium can store data that can, over time, change (e.g., in RAM or cache). In some examples, memory 104 is a temporary memory. As used herein, a temporary memory refers to a memory having a primary purpose that is not long-term storage. Memory 104, in some examples, is described as volatile memory. As used herein, a volatile memory refers to a memory that that the memory does not maintain stored contents when power to the memory 104 is turned off. Examples of volatile memories can include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories. In some examples, memory 104 is used to store program instructions for execution by processor 102. Memory 104, in one example, is used by software or applications running on system controller 100 (e.g., by a computer-implemented machine-learning model) to temporarily store information during program execution.

[0041] Memory 104, in some examples, also includes one or more computer- readable storage media. The storage media can be configured to store larger amounts of information than volatile memory and, further, can be configured for long-term storage of information. In some examples, memory 104 includes non-volatile storage elements. Examples of such non-volatile storage elements can include, for example, magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.

[0042] User interface 106 is an input and / or output device and / or software interface, and enables an operator to control operation of and / or interact with software elements of image analysis system 10, and the components thereof. For example, user interface 106 can be configured to receive inputs from an operator and / or provide outputs. User interface 106 can include one or more of a sound card, a video graphics card, a speaker, a display device (such as a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, etc.), a touchscreen, a keyboard, a mouse, a joystick, or other type of device for facilitating input and / or output of information in a form understandable to users and / or machines.

[0043] In the depicted example, user interface 106 is mounted to a front of the body of image analysis system 10 adjacent to sample carousel 170. In other examples, user interface 106 can be positioned elsewhere on image analysis system 10, including on separate hardware such as a remote device. Further, in the depicted example, user interface 106 includes a graphical display including one or more icons that a user can select or otherwise interact with (e.g., via a pointer, via a touchscreen interface, etc.) to control the operation of image analysis system 10.

[0044] In some examples, system controller 100 can operate an application programming interface (API) (e.g., as a software component of user interface 106 or as another software component of system controller 100) for facilitating communication between system controller 100 and other components of image analysis system 10 and / or for facilitating communication between image analysis system 10 and other devices connected to image analysis system 10.

[0045] Image scanner 160 is a digital imaging device for imaging histological samples. In some examples, image scanner can be referred to as an imaging apparatus, a scanner system, a scanning system, a scanning apparatus, a digital scanning apparatus, or a digital slide scanning apparatus. Image scanner 160 can be used to create image scans of histological slides for use by the programs of system controller 100.

[0046] The electronic devices and / or components of image scanner 160 are communicatively connected via communication bus 172. In particular, communication bus 172 connects to system controller 100, motion controller 174, interface system 176, illumination system 184, line scan camera 192, camera 194, objective lens positioner 198, stage positioner 199, and epi- illumination system 200. Communication bus 172 can include one or more physical ports, physical connectors, etc. and / or one or more digital interfaces, digital connectors, etc. for facilitating communication between devices, components, etc. connected to communication bus 172. Communication bus 172 can be configured to convey analog electrical signals and / or to convey digital data. Accordingly, electronic communications mediated by communication bus 172 may include both electrical signals and digital data. In some examples, communication bus 172 can also include one or more components for wireless communication, such that one or more of system controller 100, motion controller 174, interface system 176, illumination system 184, line scan camera 192, camera 194, objective lens positioner 198, stage positioner 199, and epi-illumination system 200 are connected to communication bus 172 via a wireless connection. Image scanner 160 is depicted as including a single communication bus, but in other examples, image scanner 160 can include any suitable number of communication busses for communicatively coupling the electronic devices and / or components of image scanner 160.

[0047] Motion controller 174 is configured to precisely control and coordinate X, Y, and / or Z movement of stage 178 (via stage positioner 199) and / or objective lens 186 (via objective lens positioner 198). Motion controller 174 can also be configured to control movement of any other moving part of image analysis system 10. For example, where image scanner 160 is a fluorescence scanner, motion controller 174 can be configured to coordinate movement of optical filters and related structures of epi-illumination system 200.

[0048] Interface system 176 includes one or more software and / or hardware elements for facilitating communication and data transfer between the image analysis system 10 and one or more external devices that are connected to image analysis system 10. The external devices can include, for example, one or more external devices that are directly connected to image analysis system 10 or any components thereof (e.g., a printer, removable storage medium, etc.) and / or one or more external devices that are connected via a network to image analysis system 10 or any components thereof (e.g., an image server system, an operator station, a user station, an administrative server system, etc.).

[0049] While system controller 100, user interface 106, motion controller 174, and interface system 176 are shown as separate components of image analysis system 10 in FIGS. 1-2, in other examples, two or more of system controller 100, user interface 106, motion controller 174, and interface system 176 can be integrated into a single component and / or device. Further, while system controller 100, user interface 106, motion controller 174, and interface system 176 are each shown as individual devices in FIGS. 1-2, in other examples, any of system controller 100, user interface 106, motion controller 174, and interface system 176 and / or any combination thereof can be distributed and / or virtualized across any suitable number of devices. Similarly, while image analysis system 10 is generally depicted as a single device herein, in other examples, the components of image analysis system 10 can be distributed and / or virtualized across any suitable number of systems and / or devices.

[0050] Sample carousel 170 is a carousel that stores glass slides 171. Sample carousel 170 includes robotic components for loading slides onto and unloading slides from stage 178, and enables image analysis system 10 to capture images of multiple samples without intervention or input from a human operator. Operation of sample carousel 170 can be controlled by, for example, system controller 100 and / or motion controller 174.

[0051] Glass slides 171 depicted in FIG. 1 are substrates on which histological samples are mounted, placed, etc. FIG. 2 depicts glass slide 171 A, which is one of glass slides 171, to which sample 182 is mounted. Samples can be mounted to glass slides 171 using any suitable technique (e.g., wet mounting, dry mounting, etc.). In at least some examples, the samples mounted to glass slides 171 include tissue samples for analysis via histopathology (e.g., formalin- fixed paraffin-embedded samples and / or microtome sections thereof). In at least some examples, one or more of glass slides 171 can include a cover slip. While glass slides 171 are generally discussed herein as being glass, in other examples, glass slides 171 can be made from any other suitable material for imaging by image scanner 160. Glass slide 171 A extends substantially X-coordinate and Y-coordinate directions, and has a thickness in the Z-coordinate direction.

[0052] Sample 182 is a specimen for interrogation by optical microscopy using image scanner 160. Sample 182 can he or include, for example, tissue, cells, chromosomes, deoxyribonucleic acids (DNA), protein, blood, bone marrow, urine, bacteria, beads, biopsy materials, or any other type of biological material or substance that is either dead or alive, stained or unstained, labeled or unlabeled, etc. Other examples of sample 182 include integrated circuit boards, electrophoresis records, petri dishes, film, semiconductor materials, forensic materials, machined parts, or any combination thereof. In some examples, sample 182 can include or be deposited on a substrate other than a glass slide (e.g., glass slide 171A). In some examples, sample 182 is stained with haematoxylin and eosin stain (“H&E stain”).

[0053] Illumination system 184 is configured to illuminate at least a portion of sample 182. Illumination system 184 may include, for example, a light source and illumination optics. As a specific example, the light source can be a variable intensity halogen light source with a concave reflective mirror to maximize light output and a KG- 1 filter to suppress heat. The light source can also be, for example, any type of arc-lamp, laser, or other source of light. In the depicted embodiment, illumination system 184 illuminates sample 182 in transmission mode such that line scan camera 192 and / or camera 194 sense optical energy that is transmitted through sample 182. Alternatively, or in combination, illumination system 184 may also be configured to illuminate sample 182 in reflection mode such that the line scan camera 192 and / or camera 194 sense optical energy that is reflected from sample 182. More generally, illumination system 184 can be configured for interrogation of sample 182 and / or any other sample using any known mode of optical microscopy.

[0054] Epi -illumination system 200 is an optional component of image scanner 160 and is included in examples where it is advantageous to image sample 182 and / or any other suitable sample using epi-illumination, such as in examples where image scanner 160 is used for fluorescence scanning. Fluorescence scanning is the scanning of samples that include fluorescent molecules (i.e., fluorophores), which are photon-sensitive molecules that can absorb light at a specific wavelength (excitation). These photon-sensitive molecules also emit light at a higher wavelength (emission). Because the efficiency of this photoluminescence phenomenon is very low, the amount of emitted light is often very low. This low amount of emitted light can frustrate conventional techniques for scanning and digitizing fluorophore-containing samples (e.g., transmission mode microscopy). In examples of image scanner 160 where image scanner 160 includes epi-illumination system 200 and is used to image fluorophore-containing samples, line scan camera 192 can advantageously include a time delay integration (TDI) line scan camera to increase sensitivity of line scan camera 192 and improve capture of faint fluorophores having low emitted light. An example of a TDI line scan camera suitable for use in line scan camera 192 is described subsequently in the discussion of FIG. 11C. Advantageously, TDI sensors provide a substantially better signal-to-noise ratio (“SNR”) in the output signal than other types of line scan camera sensors by summing intensity data from previously imaged regions of a specimen, yielding an increase in the SNR that is in proportion to the squareroot of the number of integration stages.

[0055] In examples where image scanner 160 is a fluorescence scanner system, line scan camera 192 can be a monochrome TDI line scan camera. Monochrome images are advantageous in fluorescence microscopy because they provide a more accurate representation of the actual signals from the various channels present on the sample. As will be understood by those skilled in the art, fluorophore-containing samples can be labeled with multiple fluorophores that emit light at different wavelengths. Each emission wavelength can be referred to as a “channel.” In at least some examples, line scan camera 192 is or includes a monochrome 10-bit 64-linear- array TDI line scan camera.

[0056] Stage 178 is configured to receive and support a glass slide, such as one of glass slides 171. In the depicted example, stage 178 receives and supports glass slide 171 A. Stage 178 is movable by stage positioner 199 and can be moved in the X-coordinate and Y-coordinate directions indicated by arrows X and Y, respectively (i.e., along the X-axis and Y-axis, respectively, of the coordinate system defined by arrows X, Y, and Z). In some examples, stage positioner 199 can also move stage 178 in the Z-coordinate direction to adjust or change the distance between stage 178 and objective lens 186, thereby adjusting or changing the distance between objective lens 186 and sample 182. Adjusting the Z- coordinate distance between objective lens 186 and sample 182 adjusts the position of the focal plane of objective lens 186 relative to sample 182 and can, accordingly, be used to adjust the focus of images of sample 182 captured by image scanner 160 (i.e., via line scan camera 192 and / or camera 194). Stage positioner 199 includes one or more motors for controlling movement of stage 178 and, as described previously, operation of stage positioner 199 can be controlled by system controller 100 (e.g., processor 102) and / or motion controller 174.

[0057] Stage positioner 199 can also be configured to accelerate stage 178 (and, as such, sample 182 mounted to glass slide 171 A) in a scanning direction to a substantially constant velocity, and then maintain the substantially constant velocity during image data capture by the line scan camera 192. The “scanning direction” in which stage 178 is moved is defined by the orientation of sensor components of line scan camera 192 and is perpendicular or substantially perpendicular to the direction in which the sensor elements of line scan camera 192 extend. In at least some examples, stage positioner 199 can move stage 178 in the X-coordinate and Y-coordinate directions according to positions defined by an X-Y coordinate grid. Further, in at least some examples, stage positioner 199 is or includes a linear actuator with an encoder or encoders capable of reporting the position of stage 178 in both X-coordinate and Y-coordinate directions to at least nanometer precision. For example, encoders sensitive to translation along the X- and Y- axes can monitor position along scanning directions, while encoders sensitive to translation along the Z-axis can monitor adjustments to objective distance.

[0058] Objective lens 186 is an optical element of image scanner 160 that gathers and focuses light from sample 182. Specifically, objective lens 186 collects and focuses light from field of view 196. Objective lens 186 can be a single optical element, such as a single lens or mirror, or can include multiple optical elements, such as one or more lenses and / or mirrors. In some examples, objective lens 186 can be referred to as an “objective lens array” or an “objective array.” In some examples, the objective lens 186 is an achromatic, apochromatic (“APO”), or semi-APO infinity plan corrected objective with a numerical aperture corresponding to the highest spatial resolution desirable, where the objective lens 186 is suitable for transmission-mode illumination microscopy, reflectionmode illumination microscopy, and / or epi-illumination-mode fluorescence microscopy (e.g., an Olympus 40x, 0.75 NA or 20x, 0.75 NA). Advantageously, objective lens 186 is capable of correcting for chromatic and spherical aberrations. In examples where objective lens 186 is infinity corrected, focusing optics 190 can be placed in the optical path 188 above the objective lens 186 where the light beam passing through the objective lens 186 becomes a collimated light beam. Focusing optics 190 focus the optical signal captured by the objective lens 186 onto the light-responsive elements of line scan camera 192 and / or camera 194, and can optionally include optical components such as filters, magnification changer lenses, etc. Objective lens 186, combined with focusing optics 190, provides the total magnification for image scanner 160. In at least one example, focusing optics 190 may contain a tube lens and an optional 2x magnification changer, thereby allowing a native 20x objective lens to scan samples at 40x magnification.

[0059] Objective lens 186 is mounted on or otherwise mechanically linked to objective lens positioner 198, such that objective lens positioner 198 can be used to control the position of objective lens 186 relative to stage 178. Objective lens positioner 198 includes one or more motors for controlling the position of objective lens 186 and, as described previously, operation of objective lens positioner 198 can be controlled by system controller 100 (e.g., processor 102) and / or motion controller 174. Objective lens positioner 198 is configured to adjust the Z-coordinate position of objective lens 186 and, in some examples, can also be configured to adjust the X-coordinate position and / or the Y- coordinate position of objective lens 186. In some examples, objective lens positioner 198 can include a very precise linear motor to move the objective lens 186 along the optical axis defined by objective lens 186 (i.e., in the Z-coordinate direction in FIG. 2).

[0060] Objective lens positioner 198 and stage positioner 199 are each optional elements of image scanner 160. However, in all examples, image scanner 160 includes at least one of objective lens positioner 198 or stage positioner 199 to enable translation of field of view 196 in the X-coordinate and Y-coordinate directions, and to adjust the Z- distance between stage 178 (and therefore any sample mounted to a slide held or placed on stage 178) and objective lens 186. The Z-location of either or both of objective lens 186 and stage 178 can be adjusted (i.e., via objective lens positioner 198 and stage positioner 199) to adjust the distance between stage 178 and objective lens 186. Adjusting the Z- distance between stage 178 and objective lens 186 adjusts the Z-position of the focal plane of objective lens 186 in sample 182 and, accordingly, can be used to focus the image(s) collected by image scanner 160.

[0061] Line scan camera 192 is an imaging sensor for imaging samples located on stage 178, such as sample 182 mounted to glass slide 171 A placed on stage 178. Line scan camera 192 includes at least one linear array of sensor elements (“pixels”) and can capture image data in either monochrome or color. Monochrome line scan cameras can include one or more linear arrays and color line scan cameras can have three or more linear arrays. For example, line scan camera 192 can be a three linear array (“red-green-blue” or “RGB”) color line scan camera. More generally, line scan camera 192 can include any type of singular or plural linear array, whether packaged as part of a camera or custom-integrated into an imaging electronic module. For example, line scan camera 192 can also be a TDI camera including 24, 32, 48, 64, 96, or any other suitable number of arrays. The array(s) of line scan camera 192 can have any suitable number of pixels, including but not limited to 512 pixels, 1024 pixels, and 4096 pixels. In all examples, motion controller 174 and / or system controller 100 can synchronize the motion of stage 178 (i.e., via control of stage positioner 199) with the line rate of line scan camera 192 to enable image scanning of sample 182. Image data generated by line scan camera 192 can be stored to memory 104 or any other suitable computer-readable memory during capture and used to generate a contiguous digital image of at least a portion of sample 182.

[0062] Camera 194 is also an imaging sensor for imaging samples located on stage 178, such as sample 182 mounted to glass slide 171A. Camera 194 can be an “area scan” camera including a matrix of sensor elements (e.g., “pixels”) or a line scan camera substantially similar to line scan camera 192. Like line scan camera 192, camera 194 can be a monochrome or color camera such that camera 194 can capture monochromatic or color images of samples on stage 178. Where camera 194 is an area scan camera, the matrix of camera 194 can have any suitable number of pixels in each dimension of the matrix.

[0063] Line scan camera 192 and camera 194 are each optional elements of image scanner 160. However, in all examples, image scanner includes at least one of line scan camera 192 or camera 194. In some examples, at least one of line scan camera 192 or camera 194 functions as a focusing sensor that operates in combination with the other of line scan camera 192 and camera 194. For example, camera 194 can be an additional line scan camera that functions as a focusing sensor for line scan camera 192. Imaging data from camera 194 acting as a focusing sensor, or from a separate focusing sensor, can be stored to memory 104 or another suitable computer-readable memory and used by processor 102 to adjust the distance between objective lens 186 and stage 178 (i.e., via one or both of objective lens positioner 198 and stage positioner 199) such that image scanner 160 can collect an in-focus image of the sample. The focusing sensor (e.g., camera 194, line scan camera 192, etc.) can be positioned on the same optical axis as the imaging sensor and / or can be positioned before or after the imaging sensor with respect to the scanning direction of the image scanner 160. The process of collecting and recording image data to generate a digital image of at least a portion of a sample using line scan camera 192 and / or camera 194 can be referred to as “capturing” an image or a digital image of the sample or sample portion. Images captured by line scan camera 192, camera 194, or any combination thereof can be stored to memory 104 for further processing and use by the program(s) of system controller 100, as described in more detail subsequently. Image scanner 160 can be configured to capture an image of an entire slide and / or the entire sample mounted to the slide (a “wholeslide image”) and / or to capture less than the entire sample mounted to a slide.

[0064] In operation, glass slide 171 A is loaded onto stage 178 by a human operator, by sample carousel 170, and / or any other suitable robotics element. Once glass slide 171 A is placed on stage 178, sample 182 is illuminated by illumination system 184, epiillumination system 200, or a combination thereof. Light reflecting off of or transmitting through sample 182 illuminates a portion thereof within field of view 196 for image capture. Light from the portion of sample 182 within field of view 196 travels along optical path 188 according to arrows 197 to either or both of line scan camera 192 and camera 194. The light excites sensor elements of line scan camera 192 and / or camera 194 and the resultant digital image data is stored to memory 104 of system controller 100 or another suitable computer-readable memory. System controller 100, motion controller 174, and / or a combination thereof can be used to perform auto-focusing using one or more autofocusing algorithms stored to memory 104 or another suitable computer-readable memory. The auto-focusing algorithm(s) can be part of image collection module 110 or any other suitable software module. As described previously, autofocusing can be performed using the imaging camera (i.e., of line scan camera 192 and camera 194) or using a separate camera specific for focusing. The relative Z-position of objective lens 186 and stage 178 is then adjusted (i.e., via one or both of objective lens positioner 198 and stage positioner 199) to move the focal plane of objective lens 186 to a position predicted to generate an in-focus image of sample 182 by the auto-focusing algorithm(s). An image scan of sample 182 can then be captured by image scanner 160. Auto-focusing and image capture are described as separate processes herein for explanatory clarity and convenience, and it is to be understood that auto-focusing and image capture can occur simultaneously and / or substantially simultaneously, and that in some examples, auto-focusing can be continuously or repeated performed as field of view 196 is moved across sample 182 (i.e., via X-coordinate and / or Y-coordinate translation caused by objective lens positioner 198 and / or stage positioner 199). As used throughout, the term “substantially” (as in “substantially simultaneously,” above) denotes a degree of deviation from a nominal value that falls within manufacturing tolerances, or that is acceptable for operability as otherwise described herein, or both. “Substantially simultaneous” operation, thus, refers to operation wherein any delay between actions (e.g., auto-focusing and image capture) is not functionally significant.

[0065] In examples where line scan camera 192 is used to image sample 182, the various components of image analysis system 10, and in particular of image scanner 160, enable automatic scanning and digitizing of sample 182. Glass slide 171 A is securely placed on stage 178 of image scanner 160 to scan sample 182. In an example where stage 178 is moved and objective lens 186 is held in a static position, under control of system controller 100 and / or motion controller 174, stage 178 accelerates sample 182 to a substantially constant velocity for sensing by line scan camera 192, such that the speed of stage 178 is synchronized with the line rate of line scan camera 192. In examples where the width of field of view 196 and / or of line scan camera 192 is less than the width of the X- coordinate or Y-coordinate dimension of sample 182 and / or glass slide 171, image scanner 160 can image sample 182 by collecting multiple “stripes” of image data. After scanning a stripe of image data, stage 178 decelerates and brings sample 182 to a substantially complete stop. Stage 178 then moves in a direction orthogonal to the scanning direction to position sample 182 for scanning of a subsequent stripe of image data (e.g., an adjacent stripe). Additional stripes are subsequently scanned until an entire portion of sample 182 or all of sample 182 is scanned. In other examples, objective lens 186 can be moved (i.e., by objective lens positioner 198) under control of system controller 100 and / or motion controller 174 in the same manner, such that the speed of objective lens 186 is synchronized with the line rate of line scan camera 192. Similarly, objective lens 186 can also be moved to collect multiple “stripes” of image data.

[0066] During the foregoing mode of digital scanning of sample 182, a contiguous digital image of sample 182 is acquired as contiguous fields of view that are combined together to form an image stripe. All adjacent image stripes are similarly combined together to form a contiguous digital image of a portion or the entirety of sample 182. The scanning of sample 182 may include acquiring stripes extending in the X-coordinate direction and / or the Y-coordinate direction. The scanning of sample 182 may be either top-to-bottom, bottom-to-top, or both (bi-directional), and may start at any point on the sample. Alternatively, the scanning of sample 182 may be either left-to-right, right-to-left, or both (bi-directional), and may start at any point on the sample. Additionally, it is not necessary that image stripes be acquired in an adjacent or contiguous manner. Furthermore, the resulting image of sample 182 may be an image of the entire sample 182 or only a portion of sample 182.

[0067] Image capture module 110, intensity profile generation module 120, artifact detection module 130, default prescan validation module 140, and image correction module 150 collectively enable image analysis system 10 to operate image scanner 160 to collect histological images and, further, to collect, analyze, and use (i.e., for image correction) prescan images. As described previously, it is advantageous to use a prescan derived from the same slide for which the prescan is to be used for image correction, as the slide-specific prescan enables improved image correction (particularly with respect to white-balancing) than prescans of other slides, including prescans of other slides taken with the same image scanner 160. However, it can be advantageous to use a sufficiently artifact-free default prescan where the slide- specific prescan contains an artifact that makes it unsuitable for image correction.

[0068] As will be explained in more detail subsequently, image capture module 110 enables the automated capture of prescan images as well as of full image scans, intensity profile generation module 120 enables the generation of intensity profiles for prescan images, artifact detection module 130 enables the automatic detection of artifacts in prescan images and determinations as to whether to use a slide-specific or default prescan, default prescan validation module 140 enables the automated identification of prescans suitable for use as default prescans, and image correction module 150 enables the use of prescans to correct (e.g., via white balancing, etc.) the image data of full slide scans.

[0069] Image capture module 110 is a software module of system controller 100 and includes one or more programs for operating image scanner 160 to capture prescan images as well as full slide scans of histological slides. Image capture module 110 is capable of identifying a region of a slide for capturing a prescan image that includes a portion of (i.e., fewer than all) a slide and, in some examples, includes one or more algorithms for detecting tissue. In these examples, image capture module 110 can be configured to cause image scanner 160 to adjust the relative position of a slide mounted to stage 178 (i.e., via stage positioner 199) and objective lens 186 (i.e., via objective lens positioner 198) to capture a prescan image of a portion of the slide that does not contain tissue. More generally, image capture module 110 is generally configured to operate image scanner 160 in the manner described previously to capture image data of slides received by stage 178 using the image sensors of line scan camera 192 and / or camera 194. Intensity profile generation module 120 enables the generation of intensity profiles based on images captured by the program(s) of image capture module 110 and / or any other suitable microscope image. The intensity profiles generated by intensity profile generation module 120 condense pixel information (e.g., as an average) in a first dimension of an image for all pixel positions of a second dimension of the image. The condensed pixel information can be, for example, an intensity value, such as a brightness or another suitable value. As a specific example, an intensity value can be an average of pixel intensities (e.g., brightness values) of all pixels in an image having a common X-position, such that the average intensity represents an average of Y -values for a given X-position of the image. More generally, the foregoing method(s) of producing intensity values enables intensity information that would otherwise be three-dimensional to instead by represented by two- dimensional graphs, as will be discussed in more detail subsequently. Intensity profdes generated by intensity profile generation module 120 can represent any suitable portion of pixels in each dimension of the image and, in at least some examples, represents all pixels in one or both dimensions of the image.

[0070] As referred to herein, an “average” can include any suitable method of taking a mean of a dataset, such as an arithmetic mean, a geometric mean, a harmonic mean, a weighted mean, and / or any other suitable method of generating a mean for a data set. More broadly, intensity profiles generated by intensity profile generation module 120 can include values that represent any suitable central tendency of a data set.

[0071] Artifact detection module 130 detects artifacts in prescans collected by image capture module 110 by analyzing intensity profiles (generated by intensity profile generation module 120) for those prescans. As will be explained in more detail subsequently and particularly with respect to the discussion of FIG. 6, artifact detection module 130 generates ratios of intensity values (i.e., intensity values averaged for individual pixel positions in one dimension of an image) for a prescan image to intensity values for corresponding pixel positions of a default prescan image also taken using image scanner 160. The use of image scanner 160 to take both images (i.e., both the current prescan and the default prescan) reduces the impact on artifact detection of aberrations or errors in intensity attributable to line scan camera 192 and / or camera 194.

[0072] The program(s) of artifact detection module 130 calculate average ratio values for all ratios within a range of corresponding pixel positions using a sliding window of pixel positions, and, further, determine whether any calculated averages are greater than an upper threshold ratio or less than a lower threshold ratio. That is, artifact detection module 130 calculates an average of all ratio values within a window defining a range of pixel positions and determines whether the average of those ratios is greater than an upper threshold or less than a lower threshold. If the average is outside the range defined by the thresholds, artifact detection module 130 flags the prescan used to generate the ratios (i.e., in combination with the default prescan) as including an artifact. If the average is not outside the range defined by the thresholds, artifact detection module 130 slides the window by one or more pixels, generates a new average for those pixel positions, and compares that average to the thresholds. Artifact detection module 130 continues sliding the window and comparing average ratios to the upper and lower thresholds until an average outside of the window defined by the thresholds is found or all pixel positions of the ratio data are examined.

[0073] One or more of the upper threshold, the lower threshold, and the number of pixel positions within window used by artifact detection module 130 can be user- configurable to adjust the sensitivity of the artifact detection performed by artifact detection module 130. Further, as used herein, “greater than” can refer to values greater than a threshold or values greater than or equal to the threshold, and “less than” can refer to values less than a threshold or values less than or equal to the threshold.

[0074] Default prescan validation module 140 is used to determine whether a particular prescan is suitable for use as a default prescan by the programs of artifact detection module 130 and image correction module 150. As referred to herein, a “validated default prescan” or a “validated prescan” is a prescan determined to be suitable for use in image correction as a default prescan. More specifically, a validated default prescan has been validated as sufficiently artifact free such that the prescan data can be used for iamge correction of slide scans taken of a different slide (i.e., different than the slide used to generate the default prescan) and in place of a slide-specific prescan. The use of a default prescan in image correction is discussed in more detail subsequently and particular with respect to FIG. 6. Default prescan validation module 140 can analyze an intensity profile for a prescan that is generated by intensity profile generation module 120 to determine if the prescan is suitable for use as a default prescan. In particular, default prescan validation module 140 analyzes the average center intensity of an intensity profile, the tilt and curvature of the intensity profile, and the smoothness of the curve to determine if the prescan is suitable for use as a default prescan.

[0075] Image correction module 150 corrects full slide scans using prescan information. For a given slide, image correction module 150 can use a prescan taken from a region of the slide or a default prescan, according to the determination of the program(s) of artifact detection module 130, to correct aspects of the full slide scan. Image correction module 150 can use prescan information to, for example, correct brightness, white-balance, or any other suitable property of the full slide scan and can perform image correction using any suitable method.

[0076] Advantageously, image analysis system 10 and, in particular, the program(s) of intensity profile generation module 120, artifact detection module 130, and default prescan validation module 140 enable automated evaluation of prescans for use in image correction, automated determination of whether to use slide-specific or default prescan image data for full slide image correction, and automated validation of prescan image data as sufficiently uniform for use as default prescan image data. In particular, the automated prescan analysis and validation enabled by system 10 and described herein reduce the incidence of artifacts in corrected slide scans owing to artifacts in prescan image data used for image correction.

[0077] FIG. 3 is schematic depiction of image set 300 and shows an example of an overview image of a tissue-containing slide as well as an example of a prescan image of a region of the tissue-containing slide. Image set 300 depicts overview image 302, prescan region 303, prescan image 304, and artifact 306, as well as directions DI and D2 (indicated by arrows). Overview image 302 is a two-dimensional image that includes two dimensions extending in directions DI and D2, respectively, such that the columns and rows of pixels that form overview image 302 extend in directions DI and D2, respectively. Overview image 302 is an image of a tissue-containing slide to be imaged by image scanner 160 and can be used by the program(s) of image capture module 110 to identify a portion of the tissue-containing slide that can be used as a prescan image. Overview image 302 can optionally be captured by a camera other than line scan camera 192 (e.g., camera 194) and, in some examples, is captured using a different imaging device than image scanner 160. Overview image 302 is generally captured at a different magnification and / or a lower- resolution than a prescan image or a line scan image of the tissue-containing slide. In some examples, overview image 302 can be referred to as a “macro image.” In some examples, image scanner 160 can also include a separate lower-resolution imaging sensor and fixed- focus lens to capture overview images. The program(s) of image capture module 110 attempt to identify a portion of the tissue-containing slide that does not contain tissue to be used as prescan region 303 based on the image data of overview image 302. Prescan image 304 is a high-resolution image of prescan region 303 taken using image scanner 160 subsequent to identification of prescan region 303 based on overview image 302. Prescan image 304 is also a two-dimensional image that extends in directions DI and D2, such that the columns and rows of the pixels of prescan image 304 extend in directions DI and D2, respectively.

[0078] In the depicted example, prescan image 304 includes artifact 306, which in FIG. 3 is a portion of the tissue mounted to the tissue-containing slide. In other examples, prescan images can contain other artifacts that result in substantially similar and / or the same errors in image correction that will be discussed subsequently with respect to FIG. 4. Both the limited resolution of overview image 302 and the relatively small size of the portion of tissue to which artifact 306 belongs make it difficult for the program(s) of image capture module 110 to detect artifact 306 when selecting prescan region 303. However, increasing the resolution of overview image 302 used to select prescan region 303 would significantly increase the time required for slide imaging.

[0079] FIG. 4 is a microscope image depicting striping caused by image-correction with an artifact-containing prescan. In particular, FIG. 4 depicts microscope image 400, which is a microscope scan capturing image data of tissue 402 and which also includes striping artifacts 404A-E. FIG. 4 also depicts arrows indicating directions DI and D2. Microscope image is a two-dimensional image that extends in directions DI and D2, such that the columns and rows of the pixels of microscope image 400 extend in directions DI and D2, respectively.

[0080] Striping artifacts 404A-404E are regularly-spaced white lines that extend through microscope image 400 in direction D2 (i.e., all of striping artifacts 404A-404E are parallel and / or substantially parallel to direction D2). Striping artifacts 404A-404E are caused by an artifact in the prescan image used for image correction (e.g., illumination, white-balancing) of microscope image 400. As described previously, image scanner 160 uses a line scanning technique to acquire slide scan images in which multiple image stripes are collected and assembled into a complete slide scan. Typically, one dimension of the prescan data is the same size as the dimension of the image stripes (i.e., the stripes used to assemble the digital slide image) that is perpendicular to the scanning direction. In these examples, the same prescan data is used to correct each “stripe’' collected during image scanning. As a result, an artifact that affects white-balancing, illumination correction, etc. will affect image correction in all stripes, often causing equally-spaced white lines in the corrected image scan. Striping artifacts 404A-404E are one such example of equally- spaced white lines caused by image correction performed in the aforementioned manner using an artifact-containing prescan.

[0081] Microscope image 400 depicts a different tissue-containing slide than the tissue-containing slide captured by overview image 302 (FIG. 3), but artifact 306 of prescan image 304 (FIG. 3) is one type of artifact that can cause the appearance of repetitive striping artifacts like striping artifacts 404A-404E in a microscope image corrected using prescan image data. Striping artifacts 404A-404E and substantially similar striping artifacts caused by prescan artifacts disrupt the image of tissue portrayed hy microscope scans (e.g., tissue 402 of microscope image 400) and, consequently, can impact the ability of microscope scans to be accurately interpreted for disease diagnosis and other suitable downstream purposes.

[0082] FIGS. 5A-5D are various examples of prescans and are discussed together herein. FIG. 5A is a microscope image of a prescan that does not include any artifacts, such as tissue, dust, debris, or other types of artifacts that can cause prescan data to be significantly non-uniform and, accordingly, introduce downstream errors during image correction.

[0083] FIG. 5B is a microscope image of a prescan that captures small debris on the slide. The type of debris depicted in FIG. 5B can be referred to as “micro-debris” in some examples. FIG. 5C is a microscope image of a prescan that captures dust on the slide coverslip. FIG. 5D is a microscope image of a prescan that captures two portions of tissue. The debris pictured in FIGS. 5B-5D cause significant non-uniformity in the image data of those respective prescans. In particular, debris of the kind depicts in FIGS. 5B-5D causes significant non-uniformity in brightness and white balance. Accordingly, when used to correct a full slide scan, the prescan images shown in FIGS. 5B-5D will introduce the same kind of striping that is depicted in FIG. 4.

[0084] FIG. 6 is a flow diagram of method 600, which is a method of determining whether to use a default prescan or a prescan taken from a current slide and, optionally, of using a prescan to correct a slide scan. Method 600 includes steps of capturing a prescan (step 602), capturing an FPN image (step 604), generating a prescan intensity profile (step 606), correcting the prescan intensity profile with the FPN data (step 608), retrieving an intensity profile for the default prescan (step 610), generating an intensity ratio profile (step 612), inspecting the intensity ratio profile using a pixel position window (step 614), determining whether an average intensity ratio is outside of threshold values (step 616), determining to use the default prescan (step 618), moving the pixel position window (step 620), determining to use the prescan from the current slide (step 622), collecting a full slide scan (step 624), correcting the slide scan using the default prescan (step 624), collecting a full slide scan (step 624), and correcting the slide scan using the prescan from the current slide (step 630). Method 600 is discussed generally herein with respect to image analysis system 10, but in other examples, method 600 can be performed by any suitable system capable of performing image analysis and processing according to method 600.

[0085] In step 602, system 10 captures a prescan. The prescan captured in step 602 depicts a portion (i.e., less than all) of a tissue-containing slide and includes two- dimensional image data having a first dimension and a second dimension, such that the pixels of the image data are arranged in a two-dimensional grid. In at least some examples, one dimension of the prescan can be a single pixel, such as in examples where the prescan tissue is a single line of image data captured by line scan camera 192. In some examples where prescan data includes a dimension that is a single pixel, the prescan data can be referred to as “one-dimensional.” The program(s) of image capture module 110 can operate image scanner 160 to capture an image of a tissue-containing slide placed on stage 178. In particular, the program(s) of image capture module 110 can move one or both of stage 178 (i.e., via stage positioner 199) and objective lens 186 (i.e., via objective lens positioner 198) and can operate line scan camera 192 and / or camera 194 to capture image data of the tissuecontaining slide to use as the prescan. Further, the prescan captured in step 602 is generally captured while the slide is illuminated (i.e., by illumination source 184).

[0086] The program(s) of image capture module 110 can identify a portion of the tissue-containing slide to capture for prescan data in step 602 by first capturing a relatively low resolution (i.e., lower than the resolution desired for the subsequent prescan) overview image of all or a suitable portion of the tissue-containing slide. The overview image can be captured using camera sensor 194 and / or any other suitable image sensor, as discussed previously with respect to FIG. 3. Based on the data of the overview image, the program(s) of image capture module 110 identify a region of the tissue-containing slide that is predicted by a tissue-detection algorithm to not include tissue and use that region for the prescan image. The program(s) of image capture module 110 can then move one or both of stage 178 (i.e., via stage positioner 199) and objective lens 186 (i.e., via objective lens positioner 198) for imaging the selected region. However, in some examples, the prescan region may in fact contain tissue despite being predicted by a tissue-detection algorithm to not include tissue. In at least some examples, the prescan captured in step 602 is or includes brightness data, such as brightness data that describes the photoresponse nonuniformity (PRNU) of the sensor of the camera (e.g., line scan camera 192, camera 194, etc.) used to capture the prescan.

[0087] In step 604, system 10 captures FPN data. The program(s) of image capture module 110 can operate image scanner 160 to collect FPN data for the same region of the tissue-containing slide. The FPN data also includes two-dimensional image data (i.e., pixel information). In at least some examples, system 10 captures FPN data in step 604 by exposing the sensor(s) of line scan camera 192 and / or camera 194 when illumination source 184 is off and / or is dimmed. In other examples, system 10 captures FPN data when the sensor of line scan camera 192 and / or camera 194 is covered. The sensor can be exposed while the tissue containing slide imaged in step 192 is loaded on stage 178 and, in some examples, while objective lens 186 and the tissue-containing slide have the same relative position as during step 602 (i.e., such that the FPN data depicts the same region as the prescan data captured in step 602). In yet further examples, system 10 captures FPN data in step 604 by exposing the sensor(s) of line scan camera 192 and / or camera 194 while there is no slide loaded onto stage 178. In either example, step 604 can optionally be performed prior to step 602, such that FPN data is captured prior to the brightness (e.g., PRNU) data in step 602.

[0088] In step 606, system 10 generates an intensity profile based on the prescan data collected in step 602. In particular, the program(s) of intensity profile generation module 120 generate the intensity profde based on the corrected prescan data, such that the intensity profile generated in step 608 describes the slide-specific prescan. The intensity profile represents intensity information for all pixels (i.e., across both dimensions) of the prescan as a set of points in a two-dimensional coordinate space. That is, the intensity profile generated in step 606 compresses three-dimensional data (i.e., first dimension position, second dimension position, and intensity) into a two-dimensional coordinate space.

[0089] The intensity profile generated in step 606 averages pixel intensity values for all pixels arranged in one dimension (i.e., having a common value in the other of the two dimensions). That is, the intensity profile averages the intensity of all pixels having a given pixel position in one of the dimensions of the two-dimensional image, and does so for all pixel positions of that dimension. The intensity profile then is able to relate average pixel intensity to pixel positions of the dimension. As a specific example, the intensity profile can average intensity across all pixels in the Y-coordinate direction for each X- coordinate position of the prescan image, thereby representing average pixel position for each X-coordinate pixel position of the prescan. Using prescan image 304 of FIG. 3 as yet another illustrative example, the intensities of all pixels having a common pixel position in direction D2 (i.e., having any pixel position in direction DI) are averaged for each pixel position in direction D2, thereby allowing average pixel intensity (i.e., in direction DI) to be plotted against pixel position in in direction D2.

[0090] The intensities averaged can be any suitable aspect of the prescan image data and, in at least some examples, are brightness values of the prescan data. For example, where the prescan image is PRNU data, the brightness gain values of the PRNU data can be used to generate the intensity profile for the prescan image data. In other examples, any other suitable value of the pixels can be used to generate the intensity profile in step 606.

[0091] As described previously, an “average” as referred to herein can include any suitable method of taking a mean of a dataset, such as an arithmetic mean, a geometric mean, a harmonic mean, a weighted mean, and / or any other suitable method of generating a mean for a data set. More broadly, an “average” can refer to any suitable central tendency of a data set.

[0092] In step 608, system 10 corrects the prescan intensity profile generated in step 606 using the FPN data collected in step 604. The program(s) of intensity profile generation module 120 can generate an FPN intensity profile from the FPN data collected in step 604 in substantially the same manner as described in step 606, and can use the FPN intensity profile to correct the intensity profile generated in step 606.

[0093] As the prescan data and the FPN data each have a two-dimensional array of pixel information, the profiles generated in step 608 enable the resultant three-dimensional brightness and FPN data to be condensed to data that can be plotted in a two-coordinate system. In particular, the prescan intensity profile averages intensity of a first dimension of the image data for each pixel position of a second, perpendicular dimension of the image data. Similarly, the FPN intensity profile averages FPN values across one dimension of the image data for each pixel position of another, perpendicular dimension of the image data. The pixel positions of the prescan intensity profile correspond (i.e., in a one-to-one manner) to the pixel positions of the FPN intensity profile, such that the prescan intensity profile and the FPN intensity profile represent the same or substantially the same portion of the tissue-containing slide. As the pixel positions of the brightness intensity profile and the FPN intensity profile correspond in a one-to-one manner, the values of the FPN intensity profile can be subtracted from corresponding values of the brightness intensity profile to create a corrected brightness intensity profile in step 608 that can be used with subsequent steps of method 600.

[0094] In step 610, system controller 100 retrieves an intensity profile for a default prescan. The default prescan is a validated default prescan that has a sufficiently consistent intensity profile and, therefore, is implicitly artifact-free. The intensity profile for the default prescan is generated in substantially the same manner as described previously with respect to steps 602-608 and, in some examples, is generated and validated according method 700, which is described in more detail subsequently with respect to the discussion of FIG. 7. The intensity profile retrieved in step 610 has average intensities for the same number of pixel positions as the intensity profile generated in step 604 such that the pixel positions of the intensity profile generated in step 604 and the default intensity profile retrieved in step 610 correspond in a one-to-one manner. More generally, the default prescan can have the same pixel dimensions as the slide-specific prescan captured in step 602. In at least some examples, the intensity profile for the default prescan is also corrected using FPN data in the same manner as described with respect to step 608.

[0095] In step 612, system controller 100 generates an intensity ratio profile. Specifically, system controller 100 generates an intensity ratio profile by, for each corresponding pixel position of the corrected intensity profile generated in step 608 and the default intensity profile retrieved in step 610, generating a ratio of the average intensity from the intensity profile generated in step 608 to the average intensity for that corresponding pixel position from the default intensity profile (i.e., retrieved in step 610). The intensity ratio profile generated in step 612 provides values that can be used to understand the relative intensities measured by groupings of sensor pixels (i.e., of line scan camera 192 and / or camera 194) for the prescan generated by the current slide and the default prescan retrieved in step 610. As the default prescan is validated for use for performing image corrections to full slide scans, the intensity ratio values generated in step 612 can be used to easily compare the prescan from the current slide to the default prescan and, further, can be used to detect artifacts in the prescan from the current slide (i.e., the prescan collected in step 602). While step 612 is generally discussed herein as using the corrected intensity profile generated in step 608, in some examples, method 600 can lack steps 604 and 608, and step 612 can instead use the intensity profile generated in step 606 to create the intensity ratio profile in step 612. Steps 614-622 relate to the use of the intensity ratio profile to detect artifacts in the prescan from the current slide. In particular and as will be discussed in more detail subsequently, steps 614-622 inspect a windowed range of pixels and, more particularly, inspect the average intensity ratio within the windowed range. The program(s) of system controller 100 and, more particularly, of artifact detection module 130 are configured to slide the windowed range to inspect the intensity ratios of any suitable number and up to all corresponding pixel positions to determine whether to use the default prescan (i.e., the prescan for which intensity profile information was retrieved in step 610) or the prescan from the current slide (i.e., the prescan captured in step 602) for image correction. The program(s) of artifact detection module 130 compare the average intensity ratio for a given windowed range of pixel positions to an upper threshold and a lower threshold. The thresholds are selected such that an average intensity ratio above the upper threshold or below the lower threshold is indicative of the presence of an artifact within the slidespecific prescan image data captured in step 602. The use of a windowed range in steps 614-622 reduces the impact of outlier average intensity ratios that are more likely to be the result of a transient error during image capture than the result of an object (e.g., tissue, dust, debris, etc.) on the slide and in view of the camera during prescan collection (i.e., within the area of the slide used to capture prescan image data).

[0096] In step 614, the program(s) of artifact detection module 130 inspect(s) the intensity ratio profile using a pixel position window to generate an average intensity ratio for the corresponding pixel positions of the intensity ratio profile that are within the range defined by window. The pixel position window defines a range of (i.e., a number of consecutive) pixel positions and, in some examples, can be user-adjustable to adjust the sensitivity of artifact detection in steps 614-622. As will be explained in more detail subsequently, the program(s) of artifact detection module 130 inspect multiple ranges of the number of consecutive pixels defined by the window (i.e., by repeatedly calculating average intensity ratios and moving the window across the corresponding pixel positions of the intensity ratio profile) until an intensity ratio outside of the intensity thresholds is found or, alternatively, not found after all desired pixel ranges are inspected. As such, the window can be positioned in any suitable manner during the first iteration of step 614 such that the first inspection performed during the first iteration of step 614 can include any suitable pixel positions. In some examples, the first iteration of step 614 can include the maximum or minimum corresponding pixel position (i.e., representing pixels taken from an edge of image data). In yet further examples, the first iteration of step 614 can include only values intermediate to the maximum or minimum corresponding pixel positions.

[0097] In step 616, the program(s) of artifact detection module compare the average intensity ratio generated in the most-recent iteration of step 614 (i.e., the average intensity ratio of all pixel positions within the range defined by the current position of the window) to upper and lower thresholds. The upper and lower thresholds together define a window or span of acceptable average intensity ratios, such that average intensity ratios greater than the upper threshold value or lower than the lower threshold value can he used to assess whether the pixel positions used to generate an average intensity ratio (i.e., the pixel positions within the position of the window used in step 614 to generate the average intensity ratio) that contain an artifact.

[0098] If the average intensity ratio calculated in the most-recent iteration of step 614 is found to be outside of the thresholds used in step 616, method 600 proceeds to step 618 and the default prescan (i.e., the prescan from which the intensity profile retrieved in step 610 was generated) is used for image correction.

[0099] If the average intensity ratio calculated in the most-recent iteration of step 614 is found to be within the thresholds used in step 616 and, of the pixel positions designed for inspection (e.g., all pixel positions), uninspected pixel positions remain, method 600 proceeds to step 620. In step 620, the pixel position window is moved to encompass a different set of corresponding pixel positions and method 600 then proceeds to perform another iteration of step 614. Average intensity ratios are calculated in step 614 and compared against the threshold values in step 616 until an average intensity ratio outside of the threshold range is found (i.e., and method 600 proceeds to step 618) or all corresponding pixel positions have been inspected and all average intensity ratios are found to be within the threshold range. If all average intensity ratios are found to be within the threshold range during all iterations of steps 614-616, method 600 proceeds to step 622 and uses the prescan from the current slide for image correction.

[0100] Advantageously, where there is an artifact within the slide-specific prescan (as detected by the iterations of steps 614-616), method 600 enables system 10 to automatedly determine to use the default prescan. Similarly, where the slide-specific prescan (i.e., the prescan collected in step 602) is found to be artifact- free according to iterations of steps 614-616, method 600 enables system 10 to automatedly determine to use the slide-specific prescan. System 10 thus substitutes the default prescan for any slide- specific prescan that is flagged (at step 616) as containing artifacts that would render the slide-specific prescan inferior to a non-specific but validated default prescan.

[0101] As described previously, the window used to generate the average intensity ratio in step 614 can include any suitable number of consecutive pixel positions. Furthermore, the thresholds used in step 616 can be any suitable value and can be, for example user configurable. In some examples, the upper threshold can be a ratio of 1.005 and the lower threshold can be a ratio of 0.995. The pixel position window can also be moved in step 620 by any suitable number of consecutive pixel positions. In at least some examples, the pixel position window is moved by one pixel position during each iteration of step 620. However, in other examples, the pixel position window can be moved by a greater number of pixel positions, such as five or ten consecutive pixel positions.

[0102] While method 600 is generally described as proceeding to step 618 after a single average intensity ratio is found to be outside of the threshold range in an iteration of step 616, in other examples, method 600 can require multiple average intensity ratios to be outside of the threshold range to proceed to step 618. For example, system controller 100 can store a count of a number of average intensity ratios calculated according to step 614 to that are outside of the thresholds used in step 616 and can proceed to step 618 when the count equals or exceeds a separate threshold value.

[0103] Method 600 proceeds to step 624 after step 618. In step 624, the program(s) of image capture module 110 cause image scanner 160 to collect a full scan of the slide used for prescan image capture in step 602. The full scan collected in step 624 may be less than the entire tissue-containing slide or the entire tissue-containing portion of the slide, but is a larger portion of the slide than was collected in step 602 to be used as a prescan. However, in some examples, the entire slide or the entire tissue-containing portion of the slide can be imaged in step 624.

[0104] Method 600 proceeds to step 626 following step 624. In step 626, the program(s) of image correction module 150 apply image corrections to the slide scan collected in step 624 using the default prescan. The image data for the default prescan for which intensity profile information was retrieved in step 610 can be retrieved from, e.g., memory 104 of system controller 100 and used by the program(s) of image correction module 150 in step 626. Image corrections performed in step 626 can include illumination corrections and / or white-balancing, and more broadly can include any suitable image correction based on default prescan image data. Image corrections performed in step 626 can be performed using any suitable method for using prescan image data to correct image data for slide scans. Following step 626, the corrected image can be stored to memory 104 and / or another suitable storage device, element, etc., and can be used for any suitable downstream purpose, including a diagnostic purpose.

[0105] In some examples, FPN data collected in step 604 can also be used for image correction in step 624. As a specific example, image normalization can be performed according to the following equation: 240 - where FPN refers to FPN data, PRNU refers to prescan data that is PRNU data, “raw image” refers to image data from the slide scan, and “prescan corrected image” refers to output image data for the corrected slide scan. Slide scans can be corrected according to the above formula in a pixel-by-pixel manner (i.e., such that pixels of the slide scan are corrected with pixels of the PRNU and FPN data that were captured by the same pixels of the camera sensor) and / or in any other suitable manner. In at least some examples, the prescan intensity profile (i.e., the intensity profile generated in step 606) and the corresponding FPN intensity profile data (i.e., the FPN intensity profile generated in step 608) are used to correct pixels according to the above formula and in a pixel-by-pixel manner, such that the pixels of the slide scan are corrected using intensity values (i.e., from the prescan intensity profile and the FPN intensity profile) for a corresponding pixel position.

[0106] Following step 622, method 600 proceeds to step 628. Step 628 is substantially similar to step 624 and the description of step 624 is applicable to step 628. Following step 628, method 600 proceeds to step 630. Step 630 is substantially similar to step 626, but uses the slide-specific prescan captured in step 602 for image correction rather than the default prescan used to generate the intensity profile retrieved in step 610.

[0107] Method 600 advantageously enables automated evaluation of slide prescan image data to determine whether the slide-specific prescan or a default prescan should be used for image correction and, further, enables the automated use of slide- specific and default prescans for image correction of full slide scans. Method 600 enables the use of a default prescan in place of a slide-specific prescan where the slide-specific prescan is found to include an artifact, thereby reducing the incidence of artifacts in slide scans using prescan data for image correction, while allowing slide-specific prescans that do not include artifacts to be used for image correction. As described previously, it is advantageous to use slide-specific prescans where those prescans do not include artifacts, as use of a slide- specific prescan will improve white-balancing as compared to use of a default prescan derived from a different tissue-containing slide.

[0108] FIG. 7 is a flow diagram of method 700, which is a method of validating a prescan for use as a default prescan. Method 700 includes steps of capturing a prescan (step 702), capturing an FPN image (step 704), generating a prescan intensity profile (step 706), correcting the prescan intensity profile with the FPN data (step 708), determining whether an average center intensity is acceptable (step 710), determining that the prescan is unacceptable to be used as a default prescan (step 712), fitting a polynomial to the line profile (step 714), determining whether the illumination source of the prescan is acceptable, determining that the prescan is unacceptable as a default prescan (step 718), outputting an indication that the illumination source is unacceptable (step 719), determining whether the curve smoothness is acceptable (step 720), determining that the prescan is unacceptable to be used as a default prescan (step 722), saving the prescan as a default prescan (step 724), capturing a new prescan and a full scan of a new slide (step 726), determining that the new prescan is unacceptable (step 728), and using the default prescan to correct the full scan of the new slide (step 730). Method 700 is discussed generally herein with respect to image analysis system 10, but in other examples, method 700 can be performed by any suitable system capable of performing image analysis and processing according to method 700.

[0109] Steps 702, 704, 706, and 708 are substantially similar to steps 602, 604, 606, and 608 of method 600, respectively, and the description herein of each of steps 602, 604, 606, and 608 is applicable to the corresponding step of steps 702, 704, 706, and 708. However, unlike method 600, method 700 can be performed using a tissue-containing slide or a slide that does not contain tissue. In some examples, it can be advantageous to use a slide that does not include tissue to generate a default prescan.

[0110] In step 710, default prescan validation module 140 determines whether an average center intensity of the corrected intensity profile generated in step 708 is acceptable. Default prescan validation module 140 generates an average intensity value for a range of pixel positions that extends across a center of the pixel positions of the intensity profile. The range can be any suitable number of pixel positions and, in some examples, can be centered on the center of the pixel positions represented in the intensity profile. The average intensity value is then compared to a threshold value to determine whether an average center intensity of the prescan is sufficiently high that the prescan can be used as a default prescan. The threshold value can be any suitable value and is selected such that prescans having average center intensities above the threshold value are suitable for use as default prescans.

[0111] In examples where the intensity profile represents a brightness detected by the sensor used to generate the prescan (i.e., line scan camera 192 or camera 194), the threshold value is a threshold center brightness. In these examples, the threshold value is selected such that prescans having a center brightness above the threshold value have sufficient brightness to be used as default prescans for a variety of tissue-containing slides.

[0112] If the average center intensity does not satisfy the threshold used in step 710, method 700 proceeds to step 712. In step 712, default prescan validation module 140 determines that the prescan is unacceptable as a default prescan and method 700 stops. Method 700 can optionally be repeated with different prescan image data. Default prescan validation module 140 can, in some examples, output an indication (e.g., via user interface 106) that communicates to a user that the prescan is unacceptable for use as a default prescan.

[0113] If the average center intensity does satisfy the threshold used in step 710, method 700 proceeds to step 714. In step 714, default prescan validation module 140 fits a polynomial curve to the intensity profile. The polynomial function can be fit in any suitable manner and can be any suitable polynomial function. In at least some examples, a fifthorder polynomial function is used for curve-fitting to the intensity profde data to improve fit of the curve to the values of the intensity profile.

[0114] In step 716, default prescan validation module 140 inspects the symmetry of the polynomial curve created in step 714 to determine whether the illumination of the prescan is acceptable. Default prescan validation module 140 can inspect the symmetry of the polynomial curve by, for example, inspecting the tilt and the curvature of the polynomial curve to determine whether the illumination of the prescan is acceptable. Curvature can indicate, for example, overall evenness of the illumination while tilt (i.e., the relative positions of the terminal ends of the polynomial curve) can indicate whether one side of the prescan has data that is significantly brighter (indicating more intense illumination) than the other side of the prescan. In other examples, other suitable values descriptive of curve symmetry can be used in step 716.

[0115] If the illumination source is found to be unacceptable in step 716, method 700 proceeds to step 718. In step 718, default prescan validation module 140 determines that the prescan is unacceptable as a default prescan and method 700 stops or, optionally, proceeds to step 719. In step 719, default prescan validation module 140 outputs an indication that the illumination source is unacceptable and should be changed. An operator can then change, replace, adjust, repair, etc. the illumination source (e.g., a bulb of illumination system 184; FIG. 1) before attempting to generate a new default prescan.

[0116] If the illumination source is found to be acceptable in step 716, method 700 proceeds to step 720. In step 720, default prescan validation module 140 determines whether the smoothness of the polynomial curve created in step 714 is acceptable. Default prescan validation module 140 can calculate curve smoothness using any acceptable method and can determine whether the smoothness of the polynomial curve satisfies a threshold smoothness value.

[0117] If the curve smoothness is found to be unacceptable in step 720, method 700 proceeds to step 722. In step 722, default prescan validation module 140 determines that the prescan is unacceptable as a default prescan and method 700 stops. Method 700 can then optionally be repeated with different prescan image data. Default prescan validation module 140 can, in some examples, output an indication (e.g., via user interface 106) that communicates to a user that the prescan is unacceptable for use as a default prescan. In some examples, curve smoothness can be evaluated by averaging the values at each end of the polynomial curve, dividing that average by the maximum of the polynomial curve, and comparing the resultant value to a threshold smoothness value.

[0118] If the curve smoothness is found to be acceptable in step 720, method 700 proceeds to step 724. In step 724, the prescan is saved to be used as a default prescan. Prescan images saved during step 724 (i.e., having an acceptable average center intensity according to step 710, having acceptable curve symmetry according to step 716, and having acceptable smoothness according to step 720) are validated prescans according to method 700. Default prescan validation module 140 can save the prescan data and, in some examples, the intensity profile for the prescan (generated in step 708) to memory 104 for use with method 600 (FIG. 6).

[0119] Following step 724, method 700 can end or can optionally proceed to method 600 (FIG. 6). The iteration of method 600 can use the intensity profile generated in step 708 as the intensity profile retrieved in step 610 and, if artifact detection module 130 determines to use the default prescan in step 618, can use the default prescan validated via method 700 and saved in step 724 to correct the slide scan in step 626.

[0120] Advantageously, method 700 enables automated evaluation of prescan images for use as default prescans with, e.g., method 600. Method 700 evaluates illumination uniformity, center brightness, and brightness uniformity (i.e., curve smoothness in step 722) to evaluate prescan images. Notably, the various image data evaluations performed in steps 710, 716, and 720 enable prescan images saved in step 724 to be used for correcting a wide range of slide scans.

[0121] It may be advantageous to periodically perform method 700 using a new slide or the same slide previously used to generate the default prescan such that the default prescan image data accurately reflects the current operating conditions of an image scanner 160. Various properties of an image scanner 160 that impact aspects of prescan image data, such as illumination uniformity and / or camera sensor uniformity, may change over time and with use of an image scanner. As such, it can be advantageous to capture a new, updated default prescan image to improve the quality of slide scans corrected using the default prescan (e.g., according to method 600).

[0122] FIG. 8 depicts intensity profile 800, which is an example of an intensity profile generated according to step 608 of method 600 (FIG. 6) and / or step 708 of method 700 (FIG. 7), and includes a polynomial curve fit to the intensity profile data. Intensity profile 800 includes axis 802, which represents pixel positions, and axis 804, which represents average intensity. Intensity profile 800 also includes intensity profile data 806 and polynomial curve 808. As described previously, average intensity data 806 represents an average of intensity values across one dimension (e.g., a row, column, etc.) of image data and pixel position represents the pixel position of the other dimension of the image data shared by the pixels for which intensity was averaged. Polynomial curve 808 is a polynomial curve fit to average intensity data 806 and can be used for illumination evaluation in step 716 of method 700 (FIG. 7) and smoothness evaluation in step 718 of method 700. FIG. 8 also depicts 1.2-sigma error lines relative to polynomial curve 808.

[0123] FIG. 9 A depicts intensity ratio profile 900, which is an example of an intensity ratio profile generated according to step 612 of method 600 (FIG. 6). Intensity ratio profile 900 includes axis 902, which represents corresponding pixel positions of the intensity profiles used to generate the intensity ratio profile, and axis 904, which represents intensity ratio. Intensity ratio profile 900 also depicts intensity ratio data 906, upper threshold 908 A, and lower threshold 908B. Upper threshold 908 A corresponds to ratio of 1.005 (i.e., 0.005 above equal intensity values) and lower threshold 908B corresponds to a ratio of 0.995 (i.e., 0.005 below equal intensity values). Intensity ratio data 906 is normalized to 0 for convenience in intensity ratio profile 900 (i.e., such that a value of 0 represents equal average intensity values for a given pixel position of a slide-specific prescan intensity profile and the corresponding pixel position of a default prescan). The intensity information depicted in FIG. 9A is brightness intensity information, but in other examples, other intensity information and the discussion herein of FIGS. 9A-9B is applicable to those examples.

[0124] FIG. 9B is a microscope image depicting slide-specific prescan 910, which has pixel data in dimension DI and dimension D2. As depicted in in FIG. 9B, slide-specific prescan 910 includes significant artifacts. The artifacts shown in FIG. 9B are dust particles, but the discussion of FIG. 9A-9B herein is generally applicable to any type of slide prescan artifact. In FIGS. 9A-9B, the slide-specific intensity profile used to generate intensity ratio profile 906 averages pixels over dimension DI for each pixel position in dimension D2. The presence of artifacts in FIG. 9B causes corresponding pixel positions of the intensity profile generated from slide- specific prescan 910 to have lower brightness intensity values due to absorption of light from the illumination source (e.g., illumination system 184; FIG. 1). Consequently, intensity ratio profile 900 has intensity ratio values below lower threshold 908B at values of axis 904 that correspond to the pixel positions in dimension D2 where dust particles are located. Furthermore, intensity ratio data 906 includes intensity ratio data that sufficiently below lower threshold 908B for a sufficient number of consecutive corresponding pixel positions (i.e., values of axis 904) such that, in at least some examples, an average intensity ratio within the pixel position window used in steps 614-616 of method 600 (FIG. 6) is below lower threshold 908B, and method 600 will proceed to step 618 and use the default prescan in place of slide-specific prescan 910 for image correction.

[0125] FIG. 10A depicts intensity ratio profile 950, which is another example of an intensity ratio profile generated according to step 612 of method 600 (FIG. 6). Intensity ratio profile 950 includes axis 952, which represents corresponding pixel positions of the intensity profiles used to generate the intensity ratio profile, and axis 954, which represents intensity ratio. Intensity ratio profile 900 also depicts intensity ratio data 956, upper threshold 958A, and lower threshold 958B. Like upper threshold 908A and lower threshold 908B, respectively, upper threshold 958A corresponds to ratio of 1.005 (i.e., 0.005 above equal intensity values) and lower threshold 958B corresponds to a ratio of 0.995 (i.e., 0.005 below equal intensity values). Intensity ratio profile 950 is otherwise substantially similar to intensity ratio profile 900, but is generated based on slide-specific prescan 960 (FIG. 10B).

[0126] FIG. 10B is a microscope image depicting slide-specific prescan 960, which has pixel data in dimension DI and dimension D2. In FIGS. 9A-9B, the slide-specific intensity profile used to generate intensity ratio profile 956 averages pixels over dimension DI for each pixel position in dimension D2. Slide-specific prescan 960 is generally artifact- free and, consequently, intensity ratio data 956 generally stays within the window defined by upper threshold 958A and lower threshold 958B. As such, in at least some examples, method 600 (FIG. 6) will proceed to step 622 and use the slide-specific prescan 960 for image correction.

[0127] FIGS. 11A-11C represent different embodiments of line scan camera sensors suitable for use as or as part of line scan camera 192 and / or camera 194 (FIG. 2). FIG. 11 A is a schematic diagram of linear array 1240, which is an example of a sensor for a line scan camera. Linear array 1240 is a single linear array that includes a plurality of individual pixel elements 1245. Pixel elements 1245 are photo sensor components of linear array 1240. In the depicted example, the single linear array 1240 has 4096 pixels. In other examples, linear array 1240 may have more or fewer pixels. For example, common formats of linear arrays include 512, 1024, and 4096 pixels. Pixel elements 1245 are arranged in a linear fashion and define field of view 196 in combination with the magnification of the image scanner 160 (e.g., of objective lens 186). In some examples, linear array 1240 is a charge coupled device (“CCD”) array or a complementary metal-oxide-semiconductor (“CMOS”) array.

[0128] FIG. 1 IB is a schematic diagram of color array 1250, which is another example of a sensor for a line scan camera (e.g., line scan camera 192). Color array 1250 includes three linear arrays, each of which may be implemented as a CCD array, and which combine to form color array 1250. Each individual linear array in color array 1250 detects a different color intensity, for example, red, green, or blue. The color image data from each individual linear array in the color array 1250 is combined to form a single field of view (e.g., field of view 196) of color image data.

[0129] FIG. 11C is a schematic diagram of TDI array 1255, which is another example of a sensor for a line scan camera (e.g., line scan camera 192). TDI array 1255 includes a plurality of linear arrays, each of which may be implemented as a CCD array, and which combine to form TDI array 1255. As described previously, TDI array sensors, such as TDI array 1255, advantageously have improved SNR as compared to other linear array sensors by summing intensity data from previously imaged portions of a specimen, yielding an increase in the SNR that is in proportion to the square-root of the number of linear arrays (also referred to as integration stages). TDI array 1255 can include any suitable number of linear arrays such as, for example, 24, 32, 48, 64, 96, or 120 linear arrays. While the invention has been described with reference to an exemplary embodiment(s), it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment(s) disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

[0130] DISCUSSION OF POSSIBLE EMBODIMENTS

[0131] The following are non-exclusive descriptions of possible embodiments of the present invention.

[0132] A method of detecting prescan artifacts according to the present disclosure includes, among other possible things, capturing a prescan of a tissue containing slide, wherein the prescan includes image data of at least a portion of the tissue-containing slide and the image data of the prescan includes a first dimension and a second dimension, generating a first intensity profile of the image data of the prescan, wherein the first intensity profile includes averages of pixel intensities for pixels of the first dimension of the image data of the prescan for each pixel position of the second dimension of the image data of the prescan, retrieving a second intensity profile for a default prescan, wherein the default prescan includes image data of at least a portion of a different slide, the image data of the default prescan includes the first dimension and the second dimension, and the second intensity profile includes averages of pixel intensities for pixels of the first dimension of the default prescan for each pixel position of the second dimension of the default prescan, generating an intensity ratio profile based on the first intensity profile and the second intensity profile, determining that an average of intensity ratios within a window of pixel positions of the intensity ratio profile is at least one of greater than an upper threshold value and less than a lower threshold value, and in response to determining that the average of intensity ratios is at least one of greater than the upper threshold value and less than the lower threshold value, determining that the prescan includes an artifact.

[0133] The method of the preceding paragraph can optionally include, additionally and / or alternatively, any one or more of the following features, configurations and / or additional components or steps:

[0134] A method as set forth above, wherein the window of pixel positions is a window of continuous pixel positions. A method as set forth above, wherein generating the intensity ratio profile comprises, for each pixel position of the second dimension of the prescan and each corresponding pixel position of the second dimension of the default prescan, generating a ratio of a first corresponding average pixel intensity of the first intensity profile to a second corresponding average pixel intensity of the second intensity profile, wherein the intensity ratio profile relates intensity ratios to corresponding pixel positions of the second dimension of the prescan and of the second dimension of the default prescan, and the window of pixel positions is a window of corresponding pixel positions.

[0135] A method as set forth above, wherein the window of corresponding pixel positions is a window of continuous corresponding pixel positions.

[0136] A method as set forth above, wherein the pixel intensity data is brightness data.

[0137] A method as set forth above, wherein the image data for the prescan is photoresponse nonuniformity data.

[0138] A method as set forth above, wherein generating the first intensity profile comprises capturing fixed pattern noise (FPN) data for the at least a portion of the tissuecontaining slide, wherein the FPN data includes the first dimension and the second dimension, extracting brightness data from the photoresponse nonuniformity data, wherein the brightness data includes the first dimension and the second dimension, generating a brightness intensity profile based on the brightness data, wherein the brightness intensity profile includes averages of pixel intensities for pixels of the first dimension of the brightness data of the prescan for each pixel position of the second dimension of the brightness data of the prescan, generating an FPN intensity profile based on the brightness data, wherein the FPN intensity profile includes averages of pixel intensities for pixels of the first dimension of the FPN data of the prescan for each pixel position of the second dimension of the FPN data of the prescan, generating the first intensity profile based on the brightness intensity profile and the FPN intensity profile.

[0139] A method as set forth above, wherein the portion of the tissue-containing slide depicted by the image data of the prescan does not include tissue.

[0140] A method as set forth above, further comprising, prior to retrieving the second intensity profile capturing an unvalidated default prescan of the different tissuecontaining slide, wherein the unvalidated default prescan includes the image data of the at least a portion of the different slide, generating the second intensity profile based on the unvalidated default prescan, generating an average pixel intensity value for a range of pixel positions of the second dimension of the image data of the unvalidated default prescan, the range spanning a center of pixel positions of the second dimension, determining that the average pixel intensity is within an acceptable pixel intensity range defined by a maximum acceptable intensity and a minimum acceptable intensity, in response to determining that the average pixel intensity is within the acceptable pixel intensity range, fitting a polynomial curve to the intensity profile, determining that a tilt of the polynomial curve is within an acceptable tilt range, determining that a curvature of the polynomial curve is within an acceptable curvature range, determining that a smoothness of the polynomial curve is within an acceptable smoothness range, and in response to determining that the tilt of the polynomial curve is within the acceptable tilt range, determining that the curvature of the polynomial curve is within the acceptable curvature range, and the smoothness is within the acceptable smoothness range, validating the unvalidated default prescan as the default prescan.

[0141] A method as set forth above, further comprising in response to determining that the prescan includes an artifact, retrieving the image data of the default prescan, capturing a full image of the tissue-containing slide, and correcting the full image of the tissue-containing slide using the image data of the default prescan.

[0142] A method as set forth above, wherein the full image of the tissue-containing slide includes a larger number of pixels than the default prescan.

[0143] A method as set forth above, wherein the window spans 120 corresponding pixel positions.

[0144] A method as set forth above, wherein determining that the average of intensity ratios within the window of continuous corresponding pixel positions of the intensity ratio profile is at least one of greater than the upper threshold value and less than the lower threshold value comprises sliding the window through a plurality of corresponding pixel position ranges and determining whether an average of intensity ratios within each corresponding pixel position range of the plurality of corresponding pixel position ranges is at least one of greater than the upper threshold value and less than the lower threshold value, wherein the plurality of corresponding pixel position ranges includes all corresponding pixel positions of the intensity ratio profile.

[0145] A method as set forth above, and further comprising mounting tissue onto a slide to create the tissue-containing slide, loading the tissue-containing slide onto a stage of an imaging apparatus, and capturing the prescan using an imaging sensor of the imaging apparatus. A method as set forth above, wherein the upper threshold value is a first ratio of 1.05, and the lower threshold value is a second ratio of 0.95.

[0146] A method as set forth above, wherein the different slide is a different tissuecontaining slide.

[0147] A system for detecting prescan artifacts according to the present disclosure includes, among other possible things, an imaging system, a processor operatively coupled to the imaging system, and at least one memory encoded with instructions. The instructions, when executed, cause the processor to cause the imaging system to capture a prescan of a tissue-containing slide, wherein the prescan includes image data of at least a portion of the tissue-containing slide and the image data of the prescan includes a first dimension and a second dimension, generate a first intensity profile of the image data of the prescan, wherein the first intensity profile includes averages of pixel intensities for pixels of the first dimension of the prescan for each pixel position of the second dimension of the prescan, retrieve a second intensity profile for a default prescan, wherein the default prescan includes image data of at least a portion of a different slide and the image data of the default prescan includes the first dimension and the second dimension, and the second intensity profile includes averages of pixel intensities for pixels of the first dimension of the default prescan for each pixel position of the second dimension of the default prescan, generate an intensity ratio profile based on the first intensity profile and the second intensity profile, determine that an average of intensity ratios within a window of pixel positions of the intensity ratio profile is at least one of greater than an upper threshold value and less than a lower threshold value, and in response to determining that the average is at least one of greater than the upper threshold value and less than the lower threshold value, determine that the prescan includes an artifact.

[0148] The system of the preceding paragraph can optionally include, additionally and / or alternatively, any one or more of the following features, configurations and / or additional components or steps:

[0149] A system as set forth above, wherein the window of pixel positions is a window of continuous pixel positions.

[0150] A system as set forth above, wherein the instructions, when executed, cause the processor to generate the intensity ratio profile by, for each pixel position of the second dimension of the prescan and each corresponding pixel position of the second dimension of the default prescan, generating a ratio of a first corresponding average pixel intensity of the first intensity profile to a second corresponding average pixel intensity of the second intensity profile, wherein the intensity ratio profile relates intensity ratios to corresponding pixel positions of the second dimension of the prescan and of the second dimension of the default prescan.

[0151] A system as set forth above, wherein the window of pixel positions is a window of corresponding pixel positions.

[0152] A system as set forth above, wherein the window of pixel positions is a window of continuous corresponding pixel positions.

[0153] A system as set forth above, wherein the pixel intensity data is brightness data.

[0154] A system as set forth above, wherein the portion of the tissue-containing slide depicted by the image data of the prescan does not include tissue.

[0155] A system as set forth above, wherein the instructions, when executed, further cause the processor to in response to determining that the prescan includes an artifact, retrieve the image data of the default prescan, capture a full image of the tissuecontaining slide, and correct the full image of the tissue-containing slide using the image data of the default prescan.

[0156] A system as set forth above, wherein the full image of the tissue-containing slide includes a larger number of pixels than the image data of the default prescan.

[0157] A system as set forth above, wherein the instructions, when executed, cause the processor to determine that the average of intensity ratios within the window of continuous corresponding pixel positions of the intensity ratio profile is at least one of greater than the upper threshold value and less than the lower threshold value by sliding the window through a plurality of corresponding pixel position ranges, and determining whether an average of intensity ratios within each corresponding pixel position range of the plurality of corresponding pixel position ranges is at least one of greater than the upper threshold value and less than the lower threshold value, wherein the plurality of corresponding pixel position ranges includes all corresponding pixel positions of the intensity ratio profile.

[0158] A method of validating a default prescan image according to the present disclosure includes, among other possible things, capturing a first prescan of a first tissuecontaining slide, wherein the first prescan includes image data of at least a portion of the first tissue-containing slide, and the image data of the first prescan includes a first dimension and a second dimension, generating an intensity profile of the image data of the first prescan, wherein the intensity profile includes averages of pixel intensities for pixels of the first dimension of the first prescan for each pixel position of the second dimension of the first prescan, generating an average pixel intensity value for a range of pixel positions of the second dimension, the range spanning a center of pixel positions of the second dimension, determining that the average pixel intensity is within an acceptable pixel intensity range defined by a maximum acceptable intensity and a minimum acceptable intensity, in response to determining that the average pixel intensity is within the acceptable pixel intensity range, fitting a polynomial curve to the intensity profile, determining that a tilt of the polynomial curve is within an acceptable tilt range, determining that a curvature of the polynomial curve is within an acceptable curvature range, determining that a smoothness of the polynomial curve is within an acceptable smoothness range, and in response to determining that the tilt of the polynomial curve is within the acceptable tilt range, determining that the curvature of the polynomial curve is within the acceptable curvature range, and the smoothness is within the acceptable smoothness range, validating the first prescan as the default prescan image.

[0159] The method of the preceding paragraph can optionally include, additionally and / or alternatively, any one or more of the following features, configurations and / or additional components or steps:

[0160] A method as set forth above, and further comprising capturing a second prescan of a second tissue-containing slide, wherein the second prescan includes image data of at least a portion of the second tissue-containing slide, and the image data of the second prescan includes the first dimension and the second dimension, capturing a full image of the second tissue-containing slide, determining that the second prescan contains an artifact, and in response to determining that the second prescan contains the artifact, correcting the full image of the second-tissue containing slide using the first prescan.

Claims

CLAIMS:

1. A method of detecting prescan artifacts, the method comprising: capturing a prescan of a tissue-containing slide, wherein: the prescan includes image data of at least a portion of the tissuecontaining slide, and the image data of the prescan includes a first dimension and a second dimension; generating a first intensity profile of the image data of the prescan, wherein the first intensity profile includes averages of pixel intensities for pixels of the first dimension of the image data of the prescan for each pixel position of the second dimension of the image data of the prescan; retrieving a second intensity profile for a default prescan, wherein: the default prescan includes image data of at least a portion of a different slide, the image data of the default prescan includes the first dimension and the second dimension, and the second intensity profile includes averages of pixel intensities for pixels of the first dimension of the default prescan for each pixel position of the second dimension of the default prescan; generating an intensity ratio profile based on the first intensity profile and the second intensity profile; determining that an average of intensity ratios within a window of pixel positions of the intensity ratio profile is at least one of greater than an upper threshold value and less than a lower threshold value; and in response to determining that the average of intensity ratios is at least one of greater than the upper threshold value and less than the lower threshold value, determining that the prescan includes an artifact.

2. The method of claim 1 , wherein the window of pixel positions is a window of continuous pixel positions.

3. The method of any of the preceding claims, wherein: generating the intensity ratio profile comprises, for each pixel position of the second dimension of the prescan and each corresponding pixel position of the second dimension of the default prescan, generatinga ratio of a first corresponding average pixel intensity of the first intensity profile to a second corresponding average pixel intensity of the second intensity profile, wherein the intensity ratio profile relates intensity ratios to corresponding pixel positions of the second dimension of the prescan and of the second dimension of the default prescan, and the window of pixel positions is a window of corresponding pixel positions.

4. The method of claim 3, wherein the window of corresponding pixel positions is a window of continuous corresponding pixel positions.

5. The method of any of the preceding claims, wherein the pixel intensity data is brightness data.

6. The method of any of the preceding claims, wherein the image data for the prescan is photoresponse nonuniformity data, and wherein generating the first intensity profile comprises: capturing fixed pattern noise (FPN) data for the at least a portion of the tissue-containing slide, wherein the FPN data includes the first dimension and the second dimension; extracting brightness data from the photoresponse nonuniformity data, wherein the brightness data includes the first dimension and the second dimension; generating a brightness intensity profile based on the brightness data, wherein the brightness intensity profile includes averages of pixel intensities for pixels of the first dimension of the brightness data of the prescan for each pixel position of the second dimension of the brightness data of the prescan; generating an FPN intensity profile based on the brightness data, wherein the FPN intensity profile includes averages of pixel intensities for pixels of the first dimension of the FPN data of the prescan for each pixel position of the second dimension of the FPN data of the prescan; and generating the first intensity profile based on the brightness intensity profile and the FPN intensity profile.

7. The method of any of the preceding claims, wherein the portion of the tissue-containing slide depicted by the image data of the prescan does not include tissue.

8. The method of any of the preceding claims, and further comprising, prior to retrieving the second intensity profile: capturing an unvalidated default prescan of the different tissue-containing slide, wherein the unvalidated default prescan includes the image data of the at least a portion of the different slide; generating the second intensity profile based on the unvalidated default prescan; generating an average pixel intensity value for a range of pixel positions of the second dimension of the image data of the unvalidated default prescan, the range spanning a center of pixel positions of the second dimension; determining that the average pixel intensity is within an acceptable pixel intensity range defined by a maximum acceptable intensity and a minimum acceptable intensity; in response to determining that the average pixel intensity is within the acceptable pixel intensity range, fitting a polynomial curve to the intensity profile; determining that a tilt of the polynomial curve is within an acceptable tilt range; determining that a curvature of the polynomial curve is within an acceptable curvature range; determining that a smoothness of the polynomial curve is within an acceptable smoothness range; and in response to determining that the tilt of the polynomial curve is within the acceptable tilt range, determining that the curvature of the polynomial curve is within the acceptable curvature range, and the smoothness is within the acceptable smoothness range, validating the unvalidated default prescan as the default prescan.

9. The method of any of the preceding claims, and further comprising: in response to determining that the prescan includes an artifact, retrieving the image data of the default prescan; capturing a full image of the tissue-containing slide; and correcting the full image of the tissue-containing slide using the image data of the default prescan.

10. The method of claim 9, wherein the full image of the tissue-containing slide includes a larger number of pixels than the default prescan.

11. The method of any of the preceding claims, wherein the window spans 120 corresponding pixel positions.

12. The method of any of the preceding claims, wherein determining that the average of intensity ratios within the window of continuous corresponding pixel positions of the intensity ratio profile is at least one of greater than the upper threshold value and less than the lower threshold value comprises: sliding the window through a plurality of corresponding pixel position ranges; and determining whether an average of intensity ratios within each corresponding pixel position range of the plurality of corresponding pixel position ranges is at least one of greater than the upper threshold value and less than the lower threshold value, wherein the plurality of corresponding pixel position ranges includes all corresponding pixel positions of the intensity ratio profile.

13. The method of any of the preceding claims, and further comprising: mounting tissue onto a slide to create the tissue-containing slide; loading the tissue-containing slide onto a stage of an imaging apparatus; and capturing the prescan using an imaging sensor of the imaging apparatus.

14. The method of any of the preceding claims, wherein: the upper threshold value is a first ratio of 1.05, and the lower threshold value is a second ratio of 0.95.

15. The method of any of the preceding claims, wherein the different slide is a different tissue-containing slide.

16. A system for detecting prescan artifacts, the system comprising: an imaging system; a processor operatively coupled to the imaging system; and at least one memory encoded with instructions that, when executed, cause the processor to: cause the imaging system to capture a prescan of a tissue-containing slide, wherein: the prescan includes image data of at least a portion of the tissue-containing slide, andthe image data of the prescan includes a first dimension and a second dimension; generate a first intensity profile of the image data of the prescan, wherein the first intensity profile includes averages of pixel intensities for pixels of the first dimension of the prescan for each pixel position of the second dimension of the prescan; retrieve a second intensity profile for a default prescan, wherein: the default prescan includes image data of at least a portion of a different slide, and the image data of the default prescan includes the first dimension and the second dimension; and the second intensity profile includes averages of pixel intensities for pixels of the first dimension of the default prescan for each pixel position of the second dimension of the default prescan; generate an intensity ratio profile based on the first intensity profile and the second intensity profile; determine that an average of intensity ratios within a window of pixel positions of the intensity ratio profile is at least one of greater than an upper threshold value and less than a lower threshold value; and in response to determining that the average is at least one of greater than the upper threshold value and less than the lower threshold value, determine that the prescan includes an artifact.

17. The system of claim 16, wherein the window of pixel positions is a window of continuous pixel positions.

18. The system of any of claims 16-17, wherein: the instructions, when executed, cause the processor to generate the intensity ratio profile by, for each pixel position of the second dimension of the prescan and each corresponding pixel position of the second dimension of the default prescan, generating a ratio of a first corresponding average pixel intensity of the first intensity profile to a second corresponding average pixel intensity of the secondintensity profile, wherein the intensity ratio profile relates intensity ratios to corresponding pixel positions of the second dimension of the prescan and of the second dimension of the default prescan, and the window of pixel positions is a window of continuous corresponding pixel positions.

19. The system of claim 18, wherein the window of corresponding pixel positions is a window of continuous corresponding pixel positions.

20. The system of any of claims 16-19, wherein the pixel intensity data is brightness data.

21. The system of any of claims 16-20, wherein the portion of the tissuecontaining slide depicted by the image data of the prescan does not include tissue.

22. The system of any of claims 16-21, wherein the instructions, when executed, further cause the processor to: in response to determining that the prescan includes an artifact, retrieve the image data of the default prescan; capture a full image of the tissue-containing slide; and correct the full image of the tissue-containing slide using the image data of the default prescan.

23. The system of any of claims 16-22, wherein the full image of the tissuecontaining slide includes a larger number of pixels than the image data of the default prescan.

24. The system of any of claims 16-23, wherein the instructions, when executed, cause the processor to determine that the average of intensity ratios within the window of continuous corresponding pixel positions of the intensity ratio profile is at least one of greater than the upper threshold value and less than the lower threshold value by: sliding the window through a plurality of corresponding pixel position ranges; and determining whether an average of intensity ratios within each corresponding pixel position range of the plurality of corresponding pixel position ranges is at least one of greater than the upper threshold value and less than the lower threshold value, wherein the plurality of corresponding pixel position ranges includes all corresponding pixel positions of the intensity ratio profile.

25. A method of validating a default prescan image, the method comprising:capturing a first prescan of a first tissue-containing slide, wherein: the first prescan includes image data of at least a portion of the first tissue-containing slide, and and the image data of the first prescan includes a first dimension and a second dimension; generating an intensity profile of the image data of the first prescan, wherein the intensity profile includes averages of pixel intensities for pixels of the first dimension of the first prescan for each pixel position of the second dimension of the first prescan; generating an average pixel intensity value for a range of pixel positions of the second dimension, the range spanning a center of pixel positions of the second dimension; determining that the average pixel intensity is within an acceptable pixel intensity range defined by a maximum acceptable intensity and a minimum acceptable intensity; in response to determining that the average pixel intensity is within the acceptable pixel intensity range, fitting a polynomial curve to the intensity profile; determining that a tilt of the polynomial curve is within an acceptable tilt range; determining that a curvature of the polynomial curve is within an acceptable curvature range; determining that a smoothness of the polynomial curve is within an acceptable smoothness range; and in response to determining that the tilt of the polynomial curve is within the acceptable tilt range, determining that the curvature of the polynomial curve is within the acceptable curvature range, and the smoothness is within the acceptable smoothness range, validating the first prescan as the default prescan image.

26. The method of claim 25, and further comprising: capturing a second prescan of a second tissue-containing slide, wherein: the second prescan includes image data of at least a portion of the second tissue-containing slide, andthe image data of the second prescan includes the first dimension and the second dimension; capturing a full image of the second tissue-containing slide; determining that the second prescan contains an artifact; and in response to determining that the second prescan contains the artifact, correcting the full image of the second-tissue containing slide using the first prescan.

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