Analytical system and method

Abstract

A computer-implemented method for analyzing an assay performed at each assay site in an array or microarray comprising a plurality of assay sites, the computer-implemented method comprising: receiving at least one image of the assay sites of the array or microarray; processing the at least one image to determine, for each of the assay sites, at least one metric representative of the extent of reaction at the assay site, determining one or more parameters of the assay site, wherein the one or more parameters of at least one of the assay sites of the array or microarray are different from one or more parameters of at least one other assay site of the array or microarray; and determining the extent of reaction at the assay site from the at least one metric of the assay site and the one or more parameters of the assay site.

Description

Technical Field

[0001] The present disclosure relates to an analysis system and method for analyzing reactions, such as an analysis system and method for analyzing assays.

Background Art

[0002] To automate the analysis of chemical assays, analysis systems are available. Such assays are generally performed in a regular array of printed areas configured to hold reactants and test samples. Such assays generally test for the presence or level of an analyte in a test sample. Reactions, often in the form of changes in opacity, color, size, or other detectable changes, are associated with the presence or level of the analyte. Analysis systems are typically provided with sensors, such as digital cameras, to identify the reactions in each print within the array. Thus, for example, digital camera images can be scrutinized and the reactions (e.g., the degree of change in color or opacity) can be identified to determine the presence or level of an analyte within any given printed area.

[0003] Typically, the same type of assay is performed within each printed area, but it may be desirable to perform different types of assays in different printed areas of the array.

[0004] Manual scrutiny of the reactions shown in digital camera images can be performed, which allows a certain degree of expertise and human judgment to be applied, but is often time-consuming, laborious, and prone to human error. Automated determination of reactions can be much faster and increase throughput, but can sometimes be associated with errors, particularly when unforeseen or unclear effects are present, or when the reaction is marginal or boundary-like such that it is generally referred to as an edge case.

[0005] At least some embodiments of this disclosure seek to improve the automated analysis of assays performed on arrays or microarrays containing multiple print regions, particularly when different assays are performed in different print regions. [Overview of the project]

[0006] Various aspects of the present invention are defined in the independent claims. Several preferred features are defined in the dependent claims.

[0007] According to a first embodiment of this disclosure, a computer method for analyzing multiple reactions performed at each site in an array comprising multiple sites, Receiving an image of at least one of multiple regions of the array, For each of multiple body parts, at least one image is processed to determine at least one image metric representing the degree or progression of the reaction in that body part. The determination of one or more parameters for each of multiple body parts, wherein the parameters for at least one body part are different from the parameters for at least one other body part. A computer-aided procedure comprising determining the progression of a response for each of several body parts based on at least one image metric and one or more parameters of that body part.

[0008] The array may be a microarray, such as a multiple microarray or a hybrid array, or it may contain a microarray, or it may be contained within a microarray.

[0009] The reaction may be an assay, or may include an assay. The array site may be an assay site, well, printed area, or reaction site of the array, or may include an assay site, well, printed area, or reaction site. The reaction may include a chemical reaction and / or a biological reaction. The assay may include one or more reagents at the site. The assay may include the addition of a sample to a reagent. The presence of an analyte in the sample may cause a reaction with the reagent. The reaction may result in a spot at the site, which arises from and indicates a measurable change caused by the reaction. As a result of at least one property of the printed material, the reaction may produce a spot characterized by one or more changes such as opacity, intensity, color, size, etc., which can indicate the degree or progress of the reaction. One or more criteria and / or parameters at the site may be a spot or an image portion representing a spot, or may include or represent one. The progress of the reaction may include or indicate a spot grade. Determining the progress of the reaction may be, or may be, included in, the grading or determination of the spot grade. The progress of the reaction or spot grade may be qualitative, such as whether or not a reaction has occurred, or quantitative, such as a value for the progress or degree of the reaction. At least one site metric may be, or may be, a spot metric.

[0010] At least one image may include images of one or more of the assay sites, or a set of images that may individually cover one or more of the assay sites, but collectively cover all of the assay sites.

[0011] Criteria and / or parameters may include at least one criterion having an associated threshold or range. At least one criterion having an appropriate threshold or range may indicate the progress of a response or spot grade. The progress of a response or spot grade may include one or more values ​​representing a response present, no response, or progress of a response, and / or an indication that the progress of a response is indeterminate. An indication that the progress of a response is indeterminate for a site may be, or include, a determination that at least one image metric determined for that site lies between a value or range of values ​​indicating no response and a value or range of values ​​indicating a response. Determining the progress of a site's response may include comparing at least one metric for that site to a criterion function or one or more values ​​that include or depend on the criteria and / or parameters for that site. Determining the progression of a site reaction may include determining whether at least one metric of that site is above or below at least one threshold, or within or outside at least one range, or whether at least one of the criteria is met or not, thereby determining the degree or progression of the reaction or the spot grade.

[0012] Site criteria and / or parameters may depend on one or more of the following: the type of assay performed at the site, the location of the site on the array, the type of sample at the site, etc.

[0013] Site criteria and / or parameters may be specific to the site, or may be adjusted, adapted, or selected for at least the site. Criteria and / or parameters may be predetermined, for example, determined before the analysis of multiple reactions. Criteria or parameters may be, and / or specific to, the type of assay performed at the site, the location of the site on the array, or the sample type at the site. The method may include determining or identifying, for at least one site or each site, the type of assay performed at the site, the location of the site on the array, and the sample type at the site, and selectively applying criteria or parameters to the determined assay type, the location of the site on the array, and / or the sample type for each site.

[0014] The site criteria and / or parameters may be fixed. The criteria and / or parameters for each given assay type, the location of the site on the array, and / or sample type may be fixed. The site criteria and / or parameters may be selectable from a plurality of predetermined criteria and / or parameters associated with different assay types, the location of the site on the array, and / or sample types, but may be fixed once selected. The selection of at least one site or the criteria and / or parameters for each site may include selecting predetermined parameters and criteria and / or parameters for the assay type, the location of the site on the array, and / or sample type for that respective site. Multiple sites (e.g., assay sites or print regions) may be included in the array of sites. The sites may be arranged in columns. Each column of sites may be staggered with respect to at least one or each adjacent column of sites. The array may include alternating staggered columns of sites, where each alternating column of sites may be positionally aligned. The array may conform to American National Standards Institute (ANSI) / Laboratory Automation and Screening Society (SLAS) standards. The array may include one or more contrasting parts, which may be located at one or more of the outer corners of the array or part array. The contrasting parts may be located at the vertices of a parallelogram.

[0015] At least one metric for a region may include, depend on, or represent, the pixel intensity of an area of ​​the image representing at least a portion of that region, such as a spot in that region. At least one metric for a region may include, depend on, or represent, the mean pixel intensity of at least a portion of an area of ​​the image representing that region, such as a spot in that region. At least one metric for a region may include, depend on, or represent, the difference between the pixel intensity or mean pixel intensity of at least a portion of an area of ​​the image representing a spot in that region and, for example, the background pixel intensity or mean background pixel intensity of a portion of the image representing a non-spot area. At least one metric for a region may include, depend on, or represent, a measure of the variance or standard deviation of the background pixel intensity.

[0016] At least one metric for a region may be, or may include, a signal-to-standard deviation ratio (SSR), such as the difference between the mean pixel intensity of at least a portion of the image region representing the spot in that region and the mean pixel intensity of at least a portion of the background, divided by a measure of the variance or standard deviation of the background pixel intensity of at least that portion of the background.

[0017] At least one metric for a region may include, depend on, or represent a measure of the variance or standard deviation of pixel intensity or the mean value of pixel intensity for at least a portion of the image region representing the spot in that region. At least one metric for a region may include, depend on, or represent a percentage or fraction of pixels representing the spot in that region that is greater than or equal to a pixel intensity threshold.

[0018] The method may include at least one of cropping an image around an array of regions. The method may also include gridding the image into multiple segments, each segment surrounding a spot and / or region.

[0019] One or more, or each, of the determination of at least one metric of the excision, griding, and / or spot may include using associated excision, griding, and / or metric determination parameters, which may be common parameters shared with one or more, or each or all, other spots or sites having the same assay type. The criteria and / or parameters used to determine the progression of the site's response from at least one image metric of that site may be site-specific.

[0020] This method may include, for example, preprocessing or cleaning the image by filtering or smoothing it. The filtering or smoothing can be configured to selectively filter out noise from the image.

[0021] Image cropping may include determining an image cropping region, which may be a subset of the entire area of ​​the image in which multiple parts are shown. Image cropping may include cropping an image around multiple parts on an array. The method may include identifying one or more or each of the control parts, which can define the outer corners of the part array on the array and can be used to define or define the corners of the image cropping region.

[0022] This method may include detecting or identifying a portion of an image corresponding to a spot within the reaction site. This method may also include, for example, using object recognition techniques to detect or identify a shape within a cropped image that matches the description of the spot. Detecting or identifying a portion of an image corresponding to a spot within the reaction site may be based on one or more parameters, such as assay-specific or site-specific parameters.

[0023] This method may include filtering out determined or identified spots in an image. This method may include filtering out determined or identified spots where the value of at least one of the metrics falls below a predetermined or determined threshold, which may be included in or derived from at least one of the parameters, such as assay-specific or site-specific parameters.

[0024] The method may include identifying the location of reference points, which may be control areas. For example, reference points, such as control areas, may be identified by identifying the furthest or most distant parts shown in the image and optionally applying known or predicted geometric shapes of the array. The method may include filtering out determined or identified spots using spatial thresholds based on known or predicted geometric shapes of the array, such as known or predicted arrangements of parts on the array, which may include arrangements of reference points, such as control areas. The method may also include filtering out spots that are not within the area defined by the control areas at each corner, such as outside the area where parts are expected to be found.

[0025] The method may include gridding an image, which may include determining a grid that includes a plurality of segments, which may be based on, for example, the determined positions of reference points such as control sites, and / or the determined positions of other sites or spots, and / or the expected geometric shape of sites on the array. Each segment may include a single corresponding assay site of the array. The cells may be non-overlapping. The cells may correspond to the arrangement of sites on the array. Gridding may be based on one or more of the parameters, such as assay-specific or site-specific parameters, for example.

[0026] Determining at least one metric of a site may include directly determining at least one of the metrics from the image. Determining at least one metric of a reaction site may include indirectly determining at least one metric from at least one of the metrics directly determined from the image. Determining at least one metric of a site may be based on one or more of the parameters, such as assay-specific or site-specific parameters, for example.

[0027] Non-exclusive examples of metrics include the average image background pixel value or intensity, the average standard deviation or variance of the image background position or intensity, the average and / or standard deviation or variance of the pixel value or intensity of the spot, the edge radius of the spot, the shape radius of the spot and / or the isoperimetric coefficient, the edge sharpness or variance of the spot, the circular signal-to-standard deviation ratio, the circular average, the circular standard deviation, the circular background average and / or standard deviation or variance, the circular intensity value exceeding the background, average, standard deviation and / or variance of the pixel value or intensity of the spot's image background, the signal-to-standard deviation ratio (SSR) of the spot, the difference between the pixel value or intensity of at least a portion of the spot and the pixel value or intensity of the background, etc.

[0028] The determination of one or more parameters of a site may include associating with an assay being performed within each site and / or determining or identifying each parameter associated with a particular site, and applying those parameters of each site. The parameters may be pre-determined for a given array, assay type, sample type, location, location within an array, site, etc.

[0029] In one example, the parameters of a site may be retrieved from an input file entered by a user using a user input device such as a keyboard.

[0030] The method may include identifying or receiving one or more identifiers indicating an array, or at least one or each of the sites on the array, or an assay or assay type being performed at at least one or each site on the array. The determination of one or more parameters of a site may include retrieving or determining the parameters from a look-up table, database, data store, or a function associating the parameters with the array, assay type, sample type, and / or site based on the determined identifier, for example. One or more indicators may be received from an input device and / or data store, for example as part of a configuration file. The input device may include a user input device such as a keyboard for receiving user input, a scanner for barcodes or QR codes or other machine-readable codes, a RFID tag reader, a machine-readable data device such as an optical tag reader, etc. One or more indicators may be received from a Laboratory Information Management System (LIMS). The method may include reading or receiving the identifier in another manner and then using the identifier to access, for example, one or more criteria and / or parameters associated with the identifier from a data store.

[0031] The parameters may be based on past assays, historical data, test assays, modeling, or predictive data. Determining one or more parameters for a site may involve determining the parameters for one or more or each different assay type and / or site using machine learning, artificial intelligence, or any other learned or trained algorithm or function, and / or using historical or modeling data, etc.

[0032] The parameters may include global parameters. Global parameters may be parameters that are functions of the assay; that is, global parameters may vary from assay to assay and / or may be associated with the corresponding assay type. The same value of a global parameter may be applied to multiple sites or spots. One or more of the following may be performed based on global parameters: image gridding, image cropping, and / or determination of one or more metrics representing the progress or degree of the reaction at each site.

[0033] The parameters may include unique parameters. Unique parameters may be parameters that are functions of a specific site on an array or spot, such as a function of the assay type and site on the array or spot. The values ​​of unique criteria may be individually variable for each site. At least one, more, or all of the parameters used to determine the progress of the reaction at each assay site from at least one image metric of that assay site may be unique parameters.

[0034] Non-exclusive examples of parameters that may be global standards include the maximum mean image background intensity value, the maximum standard deviation or variance of image background intensity, etc.

[0035] The parameters for a site on the array can specify which value of at least one metric indicates a response or no response at that site on the array. The parameters can specify one or more thresholds, criteria and / or ranges that specify the progress or degree of the response, for example, whether a spot is responding, unresponsive, or indeterminate.

[0036] Non-exclusive examples of parameters include one or more or each of the following: the maximum mean intensity value of a reaction site, the maximum standard deviation or variance of the intensity of a reaction site, the signal-to-standard deviation ratio limit of a reaction site, the pixel value or intensity threshold across the background of a reaction site that should be considered empty or filled, the maximum and / or minimum circle or shape radius of a detected shape that should be considered a spot, the minimum tolerance for the spot edge metric, the maximum spot tolerance background cv value for verifying SSR, the spot-reactive SSR value interval, the spot-non-reactive SSR value interval, the spot-reactive mean interval, the spot-non-reactive mean interval, the maximum circle tolerance background cv value for verifying SSR, the circle-reactive SSR value interval, the circle-non-reactive SSR value interval, the circle-reactive delta value interval, the circle-non-reactive delta value interval, etc.

[0037] Determining the progress of a reaction at an assay site or spot may involve applying one or more metrics and parameters of the reaction site or spot to one or more logic tests, the results of which one or more logic tests indicate whether or not a reaction occurred at the site, the progress or extent of the reaction at the site, the progress or extent of activity at the site and / or activity at the site, or the presence or absence of an analyte at the site. Determining the activity and / or progress of activity at the site may involve determining whether or not something detectable is present, such as a blob or the shape of a non-background result, but it is unclear whether the detected activity is a spot or a reaction. The logic tests may involve comparing one or more metrics of the site with one or more parameters of the site to determine the progress of the reaction.

[0038] According to a second embodiment of the present disclosure, the processing system is configured to perform the method of the first embodiment for analyzing one or more assays performed at each assay site from a plurality of assay sites of an array.

[0039] The processing system may be configured to receive at least one image of an assay site in the array. For each assay site, the processing system may be configured to process at least one image to determine at least one image metric representing the progress or degree of the reaction at that assay site. For each assay site, the processing system may be configured to determine one or more parameters of that assay site, such that at least one parameter of the assay sites in the array is different from the parameters of at least one other assay site in the array. For each assay site, the processing system may be configured to determine the progress of the reaction at that assay site from at least one metric and one or more parameters of that assay site.

[0040] The processing system may include a data storage device. The processing system may include a communication module. The processing system may include one or more output devices. The processing system may include one or more input devices.

[0041] The processing system may comprise one or more processors, which may be single or multi-core processors. One or more processors may include one or more central processing units, graphics processing units, numerical coprocessors, tensor processing units, etc. Data storage devices may include solid-state memory, magnetic memory, optical memory, etc. The communication unit may be configured to communicate via wired and / or wireless communication, for example, via a network such as a LAN, WAN, the Internet, one or more cellular networks, Ethernet networks, or fiber optic networks, to communicate with remote and / or local systems. At least one output device may include a display or other visual output device, an audio output device, and / or a haptic output device.

[0042] At a minimum, the on-input device may include a keyboard, touchscreen, trackball, touchpad, joystick, or voice recognition-based input device. The input device may also include machine-readable data devices such as a barcode or QR code or other machine-readable code scanner, RFID tag reader, or optical tag reader. The processing system may be configured to read or otherwise receive codes associated with an array or parts on the array, and then include using the codes to access one or more criteria and / or parameters associated with the codes, for example, from a data store.

[0043] The processing system may be configured to retrieve or determine parameters from configuration files, lookup tables, databases, data stores, or functions that associate each parameter with a respective assay type, sample type, and / or site. The processing system may be configured to communicate with at least one remote processing system. The remote processing system may include a server, cloud computing resources, workstations, personal computers, etc. The remote processing system may include a remote data storage device.

[0044] According to a third embodiment of the present disclosure, there is an analysis system for analyzing one or more assays performed at each assay site from a plurality of assay sites of an array, the analysis system comprising the processing system of the second embodiment described above. The analysis system may comprise at least one imaging device configured or configurable to collect at least one image of the array and to communicate at least one image to the processing system.

[0045] The processing system may be configured to receive at least one image of an assay site in the array. For each assay site, the processing system may be configured to process at least one image to determine at least one image metric representing the progress or degree of the reaction at that assay site. For each assay site, the processing system may be configured to determine one or more parameters of that assay site, such that at least one parameter of the assay sites in the array is different from the parameters of at least one other assay site in the array. For each assay site, the processing system may be configured to determine the progress of the reaction at that assay site from at least one metric and one or more parameters of that assay site.

[0046] According to a fourth embodiment of the present disclosure, the present invention relates to a computer program product that, when implemented on a processing system, includes instructions causing the processing system to perform the method of the first embodiment of the present disclosure. The computer program product may be embodied on a non-temporary and / or tangible computer-readable medium.

[0047] Each of the features and / or combinations of features defined above or below in accordance with any aspect, example, or embodiment of the present disclosure may be used separately and individually, alone, or in combination with any other defined features in any other aspect, example, or embodiment of the present disclosure.

[0048] Furthermore, this disclosure is intended to cover apparatus configured to implement any of the features described herein in connection with methods and / or methods of using or generating, or using or manufacturing, any of the apparatus features described herein.

[0049] These and other aspects of the present disclosure will be described here by reference to the attached drawings, for illustrative purposes only. [Brief explanation of the drawing]

[0050] [Figure 1] This is a schematic diagram of a computerized assay analysis system. [Figure 2] This figure shows an example of annotated images of an assay on an array collected using the system shown in Figure 1. [Figure 3] This figure shows the changes in assay attributes of a specific metric used in the spot grading process. [Figure 4] This figure shows the changes in assay attributes of a specific metric used in the spot grading process. [Figure 5] This figure shows the changes in assay attributes of a specific metric used in the spot grading process. [Figure 6]This figure shows the changes in assay attributes of a specific metric used in the spot grading process. [Figure 7] This figure shows the changes in assay attributes of a specific metric used in the spot grading process. [Figure 8] Figure 1 is an overview of assay analysis performed by a computerized assay analysis system. [Figure 9A] This is an overview of the image analysis that forms part of the assay analysis shown in Figure 8. [Figure 9B] Figure 9A is a detailed flowchart of the image analysis process. [Figure 10] This is another flowchart of the image analysis shown in Figures 9A and 9B. [Figure 11] This is a schematic diagram of the grid matching process performed as part of the cutting process. [Figure 12] Figure 11 is a schematic diagram of the microarray grid generated by the gridding process. [Figure 13] This is a flowchart of the process for determining metrics, which forms part of the image analysis in Figures 9A, 9B, and 10. [Figure 14] Figure 17 is a flowchart illustrating the segmentation process, which is performed as part of the metric determination. [Figure 15] Figure 8 is a flowchart illustrating the operation of the grading rule engine used for assay analysis. [Figure 16] Figure 15 is a flowchart illustrating the logic for determining the suitability of an analysis for use in the grading rule engine shown as an example. [Figure 17] Figure 15 is a flowchart illustrating the spot determination logic for use in the grading rule engine shown as an example. [Figure 18] Figure 15 is a flowchart illustrating the object determination logic used in the grading rule engine shown as an example. [Figure 19]Figure 15 is a flowchart illustrating the clear object determination logic for use in the grading rule engine shown as an example. [Figure 20] Figure 15 is a flowchart illustrating the spot classification logic used in the grading rule engine shown as an example. [Figure 21] Figure 15 is a flowchart illustrating the spot classification verification logic for use in the grading rule engine shown as an example. [Modes for carrying out the invention]

[0051] Figure 1 shows an assay analysis system 5 for analyzing assays performed on an array 10, such as a multiple microarray or hybrid array, which includes multiple assay sites or print regions 15, and in which case individual assays can be performed within each assay site 15 or print region. The system 5 comprises one or more sensors, in the form of a digital camera 17 in this example, configured to collect images of the array 10. Although only one camera 17 is shown in Figure 1, two or more cameras 17 may be provided. In this example, the camera 17 is configured to capture an image of the entire array 10, for example, the field of view 18 of the camera 17 encompasses the entire array 10, or at least all of the assay sites 15 of the array 10. However, the camera 17 may be configured to capture images of only a portion of the assay sites 15 of the array 10, or multiple different cameras 17 may be configured to capture images of multiple different subsets of the assay sites 15 of the array 10, or to capture the array 10 from multiple different angles. System 5 includes an analysis system 20 configured to receive images collected by the camera 10 and analyze the images to determine the state of the reaction within a specific assay site 15.

[0052] The analysis system 20 comprises a processing system 25, a data storage device 30, a communication module 35, one or more output devices 45, and one or more user input devices 40. The processing system 25 comprises one or more processors, which may be single-core or multi-core processors. One or more processors include one or more central processing units and optionally also include one or more graphics processing units, numerical coprocessors, tensor processing units, etc. The data storage device 30 may include solid-state memory, magnetic memory, optical memory, etc. The communication unit 35 can be configured to communicate via wired and / or wireless communication. In this example, the communication unit 35 is configured to communicate with remote and / or local systems via a network such as a LAN, WAN, the Internet, one or more cellular networks, Ethernet networks, or fiber optic networks. At least one output device 45 may include a display or other visual output device, an audio output device, and / or a haptic output device. At least one of the following on-input devices 40 may include a keyboard, touchscreen, trackball, touchpad, joystick, voice recognition-based input device, RFID tag reader, barcode or QR code reader, etc.

[0053] The analysis system 20 can be configured to communicate with at least one remote processing system 50. The remote processing system 50 may include a server, cloud computing resources, a workstation, a personal computer, etc. The remote processing system 50 includes a remote data storage device 55. Thus, any or all of the method steps described herein, in particular any method steps relating to data processing and / or data storage, may be performed using the processing system 25, and at least one remote processing system 50 or the method steps described herein may be distributed between the processing system 25 and at least one remote processing system 50.

[0054] An example of an image of array 10 collected by the system in Figure 1 is shown in Figure 2. Part of the assay site 15a includes a spot indicating a reaction, such as a reaction indicating the presence of the analyte; part of the assay site 15b indicates no reaction, which may indicate the absence of the analyte; and part of the assay site 15c is empty. Spots are generally associated with changes such as opacity or color, which represent the progress or degree of the reaction. Importantly, the inventors have recognized a situation in which multiple different assay sites 15a, 15a' of array 10 are used to perform reactions having different reaction intensities. Different reaction intensities may be associated with different assays, i.e., different types of assays are performed at different assay sites 15, and at least one or each of those assays may be associated with different reaction intensities to at least one other assay. In another example, the reaction intensity may be sample-dependent or may vary from assay site to assay site.

[0055] Analysis of reactions within each assay site 15 involves determining one or more metrics of the reactions within the assay site 15 and applying logic, such as comparison with thresholds, that depends on one or more parameters to obtain a measure of the reaction state (e.g., whether or not a reaction occurred, i.e., a qualitative determination, or a value representing the progress or degree of the reaction or activity, i.e., a quantitative determination, or an indication of whether or not it is active). This may be done by selecting thresholds for all reactions across all assay sites, such as the assay and the sample. However, the inventors have noticed that the dependence of each analysis's attributes on a given metric can vary considerably from one assay to another.

[0056] This is evident from Figures 3 to 7, each showing how different attributes (accuracy, specificity, and sensitivity) change with exemplary metrics (edge ​​metrics) for different assays. Therefore, in these examples, if a common edge metric threshold is set for the assay shown in Figure 3, an edge metric threshold of 20 or higher may be desirable. However, for the assay shown in Figure 4, an edge metric threshold of 20 or higher may result in undesirably low sensitivity for that assay, and an edge metric threshold of 5 to 10 may be more preferable. From each of the assays shown in Figures 3 to 7, it can be seen that different metric thresholds may be better for different assays. Recognizing these issues, the assay analysis system 5 is specifically configured to enable enhanced analysis of arrays used to perform different assays at different assay sites 15 of the same array 10.

[0057] Figure 8 provides an overview of a computerized method for analyzing array 10 to determine the progress or extent of the reaction within assay sites 15 of array 10. This method can be carried out by the processing system 25 shown in Figure 1, but one or more of these steps, or each of them, may be carried out by the remote processing system 50, or different steps may be carried out by the processing system 25 and the remote processing system 50 so that the processing is effectively distributed between them.

[0058] Step 705 shows the execution of an assay at each of the multiple assay sites 15 of the microarray 10. Although the execution of each assay is known in the art, in this example different assays are performed at at least some of the assay sites 15 of the microarray 10 and captured in image 805 collected by the assay analysis system 5 in step 710.

[0059] Algorithm 810 provides image analysis as part of the assay analysis, which is shown in more detail in Figures 9A, 9B, and 10.

[0060] The processing system 25 and / or the remote processing system 50 receive images 805 of the array 10 collected by the camera 17 (see Figure 1). The processing system 25 and / or the remote processing system 50 perform the algorithm 810 shown in Figures 8 and 9A, which processes the images 805 to determine a metric indicating the progress or degree of the reaction within each assay site 15. The algorithm 810 uses various parameters 815 to determine a metric indicating the progress or degree of the reaction in each assay site 15.

[0061] The parameters 815 used to determine the metric are global and applicable regardless of the assay being performed. However, the metric analysis by the grading engine is specific and uses non-general parameters; that is, different parameters may be used for different assay sites 15 or groups or subsets of assay sites 15 within the same array 10. For example, different assay types may be associated with different parameters. When array 10 is used to perform different assays included in image 805, at least one parameter of the assay site 15 will be different from the parameters of at least one other assay site 15 within the same array 10. The parameters for each assay type are generally predetermined, and appropriate predetermined parameters are selected for each different assay site depending on the type of assay being performed at that site. The assay types being performed within each different assay site and / or the parameters associated with those assay types are generally provided in a configuration file. For completeness, the provision of parameters is not inherently limited, and in other possible embodiments, the assay type being performed within each different print area may be determined from user input received from one of the input devices, such as a keyboard or an identifier reader provided on or with the array, such as a QR code, RFID tag, holographic tag, or barcode. For example, the assay type for each assay site 15 may be entered by the user using a keyboard, or the identifier on the array may be read by a reader, and pre-stored data for that assay type may be retrieved from a database, lookup table, or other form of data file which may be stored on and / or in the remote data storage device 55. Additionally or alternatively, parameters may vary depending on the sample type, sample size, the location of the individual assay sites within the array 10, and / or other factors that affect the reaction intensity or the progress or extent of the response to the reaction.In this case, it should be understood that the parameters for each assay site 15 or group of assay sites 15 can be determined in the same manner as described above.

[0062] The algorithm 810 is configured to analyze an image, identify regions of the image associated with different assay sites 15, determine one or more metrics for the regions of the image associated with different assay sites 15, and determine the progress or degree of the reaction of the different assay sites 15 (reactive or non-reactive, or a value for the progress or degree of the reaction or activity) based on the metrics and parameters 815.

[0063] As shown in Figures 9A, 9B, and 10, the output from algorithm 810 may include a metric 820 indicating the progress or degree of the reaction at one or more of the assay sites 15, or at each of them. The metric for assay site 15 may be a simple indication of reaction present or not, or it may be more nuanced, such as a value that gives the progress or degree of the reaction within the assay site, and is processed by a grading engine to determine the progress or degree of the reaction (e.g., spot grade) 720. The determined spot grade may then be presented for interpretation by the user 725. The output may also include one or more errors and / or warnings 825, for example, if the algorithm failed to determine the progress or degree of the reaction at a given assay site 15, or if any other error occurred during the process.

[0064] A more detailed overview of the process shown in Figures 8 and 9A is shown in Figure 9B. As shown in Figure 9B, the process has as input an image 805 of array 10 collected by camera 17.

[0065] The input image 805 is cropped to 905, resulting in image 805 being tightly cropped to an area of ​​image 805 containing assay sites 15 (and any other features used by the process, such as control assay sites / spots) based on cropping parameters 910 that control the cropping process 905. The microarray 10 can be supplied in standard sizes, dimensions, and layouts. The cropping process is intended to reduce the area of ​​the original image 805 to an area of ​​image associated with the array of assay sites 15 and any other features used by the process, such as control assay sites 15 / spots. The cropping process is generally known, and any suitable cropping process may be used. An example of a suitable cropping process involves identifying corner control spots and using them to determine the array region in the image where the array of assay sites 15 is located. This region is defined based on the positions of four control spots and the safety margin required to take into account the tolerance of the spot placement. The image can then be cropped around the array region.

[0066] The cropped image output from the cropping step 905 undergoes gridding 915, which divides the cropped image into multiple segments 925 (see Figure 2), with each segment 925 of the image containing a single assay site 15. Gridding 915 is performed based on gridding parameters 920 that control the gridding process 915. The gridding parameters 915 may be common parameters specific to the assay under test. The grid can be superimposed on the image. This involves determining the most likely positions of the grid based on the locations of corner spots. The segments 925 of the grid model corresponding to the corner control spots are known for the array 10, and the grid model can be applied to the image 805 using the determined positions of the corner control spots, as shown in Figure 11.

[0067] The gridding process 915 includes constructing a model of the array of assay sites 15 in array 10 based on the geometric definition of the array layout of the assay sites and the positional tolerance of the spots within the assay sites 15. The grid is shown in Figure 12 and is organized into rows and columns of segments 925, each segment 925 containing a single assay site 15. A print domain corresponding to a single assay site 15 contains a single spot, and the spot cannot come into contact with an adjacent spot. As a result, the grid is organized into rows and columns of print domains corresponding to segments 925 of the grid. The grid model (grid descriptor) is stored in data storage device 30 and / or remote data storage device 55.

[0068] Once the image segment 925 is generated, the spot metric 930 of the spot within the given assay site 15 can be generated based on the metric generation parameter 935.

[0069] A spot metric 930 can be any metric of the image associated with a spot that can be used to characterize a spot exhibiting a reaction within a given assay site 15. For example, at least one of the spot metrics 930 for a given spot may include, or represent, the pixel intensity of the area of ​​the image representing that site. A particular example of this metric is a spot metric 930 for a given spot that includes, or represents, the mean pixel intensity of at least a portion of the area of ​​the image representing that spot at the reaction site, and / or the difference between the mean pixel intensity of the area of ​​the image representing that spot and the mean background pixel intensity. A particular example of a suitable metric for each spot is the standard deviation ratio (SSR), which is the difference between the mean pixel intensity of at least a portion of the image representing a given spot and the mean pixel intensity of at least a portion of the background, all divided by a measure of the variance (e.g., standard deviation) of the background pixel intensity of at least that portion of the background.

[0070] The spot metric calculation 930 can be designed to be reproducible and generally the same regardless of the spot being analyzed, for example, by using a mask to select pixels from a specified area of ​​the spot and the background, or by using the average pixel intensity value. Therefore, the metric generation parameters 935 can also generally be general or common across spots, regardless of the assay type.

[0071] Once a spot metric 930 is determined for any spot of interest, the spot metric can then be evaluated using the grading engine 940 to determine the progress or extent of the reaction for each reaction taking place within each assay site 15 containing each spot. The grading engine 940 applies spot-specific (and also assay-specific and / or sample-type-specific, etc.) grading parameters 945 and / or grading rules 950 to the determined spot metric for a given spot in order to determine the progress or extent of the reaction (e.g., spot grade) for that given spot / assay site 15. As described above, when different spots / assay sites 15 belong to different assays and / or sample types, the grading parameters 945 and / or rules 950 applied by the grading engine 940 to one or more spot / assay sites 15 will differ from those applied to any other spot / assay site 15 in order to determine the progress or extent of the reaction (spot grade) for each spot / assay site 15.

[0072] Thus, for example, common or general parameters are applied to one or more of the cutting 905, gridding 915, and / or spot metric calculation 930, where appropriate, thereby facilitating the efficient handling of parameters for which individual parameterization is less effective. In contrast, if using parameters specific to the assay, sample type, or other factors could result in significant differences in the performance and quality of the assay being performed, then the custom rules 950 and / or parameters 945 are determined and used by the grading engine 940 for different spot / assay sites 15 and / or different groups of spot / assay sites 15 on the same array 10. However, it will be understood that in other embodiments, one or more or each of the cutting 905, gridding 915, and / or spot metric calculation 930 may be performed using parameters specific to the assay site 15 (e.g., specific to the assay, spot location, and / or sample type).

[0073] An alternative flowchart illustrating the method in Figure 9B is shown in Figure 10, which shows the provided and loaded input image 805. Parameters in the form of a grid construction descriptor 1007 are generated and used for cropping 905 and gridding 915 of the input image 805. A metric 1012 for each segment 925 of the grid is determined (step 1008) and output to the spot grading engine 940 in the image analysis output file, which uses the metric to grade the spots within each segment 925 / well 15. The results of the image analysis are also output (step 1014). The output results of step 1014 may include, for example, the determined metric 1012 and any additional image analysis data such as error indications that may be useful or required for further investigation.

[0074] The grid model segment 925 corresponding to the corner reference spot 1747 is identified for array 10, and the grid model can be applied to image 805 using the determined positions of the corner reference spot 1747 and other spots, as shown in Figures 11 and 12.

[0075] More details on the generation of metrics 930 / 1008 shown in Figures 9B and 10 are shown in Figure 13. Metrics 930 / 1008 are determined from the image of each segment 925 / print domain in the gridded image. The metrics 930 / 1008 of segment (print domain) 925 are input to the grading rules engine 940, which uses the metrics to determine the grade or reactivity of each assay performed within the associated segment (print domain) 925.

[0076] As shown in Figure 13, the metric generation process includes calculating a shape (e.g., a circle) metric 1805 1732. The shape metric is identified by using object recognition techniques to identify a shape (but not limited to a circle) that represents a reaction spot within a segment, and then determining the metric for that shape. The shape metric may include, for example, the circle center coordinates, the circle radius, the circle mean (i.e., the average pixel value of the circular area defined by the circle coordinates and radius), the circle standard deviation (i.e., the standard deviation of the pixel values ​​within the circular area), the circle background mean (i.e., the average pixel value within the background area), the circle background standard deviation (i.e., the standard deviation of the pixel values ​​within the background area), the circle SSR (signal-to-standard deviation ratio of the circular area), or any alternative metric that indicates any of the above.

[0077] The metric generation process also includes calculating the domain metric 1810 for each segment (print domain). Domain metrics can include metrics such as the domain mean (the average of the pixel values ​​in the area defined by the segment coordinates and the entire radius) and the domain standard deviation (the standard deviation of the pixel values ​​in the area defined by the segment coordinates and the entire radius). Domain metrics are calculated based on the position and size of the individual segments in the grid.

[0078] In addition to calculating the domain metric 1810, the process also includes calculating the default domain metric 1815 for each segment (print domain), where the default domain is a circle of default radius located at the center of each segment. Examples of default domain metrics include one or more, or each of, the mean pixel intensity of the default domain, the standard deviation of the pixel intensity of the default domain, the mean of pixels in a certain area of ​​the background region of the default domain, the standard deviation of pixels in a certain area of ​​the background region of the default domain, and the signal-to-standard deviation ratio (SSR) of the default domain.

[0079] The metric generation process further includes calculating spot metrics. Spot metrics are extracted from each segment (print domain) by a segmentation method. The purpose of the segmentation step is to detect whether an object (e.g., some form of activity or "blob," which may or may not be a spot caused by and / or indicating the progress of an assay reaction) exists within the segment (print domain) under consideration, and if so, to determine the location and size of the object. Corresponding metrics are calculated for both the object and the background surrounding it. If no object is found within the segment (print domain), no spot metrics are generated. If an object is detected, the spot metrics are determined and then used in an analysis performed by a grading rules engine to determine whether the object is a spot, and if so, to determine the spot's grade. Possible spot metrics include spot center coordinates, spot radius, spot mean and average of pixel values ​​within the area defined by the spot radius, standard deviation of pixel values ​​within the area defined by the spot mean and spot radius, mean and / or standard deviation of the area defined by the background, spot threshold, spot edge metric, spot isoperimetric coefficient, and / or spot signal-to-standard deviation ratio.

[0080] The determination of the spot metric begins with segmentation step 1820, which is detailed in Figure 14.

[0081] The segmentation process 2005 shown in Figure 14 includes the creation of an erosion-processed image 2405. This involves applying a structured element of size and shape set by erosion parameters 2410, which includes data that sets the shape and size of the erosion element. The erosion element is a matrix placed on each image pixel, and the value of the erosion-processed pixel is the smallest input pixel found on the matrix. Erosion is used to determine an estimate of the local background. After erosion, the structured element can be selected so that the center of the segment reflects the local background.

[0082] For each segment 925, the segmentation process shown in Figure 14 includes determining a pixel intensity-based metric for the segment 925. In this example, the metric for each segment 925 is the IQ3 metric 2415 calculated for each segment 925. The IQ3 metric for a segment 925 is the pixel intensity of the image at the center of that segment 925.

[0083] In step 2430 of Figure 14, thresholding is applied to segment 925. Thresholding converts a color or grayscale image into a black and white image by applying a threshold to the pixel intensity, where pixels above the threshold are determined to be white and pixels below the threshold are determined to be black. The threshold may be optionally dynamically determined (i.e., to apply adaptive thresholding) or provided as a parameter.

[0084] In step 2435 of Figure 14, the thresholded image output from step 2430 is analyzed to determine any contours within the image. Contours and edges can be identified using various techniques, and the appropriate technique can be selected.

[0085] In step 2440 of Figure 14, the contours found in step 2435 are checked for each segment 925 to determine if they are close to the boundary width threshold of the segment 925 boundary (provided as input parameter 2005). If so, they are removed. If two or more contours remain after this process, in step 2450, the largest contour is selected for further processing, and the remaining contours are discarded. If no contours are found for a given segment 925, that segment 925 is labeled "not segmented" or "no blob found," and no further processing is performed on that segment 925.

[0086] For the segments 925 that have not yet been labeled as "not segmented," in step 2455, the convex hull of the spot 2710 within those segments is determined. The convex hull or convex envelope 2705 of the spot is the smallest possible convex shape containing the spot 2710.

[0087] In step 2460, the minimum circumscribed circle for each segment is determined. The minimum circumscribed circle is the smallest possible circular shape that encloses the convex hull. The coordinates and radius of the minimum circumscribed circle's center are the output of the segmentation stage.

[0088] For each segment 925, if no blobs are found in the segmentation process 1820 (i.e., it is not segmented), the spot metric is not determined for that segment 925. However, if it is determined that a spot exists within a given segment 925, then in step 1825, the segment 925 is divided into a spot portion and a background portion.

[0089] The portion of the image representing the assay site 15 associated with segment 925 can be divided into a portion representing the spot and a portion representing the background. The portion representing the spot is used, for example, to calculate the average intensity of the spot by taking the average of all pixel intensity values ​​of the pixels contained within the area of ​​the portion representing the spot.

[0090] The portion representing the background is used to calculate the mean intensity and standard deviation of the background within the portion of the segment that does not contain the spot (e.g., the mean and standard deviation of the pixel intensity values ​​of pixels that represent the background) (or other measures of variance).

[0091] For segments or spots that have been determined to be segmented (i.e., where a blob or some form of activity is present), the process can then proceed to determine the spot metric necessary to evaluate the spot grade or the progress or extent of the reaction for each spot or assay site 15 that had not previously been considered unreactive or to have technical errors.

[0092] Step 1830 of the process in Figure 13 involves pixel filtering the image to remove pixels that are atypical due to having extremely high or low intensity. Image artifacts are typically very high in intensity, and this process removes any residuals of these artifacts. Pixels with very low intensity may indicate noise. Pixel filtering applies high and low pixel intensity thresholds, which are parameters of the process, to both the spot pixel population and the background pixel population, removing pixels below the low intensity threshold and pixels above the high intensity threshold.

[0093] In step 1835, the spot metric used to determine the spot grade / reaction progression or degree is determined. Various metrics may be used. One metric is the signal-to-standard deviation ratio (SSR), which can be calculated for each spot / segment 925 / assay site 15 using the following formula:

[0094]

number

[0095] Other examples of metrics that may be used in addition to or as an alternative to SSR include IQ1, IQ2, IQ3, and IQ4, as follows:

number

number

[0096] Other suitable metrics may be apparent to those skilled in the art from the teachings of this disclosure. Any determined metrics are output to the grading rules engine 940. The grading rules engine obtains metrics for various assay sites that generally exhibit image characteristics, such as circular metrics, domain metrics and / or spot metrics, and uses these to determine the spot grade or reaction progress or degree that characterizes the assay being performed at the associated assay site.

[0097] The grading rule engine 940 operates according to the logic outlined in Figure 15. Figure 15 is a flowchart that provides an overview of the rules used in the spot grading process, i.e., the generation of metrics for the spot / assay site 15 930, and outlines the operation of the grading engine 940. The application of individual rules by the grading engine 940 is described in more detail below with respect to Figures 24-29.

[0098] In this example, the grading parameters 945 and rules 950 (as shown in Figure 9B) used by the grading engine 940 to determine the spot grade or the progress or extent of the reaction can be either global or specific. Global parameters are specific to the type of assay, while specific parameters are defined as a function of both the assay and the location of the spot / assay site 15; that is, the parameter depends on both the assay site 15 / spot and the assay.

[0099] The spot grading performed by the grading rule engine 940 considers the array images 805 of the assay sites 15 within the array 10 (appropriately cropped 905, gridded 915 to form segments) along with the metrics of each segment 925 / assay site 15, and delivers the grading results for the corresponding spots / assay sites 15 / segments 925. In the specific example shown in Figure 23, the grading engine 940 applies a set of six rules to each segment 925 / assay site 15 of interest, as follows: Rule 00: Identify images unsuitable for analysis. Rule 1205: Rule 01: Rule 1210 to identify whether segment 925 is empty or contains an object. Rule 02: For example, Rule 1215 identifies whether segment 925 contains a spot by segmentation or circle search. Rule 03: Rule 1220 to grade a spot as being reactive, non-reactive, having a technical error, or having an undetermined degree of reaction within the spot / assay site 15. • Rule 04: Rule 1225 for detecting bright objects, and Rule 05: Check the results. Rule 1230.

[0100] A useful set of rules 1205–1230 for grading spots / reactions within assay site 15 by the grading rule engine 940 is given above; however, in other examples, only some, or all, of rules 1205–1230 may be applied, or additional or alternative rules may be applied. The operation of the system will be further explained with reference to Figures 16–21.

[0101] Figure 16 shows an example flowchart of a process or rule for determining whether an image is suitable for analysis. The process or rule shown in Figure 16 can be operated to determine whether an array of assay sites 15 in array 10 is suitable for analysis if the background of the image of array 10 is sufficiently dark and uniform. The process uses an overall image metric 1405, which in this example includes the mean pixel intensity of the background portion of the overall image and the standard deviation (or some other appropriate measure of variance) of the pixel intensity of the background portion of the overall image. The rule uses global parameters 1410, which include the maximum mean (or threshold) of the image background and the maximum standard deviation or variance (or threshold). The process includes implementing the rule 1415, which determines whether the mean pixel intensity of the background portion of the overall image exceeds the maximum mean (or threshold) of the image background, or whether the standard deviation (or some other appropriate measure of variance) of the pixel intensity of the background portion of the overall image exceeds the maximum standard deviation or variance (or threshold). If so, a technical error is flagged 1420, and the process for that image is terminated 1425. Otherwise, the image is determined to be suitable for analysis, and the entire process carried out by the grading engine 940 continues to rule 1215 shown in Figure 17.

[0102] Figure 17 is a schematic diagram of process 2205 for identifying whether an object detected within segment 925 is a reacting spot. If the object cannot be identified as a spot with the required accuracy, the process proceeds to process 2305 in Figure 18, which determines whether segment 925 is empty. Process 2205 in Figure 17 determines that an object is a reacting spot if it satisfies certain conditions relating to detectability, shape, size, and the location of the spot within segment 925.

[0103] The process involves receiving parameters such as maximum and minimum blob radii, maximum and minimum isoperimetric coefficients, and maximum and minimum blob edge metrics. These parameters are specific to each assay site / assay being performed.

[0104] The process includes applying rules to determine whether the metrics found in the segment support recognition of objects within a given segment exhibit a suitable response for grading.2220 If so, the graded response can be determined specifically for the given segment.

[0105] The application of Rule 2220 utilizes circle and domain metrics 2210, such as those generated during metric generation as described above in relation to Figure 13. The application of Rule 2220 may include, for example, determining whether a circle has been found within a given segment 925. If a circle has been found within a segment under analysis, it is determined that a responsive spot exists within that segment, and the radius and location of the circle are determined.

[0106] The presence of a reactive spot can be determined using additional or alternative rules. For example, the application of rule 2220 may also include determining, based on the SSR, whether an object has been found in the shape of a non-circular "blob." If so, various metrics of the "blob," such as radius, edge metric, isoperimetric coefficient, and location, are determined. If all blob criteria are met, it is determined that a reactive spot exists within the segment, and the blob criteria include, for example, one or more of the following: the blob radius is greater than or equal to the minimum blob radius and less than or equal to the maximum blob radius; the blob isoperimetric coefficient is greater than or equal to the minimum blob isoperimetric coefficient and less than or equal to the maximum blob isoperimetric coefficient; and the blob edge metric is greater than or equal to the minimum blob edge metric and less than or equal to the maximum blob edge metric. If all blob criteria are met, it is also determined that a spot has been detected.

[0107] If a circle is identified within segment 925, or if all blob criteria are met, process 2420 shown in Figure 19 is applied to check whether the spot identified in process 2205 can be identified as a bright object artifact rather than a reactive spot.

[0108] If no circles are found and none of the blob criteria are met, process 2305 in Figure 18 is applied to determine whether the segment is empty or not.

[0109] If segment 925 is determined not to contain a circle and does not meet the blob criteria, process 2305 determines whether segment 925 contains an object or whether the segment is empty. The process does this by determining 2310 whether segment 925 is blank (i.e., the average pixel value is below a certain level) or whether the segment is uniformly filled with a gray level that does not allow objects to be distinguished (i.e., no transitions or edges are detected) and matches the image gray level of the entire array. Determination 2310 includes comparing a specific metric of segment 925 (such as the SSR value) to one or more thresholds set as parameter 2312 (such as a segment SSR limit parameter). This check may be specific to the printed area or assay within that segment 925.

[0110] For example, if the SSR value of segment 925 is less than the segment SSR limit parameter, the mean pixel value of segment 925 is less than the maximum mean pixel value parameter, the standard deviation (or other variance metric) of segment 925 is less than the maximum standard deviation parameter, and the indication for object detection against background noise less than the measure of background noise is less than the maximum threshold parameter, then segment 925 is determined to be empty, and the process for that segment is terminated. Otherwise, a technical error is determined, i.e., the domain does not contain an object, but it cannot be said with sufficient confidence that it is eligible as a spot where an object would react.

[0111] As mentioned above in relation to the process shown in Figure 17, if an object inside a given segment 925 can be identified as a potentially reactive spot, the process in Figure 17 includes removing bright object artifacts from each segment 925 as step 2420, which is shown in more detail in Figure 19.

[0112] The bright object artifact removal step 2420 includes applying a rule 2505 for determining whether an object in a segment is a bright object anomaly by comparing a pixel intensity metric with one or more thresholds set as parameter 2425 and / or derived from other metrics, as shown in Figure 19. For example, rule 2505 may specify that an object identified in segment 925 is a bright object anomaly if the signal mean exceeds a detection threshold calculated from the IQ3 value, or if the signal standard deviation exceeds a value defined by a specific parameter.

[0113] The threshold is determined using the detection gradient and detection intercept provided as parameters 2425 specific to a given segment 925, and the detection threshold is defined as the value obtained by multiplying the detection gradient of the segment 925 by the IQ3 value of the segment 925 and adding the detection intercept of the segment 925. For example, rule 2505 may specify that an object in a given segment 925 can be determined to be a clear object anomaly if any of the following criteria apply: (1) The average spot signal of the spot (e.g., pixel value) is greater than or equal to the detection threshold. (2) If the standard deviation or other measure of variance of the spot signal is greater than the associated threshold given as a unique parameter 2425, (3) The average of the circular signals is greater than or equal to the detection threshold. (4) The standard deviation or other measure of variance of the circle signal is greater than the associated threshold, which is given as an intrinsic parameter 2425.

[0114] If none of these criteria are met, the process performs process 2025, as shown in Figure 20, which applies rules for classifying the spots, as step 2510. If any of the above criteria are met, a bright object anomaly is detected for that segment 925 and a technical error 2515 is recorded (i.e., the detected object is likely to be an anomaly), and process 2420 is repeated for other segments 925 until all segments 925 in image 10 have been analyzed.

[0115] It should be understood that bright object anomaly detection may use only one or some of the above criteria, and / or different criteria. In an alternative or additional step, the process may remove any pixels with a pixel intensity above a detection threshold, rather than rejecting the entire segment 925 on the grounds of a technical error.

[0116] As mentioned above, with respect to Figure 19, if the process in Figure 19 determines that the object is not a bright object artifact, then the process 2025 shown in Figure 20 is applied, which applies rules for classifying the spot.

[0117] In process 2025 shown in Figure 20, the spot grade / reaction level of each assay site 15 / spot / segment 925 is determined using a metric determined using the process shown in Figure 13, as well as parameters specific to the assay and / or assay site 15. Spot grading / reaction level determination is performed only for spots / segments 925 that have not been rejected by any of the preceding rules applied by the grading engine 940.

[0118] The determination of spot grade / response level based on SSR is provided as a useful example. It should be understood that spot grade / response level may, additionally or alternatively, be based on other metrics such as IQ1, IQ2, IQ3, IQ4, etc., but not limited to these.

[0119] In this example, four thresholds are set, which are assay-dependent, arbitrarily selected, spot-specific, and / or sample-type-dependent parameters. The thresholds are Tel, Tl, Th, and Teh in ascending order of value. The spot grade / reaction level of a given spot / assay site 15 / segment 925 is determined based on the SSR according to Table 1 below.

[0120] [Table 1]

[0121] Process 2025 includes applying rules 3105 to determine the spot grade / degree of reaction of each spot / assay site 15 / segment 925, as shown in Figure 20. According to these rules 3105, spots are graded as reactive, non-reactive, or undetermined based on a metric value that reflects their intensity, such as SSR or delta (where delta is the difference between the average spot or circular pixel intensity value and the average background pixel intensity value).

[0122] In the specific examples shown in Table 1, a technical error is determined if the segment's SSR value falls below the minimum threshold Tel or exceeds the maximum threshold. If the SSR exceeds the minimum threshold Tel but falls below the upper non-reactive threshold Tl, a non-reactive grade is determined. If the SSR exceeds the upper non-reactive threshold Tl but falls below the lower reactive threshold Th, an indeterminate state is determined, indicating that it is not possible to determine the reactive and non-reactive state grades with sufficient accuracy. If the SSR falls between the lower and upper reactive thresholds Th, a reactive grade is determined. While the specific examples given in Table 1 use SSR as a metric, it should be understood that the same concept may also apply to other metrics.

[0123] Beneficially, Rule 3105 may include logic for dynamically switching between metrics used to determine the degree of spot value / response in order to select the most suitable metric, for example, the switching based on the standard deviation of background pixel intensity or another variance metric. In this example, the logic would use SSR as the default and switch to another metric, such as delta, if SSR proves unsuitable. The switching between SSR and delta would be done by using a background CV value, which is, for example, the value obtained by dividing 100 times the background standard deviation by the background mean, based on a measure of the variance of background values.

[0124] For example, in some cases, the background surrounding the spot is non-uniform, resulting in a background standard deviation that is well above the mean. As a result, the spot response rate (SSR) is significantly reduced, which can lead to false non-reactivity judgments even when the spot clearly responds. In this case (invalid SSR), an alternative spot response assessment using delta is used, and background uniformity is assessed using coefficient of variation (CV) instead of standard deviation to take into account the mean level of the background relative to the standard deviation.

[0125] The threshold parameters 3110 for determining the spot grade / degree of reaction are all assay-dependent and, optionally, also spot location and / or sample type-dependent; that is, they vary for different assay sites 15 within the same array 10. The required threshold parameters 3110 include the maximum acceptable background CV value, the reactive SSR value range, the non-reactive SSR value range, the reactive spot delta value range, the non-reactive delta value range, and so on.

[0126] In relation to Figure 17, the process described above (Rule 2) may determine a spot by segmentation or circle detection. The applicable threshold parameter 3110 may also depend on whether the spot is determined as a circle from circle search or as a spot or "blob" via the segmentation process, with different threshold parameters applied to each case.

[0127] The application of Rule 3105 begins by determining whether the background CV value is less than the maximum allowable background CV value. If so, the process proceeds using the SSR metric. Otherwise, the process proceeds using the delta metric, thereby providing the dynamic switching described above.

[0128] When proceeding using the SSR metric, it is determined whether the SSR (for either the blob or the circle, depending on how the spot was determined) is within the reactive SSR value range (which is a threshold parameter), and if so, the spot / assay site 15 / segment 925 is determined to indicate that the associated assay is reactive 3115. If the SSR is not within the reactive SSR value range, it is determined whether the SSR is within the non-reactive SSR value range (which is also a threshold parameter), and if so, the spot / assay site 15 / segment 925 is determined to indicate that it is non-reactive or has not reacted 3120. If the SSR for the spot is determined to be outside both the reactive and non-reactive SSR value ranges, the spot / assay site 15 / segment 925 is determined to be indeterminate or that a technical error has occurred 3125.

[0129] If it is determined that the delta metric should be used, the determination process is the same as above, but instead of the SSR and the threshold parameter 3110 associated with the SSR, a determination process is used that uses the delta and the threshold parameter 3110 associated with the delta (such as the reactive delta value range and the non-reactive delta value range).

[0130] Regardless of the determination made in process 2025 shown in Figure 20, check step 3205 shown in Figure 21 is performed. Check step 3205 includes applying rule 3215 to the determination of the spot grade / degree of reaction determined from spot qualification step 2025 shown in Figure 17. Check step 3205 shown in Figure 21 is an optional step and can be switched on or off by using the appropriate check parameter value 3210. If spot qualification step 2025 in Figure 20 determines that spot / assay site 15 / segment 925 should be graded as "unreactive" 3120, the process in Figure 18 will show the same spot / assay site 15 / segment 925 as "empty" 2315, and then rule 3215 will confirm that spot / assay site 15 / segment 925 is unreactive 3220. However, if Spot Qualification Step 2025 determines that Spot / Assay Site 15 / Segment 925 should be graded as “Non-reactive” 3120, the process in Figure 26 does not show the same Spot / Assay Site 15 / Segment 925 as “Empty” 2315, and then “Technical Error” 3225 is output for Spot / Assay Site 15 / Segment 925 instead of “Non-reactive”. If Spot Qualification Step 2025 determines that Spot / Assay Site 15 / Segment 925 should be graded as “Reactive” 3115, this is maintained by Rule 3215, and then the “Reactive” determination is output 3230.

[0131] Beneficial in this regard, at least the threshold parameters used in the spot qualification process 2025 described above in relation to Figure 20 are individually variable depending on one or more factors such as the assay being performed, the specific location of the assay site 15 on the assay 10, and the type of sample being analyzed. Thus, the effectiveness of assay determination can be improved by selecting a better threshold for any given assay, assay site 15, and / or sample type, regardless of which other (different) assays are being performed on the same array 10. In other words, less concession is needed in setting the threshold parameters to take into account that different assays are being performed within different assay sites 15 on the same array 10.

[0132] Various steps are performed prior to spot qualification, and while these are not mandatory, they can improve the accuracy or efficiency of assay result determination individually or in combination.

[0133] Although specific examples have been described above, this invention is provided to give a possible method for putting the present invention into practice to those skilled in the art, and it will be understood that modifications of the above-described method and apparatus are possible within the scope of the claims.

[0134] For example, various metrics such as SSR, delta, and IQ3 are used, but it should be understood that alternative metrics may be used. Furthermore, various techniques for detecting spots within assay site 15, such as edge detection, thresholding, and shape recognition (e.g., circles), have been described above, but it should be understood that alternative techniques may be used.

[0135] While it is advantageous to use a digital camera 17 to collect images of the assay, it should be understood that other sensor devices useful for determining a metric representing the degree of reaction may be used, such as a thermal camera or an ultraviolet or infrared sensor.

[0136] Embodiments of the method steps of the present invention can be implemented by one or more programmable processors that execute a computer program to perform the functions of the present invention by operating on input data and generating an output. The method steps can also be implemented by dedicated logic circuits such as FPGAs (Field Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits) or other customized circuits. Processors suitable for executing computer programs include CPUs and microprocessors, as well as any one or more processors. Generally, processors receive instructions and data from read-only memory or random access memory or both. Essential elements of a computer are a processor for executing instructions, and one or more memory devices for storing instructions and data. Generally, a computer also includes one or more mass storage devices for storing data, such as magnetic, magneto-optical, or optical disks, or is operablely coupled to receive data from or transfer data to mass storage devices, or both. Information carriers suitable for realizing computer program instructions and data include, for example, solid-state memory such as EPROM, EEPROM, and SSD, and semiconductor memory devices such as flash memory devices, magnetic disks such as internal hard disks or removable disks, magneto-optical disks, and all forms of non-volatile memory, including CD-ROM and DVD-ROM disks. Processors and memory can be complemented by or incorporated into dedicated logic circuits.

[0137] The method steps can be performed by a logical processing device on a remote processing device, or, in a distributed processing configuration, specific steps can be performed on a local processing device and specific method steps on a remote device.

[0138] To enable interaction with the user, the present invention can be implemented on a device having a screen for displaying information to the user, such as a CRT (cathode ray tube), plasma, LED (light-emitting diode), or LCD (liquid crystal display) monitor, and an input device that allows the user to provide input to the computer, such as a keyboard, touchscreen, mouse, or trackball. Other types of devices can be used, and for example, the feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or haptic feedback, and the input from the user may be received in any form, including acoustic, verbal, or tactile input.

[0139] Processing devices suitable for carrying out the methods described above may include mobile, fixed, or network-enabled devices that include or are configured to implement a controller or processing system. The device may be, include, or be a mobile phone, smartphone, PDA, tablet computer, laptop computer, etc. The controller or processing system may be implemented by a suitable program or application (app) that runs on the device. The device may include at least one processor, such as a central processing unit (CPU), numerical coprocessor (MCP), or graphics processing unit (GPU). The at least one processor may be a single-core or multi-core processor. The device may include memory and / or other data storage devices, which may be implemented on DRAM (dynamic random access memory), SSD (solid-state drive), HDD (hard disk drive), or other suitable magnetic, optical, and / or electronic memory devices. The at least one processor and / or memory and / or data storage devices may be locally located, for example, in a single device or in multiple devices communicating in a single location, or they may be distributed across several local and / or remote devices. The device may include a communication module, such as a wireless and / or wired communication module. The communication module may be configured to communicate via cellular communication networks, Wi-Fi, Bluetooth, ZigBee, near-field communication (NFC), IR, satellite communication, or other internet-enabled networks. The communication module may be configured to communicate via Ethernet or other wired networks or connections, via telecommunications networks such as POTS, PSTN, DSL, ADSL, optical carrier lines, and / or ISDN links or networks, via the cloud, and / or via the internet or other suitable data transport networks.The communication module can be configured to communicate via optical communication such as optical wireless communication (OWC), optical free-space communication, or Li-Fi, or via optical fiber, etc. The device and / or controller or at least one processor or processing unit can be configured to communicate with a remote server or data store via the communication module. The controller or processing unit may include, or be implemented using, at least one processor, memory and / or other data storage device and / or communication module of the device.

Claims

1. A computer implementation method for analyzing an assay performed at each assay site in an array or microarray comprising a plurality of assay sites arranged in a plurality of rows and / or columns, wherein each assay site includes a well or spot that forms a reaction site, Receiving at least one image, wherein the at least one image images the plurality of assay sites of the array or microarray collectively or individually; and processing the at least one image to determine at least one metric representing the degree of response at the assay sites. For each of the assay sites, to identify one or more parameters of the assay site, wherein the one or more parameters are predetermined for a given assay type, sample type, and location of the assay site, and the one or more parameters of at least one of the assay sites of the array or microarray are different from the one or more parameters of at least one other assay site. For each of the assay sites, the progress of the reaction in the assay site is determined from at least one metric of the assay site and one or more parameters of the assay site. Includes, At least one of the parameters includes one or more criteria, thresholds, or ranges. Each criterion, threshold, or range indicates a different degree of response. A computer implementation method for determining the progress of the reaction, comprising determining whether the at least one metric of the assay site is above or below the one or more thresholds, or within or outside the one or more ranges, or whether at least one of the criteria is met or not, in order to determine the degree of the reaction.

2. The method according to claim 1, wherein the at least one metric of the region includes or represents the pixel intensity of the region of the image representing a spot in the region, and the spot is formed by a reaction of an analyte and has properties indicating the degree of the reaction.

3. The method according to claim 2, wherein the at least one metric of the area includes a signal-to-standard deviation ratio or delta, which is the difference between the average pixel intensity value of the spot and the average background pixel intensity value.

4. Cropping the image around the array of the aforementioned parts, and / or The aforementioned image is gridded into multiple segments, with each segment surrounding a spot and / or assay site. The method according to any one of claims 1 to 3, comprising at least one of the above.

5. The method according to claim 4, wherein at least one of the determinations of the cutting, the grating, and / or the at least one metric of the assay site uses a common parameter shared with one or more or each or all other spots or sites having the same assay type.

6. The method according to any one of claims 1 to 5, wherein the parameter for a metric for determining the progress of the reaction at the assay site is specific to the site.

7. The method includes detecting or identifying the portion of the image corresponding to the spot within the reaction site, and the detection or identification is To detect or identify shapes in the image that include circles with diameters within a predetermined interval, and / or Edge detection for determining the edges of the spot in the at least one image The method according to claim 2 or any one of claims 3 to 6 dependent on claim 2, comprising one or both of the above.

8. The method according to claim 7, comprising filtering out an identified spot having a pixel intensity measure below a threshold and / or located outside a predetermined geometric region corresponding to the location of the assay site, and positioned based on at least one other identified spot, control spot, or reference point on the array or microarray.

9. The method according to any one of claims 1 to 8, wherein the parameter used to determine the progress of the reaction at the assay site includes at least one of the parameters received from a user input device and / or parameters retrieved from a data store corresponding to one or more identifiers associated with the array or microarray or the assay site or each of the assays being performed within the assay site.

10. The method according to claim 9, wherein the identifier is acquired by an input device including at least one of a user input device for receiving user input, a barcode reader, a QR code reader or other machine-readable code reader, an RFID tag reader, and / or an infrared signal reader.

11. The determination of the progress of the reaction at the assay site comprises performing one or more logic tests on at least one metric of the assay site and one or more parameters of the assay site, wherein the results of the one or more logic tests are Whether or not a reaction occurred at the assay site, The degree of reaction at the assay site, Whether or not activity occurred at the assay site, The degree of activity at the assay site, and / or Whether or not the analyte is present at the assay site The method according to any one of claims 1 to 10, wherein the progress of the reaction at the assay site is the degree of the reaction, comprising at least one of the following instructions.

12. A processing system comprising at least one processing device, a data storage device, and a communication system for receiving an image and outputting an indication of the progress of a reaction at an assay site, wherein the processing device is configured to carry out the method according to any one of claims 1 to 11.

13. An analytical system for analyzing one or more assays performed at each assay site from multiple assay sites of an array or microarray, wherein the assay sites are arranged in multiple rows and / or columns, and each assay site includes a well or spot. The processing system according to claim 12, At least one imaging device configured or configurable to collect images of at least one array or microarray and to communicate the at least one image to the processing system, An output device configured to receive an indication of the progress of the reaction at the assay site from the processing system and to output an indication of the progress of the reaction at the assay site. An analytical system equipped with the following features.

14. A computer program product that, when implemented on a processing system, includes an instruction to cause the processing system to perform the method according to any one of claims 1 to 11.

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