Abstract:
A method for automated microscopic analysis wherein the test protocol is obtained from interrogatable data affixed to each microscope slide. The method further comprises the algorithms that implement the test protocol.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of priority of U.S. Provisional Application No. 60/821,557, filed Aug. 4, 2006, which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND OF THE INVENTION 
     All references cited in this specification, and their references, are incorporated by reference herein where appropriate for teachings of additional or alternative details, features, and/or technical background. Incorporation by reference to non-patent publications is only for nonessential material. 
     FIELD OF THE INVENTION 
     The present invention generally relates to the automated microscopic detection of biological structures using fluorescent tags directed to such biological structures. 
     DESCRIPTION OF THE RELATED ART 
     Conventional optical microscopy generally employs a microscope slide to which a biological sample has been affixed, and a single objective lens that is used to focus on discrete areas of the biological sample in a search for structures of interest, such as cells, nuclei, etc. Dimensions of the image seen through the objective lens depend on the magnification and numerical aperture of the objective lens. The specimen on the microscope slide is manually moved with respect to the objective lens resulting in a plurality of fields of view. Structures of interest seen through the objective in each field of view are analyzed with image details recorded. Images may be stored by means of acquisition by a camera. The multiple field of view are used to characterize the sample as a whole. Of course, such process may be slow for any application that requires a complete view of the specimen. 
     Numerous factors must be dealt with in microscopy, including resolution, contrast, depth of focus, working distance, magnification, parfocality, and parcentricity. Resolution is the ability to distinguish in an image two points as two points. Resolution is important to determine differentiate features in a sample. Resolution may decrease with magnification, and is typically related to the numerical aperture of the objective. Contrast is also necessary in the evaluation of an image. Contrast is the difference between the brightest point in an image and the darkest point in the image, or the relative intensity of the zero order versus the diffracted orders. Without sufficient contrast an image may appear “flat” at best, or invisible at worst. Contrast is conventionally controlled in a manual microscope by way of a condenser diaphragm. Depth of focus refers to the depth of the image in focus. Depth of focus changes as the numerical aperture of the objective changes, and the working distance of the objective changes (as the working distance of the objective is increased the depth of focus increases). The depth of focus is important in that objects within the specimen that are outside the depth of focus are not detected. Working distance refers to the distance from the front of the objective to the specimen plane. When objectives are changed working distance (particularly when the objective has a different numerical aperture) may change as well as focus. It is generally important to keep the working distance sufficient so as not to have the objective interfered by the specimen proper. Parfocality, that is the specimen staying in focus when the objective is changed, and parcentricity, that is, an object in the center of the field staying in the center of the field no matter which objective is being used, are also generally desirable. 
     Many methods are known to aid in the microscopic analysis of samples. For example, without limitation, it is known that certain dyes have an affinity for certain cellular structures. Such dyes may therefore be used to aid in analysis by helping to further elucidate such structures. 
     Fluorescence microscopy of cells and tissues is well known in the art. Treating cells with fluorescent reagents and imaging the cells is well known in the art. Methods have been developed to image fluorescent cells in a microscope and extract information about the spatial distribution and temporal changes occurring in these cells. Some of these methods and their applications are described in an article by Taylor, et al. in American Scientist 80 (1992), p. 322-335. These methods have been designed and optimized for the preparation of a few specimens for high spatial and temporal resolution imaging measurements of distribution, amount and biochemical environment of the fluorescent reporter molecules in the cells. Detection of fluorescent signals may be by way of an epifluorescent microscope which uses emitted fluorescent light to form an image (whereas a conventional reflecting microscope uses scattered illumination light to form an image). The excitation light of a epifluorescence microscope is used to excite a fluorescent tag in the sample causing the fluorescent tag to emit fluorescent light. The advantage of an epifluorescence microscope is that the sample may be prepared such that the fluorescent molecules are preferentially attached to the biological structures of interest thereby allowing identification of such biological structures of interest. 
     One fluorescent dye used in fluorescence microscopy is DAPI or 4′,6-diamidino-2-phenylindole [CAS number: [28718-90-3]; SMILES structure: NC(C2=CC1=C(C═C2) C═C(C3=CC═C(C(N)═N)C═C3)N1)=N], a fluorescent stain that binds strongly to DNA. Since DAPI will pass through an intact cell membrane, it may be used to stain live and fixed cells. DAPI is excited with ultraviolet light. When bound to double-stranded DNA its absorption maximum may be about 358 nm and its emission maximum may be about 461 nm. DAPI will also bind to RNA, though it is not as strongly fluorescent. Its emission shifts to about 400 nm when bound to RNA. DAPI&#39;s blue emission is convenient for microscopists who wish to use multiple fluorescent stains in a single sample. There is very little fluorescence overlap, for example, between DAPI and green-fluorescent molecules like fluorescein and green fluorescent protein (GFP), or red-fluorescent stains like Texas Red. Other fluorescent dyes are used to detect other biological structures. 
     Other types of fluorescing materials are used in fluorescence in situ hybridization (FISH). The FISH method uses fluorescent tags to detect chromosomal structure. Such tags may directed to specific chromosomes and specific chromosome regions. Such technique may be used for identifying chromosomal abnormalities and gene mapping. For example, a FISH probe to chromosome 21 permits one to identify cells with trisomy 21, i.e. cells with an extra chromosome 21, the cause of Down syndrome. FISH kits comprising multicolor DNA probes are commercially available. For example, AneuVysion® Multicolor DNA Probe Kit sold by the Vysis division of Abbott Laboratories, is designed for in vitro diagnostic testing for abnormalities of chromosomes 13, 18, 21, X and Y in amniotic fluid samples via fluorescence in situ hybridization (FISH) in metaphase cells and interphase nuclei. The AneuVysion® Assay (CEP 18, X, Y-alpha satellite, LSI 13 and 21) Multi-color Probe Panel uses CEP BAN probe to detect alpha satellite sequences in the centromere regions of chromosomes 18, X and Y and LSI13/21 probe to detect the 13q14 region and the 21q22.13 to 21q22.2 region. The AneuVysion kit is useful for identifying and enumerating chromosomes 13, 18, 21, X and Y via fluorescence in situ hybridization in metaphase cells and interphase nuclei obtained from amniotic fluid in subjects with presumed high risk pregnancies. The combination of colors emitted by the tags is used to determine whether there is a normal chromosome numbers or trisomy. 
     In a similar vein, the UroVysion® kit by the Vysis division of Abbott Laboratories designed to detect chromosomal abnormalities associated with the development and progression of bladder cancer by detecting aneuploidy for chromosomes 3, 7, 17, and loss of the 9p21 locus via fluorescence in situ hybridization in urine specimens from persons with hematuria suspected of having bladder cancer. The UroVysion Kit consists of a four-color, four-probe mixture of DNA probe sequences homologous to specific regions on chromosomes 3, 7, 9, and 17. The UroVysion probe mixture consists of Chromosome Enumeration Probe (CEP) CEP 3 SpectrumRed, CEP 7 SpectrumGreen, CEP 17 SpectrumAqua and Locus Specific Identifier (LSI 9p21) SpectrumGold. 
     To overcome the laborious process of manual microscopy, a number of researchers, including the present inventors, have proposed automated microscopy systems for capturing and analyzing multiple image views of a biological sample on a microscope slide or other sample retaining device (such as a multiple well plate). Such systems have the potential to greatly improving the efficiency of microscopic analysis and to remove some of the subjective inputs that affect microscopic analysis of a sample. 
     A number of difficulties are associated with automated microscopy. For example, many of the functions performed in manual microscopy are dictated by undefined methodologies under the control of the human eye and brain. Each of these functions needs to be addressed to allow for the slide to be reviewed with the required clarity. Further, much of the analysis undertaken in traditional manual microscopy involves human reasoning based upon a prior experiences. For example, microscopists are often able to discern an artifact or mistreated sample portion from an actual biological structure, yet have difficult expressing the basis for such decision when asked to set forth the same in words. Further automated microscopy entails the automated device having the ability to handle the slide, interpret the biological structure which is to be investigated and the protocol by which interpretation is to be performed, adjust the slide with respect to the objective, search numerous areas on the slide for such biological structure, determine areas on the slide in which structures of interest reside, process desired signals from structure from extraneous signals, interpret signals, etc. 
     The present inventors have recognized these and related needs in implementing automated microscopy of a plurality of samples, such as may be used in high throughput microscopic analysis, and addressed these needs herein. 
     SUMMARY OF INVENTION 
     In embodiments there is included: 
     First, a method of microscopic analysis comprising 
     (a) providing an automated microscope comprising a slide stage, at least one objective lens, image capturing means, programmable means for operating the microscope according to a protocol, and programmable means for providing an analytical outcome; 
     (b) providing a microscope slide containing a sample and interrogatable data thereon, wherein the interrogatable data provide information related to a protocol for analysis of said sample; 
     (c) interrogating the data; 
     (d) positioning the slide on the slide stage; 
     (e) causing the microscope to analyze the sample in accordance with the analytical protocol encoded in the interrogatable data; and 
     (f) causing the microscope to provide an analytical outcome representing the sample. 
     Second, a method for high throughput microscopic analysis comprising 
     (a) providing an automated microscope comprising a slide stage, at least one objective lens, at least one slide cassette containing at least one microscope slide therein, programmable means for operating the microscope according to a protocol, and programmable means for providing an analytical outcome; 
     (b) providing a plurality of microscope slides each containing a sample and interrogatable data thereon, wherein the plurality of slides is contained in one or more of said slide cassettes, wherein the interrogatable data provide information related to a protocol for analysis of said sample; 
     (c) transporting a first cassette into a position suitable for transporting a slide to said microscope stage; 
     (d) transporting a first slide from the first cassette to said microscope stage; 
     (e) interrogating the data found on said first slide; 
     (f) positioning said first slide on the slide stage; 
     (g) causing the microscope to analyze the sample on said first slide in accordance with the analytical protocol encoded in the interrogatable data; 
     (h) causing the microscope to provide an analytical outcome representing the sample on said first slide; 
     (i) if there remains another slide to be analyzed in said first cassette repeating steps (d) to (h); and 
     (j) if there remains another cassette repeating steps (c) to (i). 
     Third, a computer-readable storage medium tangibly embodying a program of instructions executable by a computer for a method of microscopic analysis using an automated microscope comprising a slide stage, at least one objective lens, image capturing means, programmable means for operating the microscope according to a protocol, and programmable means for providing an analytical outcome; 
     wherein the program comprises 
     a) a set of instructions for interrogating data on a microscope slide wherein the interrogatable data provide information related to a protocol for analysis of a sample included on said slide; 
     b) a set of instructions for positioning the slide on the slide stage; 
     c) an analyzing set of instructions for causing the microscope to analyze the sample in accordance with the analytical protocol encoded in the interrogatable data; and 
     d) a set of instructions for causing the microscope to provide an analytical outcome representing the sample. 
     Fourth, a computer-readable storage medium tangibly embodying a program of instructions executable by a computer for a method of high throughput microscopic analysis wherein the method uses an automated microscope comprising a slide stage, at least one objective lens, at least one slide cassette containing at least one microscope slide therein, programmable means for operating the microscope according to a protocol, and programmable means for providing an analytical outcome; 
     wherein the program comprises 
     a) a set of instructions for transporting a first cassette into a position suitable for transporting a slide to said microscope stage; 
     b) a set of instructions for transporting a first slide from the first cassette to said microscope stage; 
     c) a set of instructions for interrogating data on a microscope slide wherein the interrogatable data provide information related to a protocol for analysis of a sample included on said slide; 
     d) a set of instructions for positioning the slide on a slide stage; 
     e) an analyzing set of instructions for causing the microscope to analyze the sample in accordance with the analytical protocol encoded in the interrogatable data; 
     f) a set of instructions for causing the microscope to provide an analytical outcome representing the sample; 
     g) a set of instructions for determining whether there remains another slide to be analyzed in said first cassette and if so repeating the instructions in (b) to (f); and 
     h) a set of instructions for determining whether there remains another cassette and if so repeating instructions in (a) to (g). 
     Fifth, a method comprising obtaining a slide containing electronically interrogtable data recorded therewith and having a biological sample thereon; 
     reading said electronically-interrogatable data from said slide; 
     determining from said electronically-interrogatbale data how said biological sample is to be scanned by an automated microscope; 
     scanning with a automated microscope said slide in the manner dictated by the electronically interrogatable data recorded therewith; and 
     determining from said scans a testoutcome indicative of a state of said biological sample. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  provides a flow chart giving an overview of steps in an embodiment of the invention. 
         FIG. 2  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 3  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 4  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 5  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 6  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 7  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 8  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 9  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 10  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 11  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 12  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 13  provides a flow chart giving details of steps in an embodiment of the invention. 
         FIG. 14  is a schematic representation of a scanned area using a spiral scanning technique, according to an embodiment related to  FIG. 8 . 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Turning to  FIG. 1 , there is disclosed a master diagrammatic flow chart of an embodiment of the present invention.  FIG. 1  presents an overview of the various computational modules that together implement the automatic retrieval and analysis of samples on multiple slides. Such a collection of slides may arise in a research setting or in a diagnostic setting. Large numbers of slides are advantageously examined and analyzed by the automated methods disclosed herein. Biological specimens, cellular or tissue preparations, and similar subjects of investigation constitute nonlimiting examples of subjects for microscopic analysis by methods of the invention. These are generally termed “samples” or “specimens” herein. Commonly the samples include labels to assist in microscopic analysis. Frequently such labels are fluorescent labels. A sample may furthermore include more than one fluorescent labels, wherein each label has particular and distinguishable fluorescent properties, esp. distinguishable excitation and emission wavelengths. In order to conduct suitable microscopic analysis of such samples, appropriate excitation filters are placed in the light beam illuminating the sample, or one of a plurality of laser sources of differing wavelengths is chosen, and corresponding emission filters are placed between the sample and an image capture device such as a camera or charge coupled detector (CCD). In a procedure governing automated microscopic analysis of such samples, a computer or similar controlling device must have available information describing the nature of the probes to be examined. Sample identification including this requisite information, as well as additional sample identifiers, may be encoded on each slide using an interrogatable coding means, such as a barcode or barred array. The interrogatable coding is read as a slide is positioned in the microscope, and the corresponding information is communicated to the computer or controlling device. 
     As seen in  FIG. 1 , the analysis for a particular slide, once loaded in place onto the stage of a microscope ( 15 ), begins by reading a barcode present on the slide ( 20 ). The barcode include information designating the nature of the microscopic analysis to be carried out. The details for the diverse analytical protocols are stored in a database for reference by the computer or controlling device. Once the slide barcode is read, the correct experimental protocol is identified in a database (DB) according to the information encoded in the barcode ( 25 ). With this information now available to control the operation of the microscope, a concatenated series of operations that regulate the focusing, optimize the region on the slide to be scanned to provide a suitable image, including adjustments for low magnification to start with, and moving to a higher magnification for the actual analysis, is carried out (see steps  30 ,  35 ,  40 ,  45 , and  50 ). A successful implementation of the various modules involved in this protocol provides results, designated a “Testoutcome” in  FIG. 1  ( 55 ). The remaining loops illustrated in  FIG. 1  relate to determining whether, in a given cassette, the last slide in the cassette has been examined ( 65  and  85 ); and whether slides in the last cassette have been analyzed ( 70  and  80 ). When the last cassette has been examined, the operation of the microscope ceases ( 75 ). 
     As indicated at  FIG. 1 , the databridge application is started (step  5 ) to run as a system service for file handling in parallel with other process that may be running. Such service may be a method such as shown at  FIG. 13 , wherein the service is started (step  300 ) which might include setting parameters and the environment in which the application will run. In the method of  FIG. 13 , a configuration file is read (step  310 ) such as may be provided by IKoDataBridge.exe.config (step  305 ). If preconditions are not met an error is recorded in a file, such as an application event log (step  320 ) and the process shut down (step  325 ). If preconditions are meet (step  315 ) such as the existence of source folders, a loop is performed (step  335 ) until a shut down is requested. Starting the loop a log file is queried for a list of files (step  340 ), for example “.txt” files. If files are found (step  345 ) another loop is started (step  350 ) wherein a further check is performed for a corresponding file, such as a “.nvc” type file. Existence of the corresponding file would then lead to a read of success counts within such a “.nvc” file and cause a skip of entries in the original file (step  360 ). After reading of the entry from the original file, for example the “.txt” file (step  365 ) a query is performed as to whether the complete marker is found (step  370 ), whereupon the text file would be removed (step  375 ). Interrogation of more files is made (step  380 ), resulting in a return and continuation of the loop initiated for each file found, such as a “.txt” file (step  350 ). If more files are not found (step  380 ) the system, as illustrated by the alternative path (step  385 ,  385 ′), is put to sleep based on the time specified, for example in a configuration file such as “.config” (step  330 ). Completion of the sleep period (step  330 ) results in return and continuation of the shutdown loop starting (step  335 ). Failure of finding the complete marker in step  370  will trigger a specific command in step  425  to execute. If the execution is successful (step  405 ) the reading of an entry from, for example, a “.txt file” is resumed as seen in step  365 . Non success at step  405  in executing the command of step  425  records an entry into a log file, such as an application event log (step  410 ), query of the error type and count (step  415 ) and possible increment of a retry count at step  430 , returning to the execution step of  425 . A sufficient error or retry count of commands, as tested at step  415  may result in a notification to a scanner application as in step  420  and return to step  350  for continue to loop for another file, such as “.txt” file. In the event a corresponding file, such as a “.nvc” file does not exist (step  355 ), a file will be created containing a zero (step  400 ), where after the process will occur as performed above continuing from step  365 . The absence of found files at step  345  would cause a retrieval of a file list from a folder, for example a databaselog folder (step  390 ), and query of the list in step  395  for files. If no files are found the service would be placed in sleep mode as shown in step  330 , or if files were found the process would return to the file loop at step  350 . 
     Turning back to  FIG. 1 , slides having bar coded or other electronically-readable indicia are loaded into a cassette (step  10 ) having multiple slots from which such slides may be obtained. A slide for analysis is then loaded (step  15 ) into an automated microscope. The barcode or other electronically-readable indicia is read (step  20 ) to determine the type of processing demanded (e.g., type of application demanded) on the slide by reference to a database (step  25 ). The automated microscope then seeks to execute a number of steps to detect objects of interest in the sample based on the processing demand. 
     First the sample is focused with respect to the objective. Focusing may be transacted by using a known reference point, such as the slide edge (step  30 ) from which focus may be effectuated. Such focusing may be a method such as shown at  FIG. 7  wherein depth of focus in the z range is redefined if certain parameters raise a flag of out-of-focus situation (step  11 ) or not (step  19  termination). In the method described at  FIG. 7 , the slide is exposed to an interrogation for a period of time, for example 100 msec (step  12 ), with the binning mode being set to cover a substantial area, for example set to 4×4 (step  13 ). The interrogation spot is then set to a reference point on the slide edge, such as the top middle slide edge (step  14 ). Autofocus is then performed to determine a Zbase (step  16 ), that is, a base point along the Z axis, such as at the top surface of the slide edge. From the Zbase a z-focus upper limit is defined (step  17 ), such as 25 times the depth of focus from the Zbase, and a z-focus lower limit is defined (step  18 ) 
     Returning to  FIG. 1 , after focusing, the scan area is determined (step  35 ) based upon a predetermined algorithm. For example,  FIG. 2  shows two different schemes for scan area definition based upon two different FISH-based tests, AneuVysion ( 22 ) and UroVysion ( 23 ) based on bar coded or other electronically-readable indicia on the slides (step  21 ). Such tests differ in the manner of applying the sample, with the AneuVysion sample being placed in smear on the slide, and the sample applied to a UroVysion Slide a dropped blob. 
     As illustrated at  FIG. 2 , if an AneuVysion test ( 22 ) is indicated, the scanned area is defined at step  24  as being the entire scannable area on the slide to determine the position of a smear on the slide. As illustrated, low magnification field visits (“survey visits”) are made for rapid detection of possible candidates according to a sequence along the vertical axis of the slide (step  26 ), for example, in a pattern as set forth at  27 . Query of isolated possible candidates may then be performed by high magnification (“investigation mode”). 
     As further shown in  FIG. 2 , with respect to UroVysion slide  28  investigation of possible candidate may employ numerous steps. At step  29 , a filter is set to selectively determine fluorescent signals from a label such as DAPI interacting with the sample. Exposure value is set to a predefined value at step  31 , and the binning mode (merging of distinct pixels) of the camera set to a predefined level, such as 4.times.4 (step  32 ), to allow for expeditious scanning of the slide. The Z-motor is then positioned to allow for fixed z-position reading of locations on the slide, for example, set to the middle of the entire z-movement range (step  33 ). Read is made of pre-recorded positions on the Urovyision Slide  28 , for example, as illustrated  2 ,  8 ,  11 , and  5  of the registry (step  34 ). Interrogation is made of pre-programmed location field on slide  28 , such location field for example, encompassing positions  1 ,  2  and  3  ( 36 ), with imaging being made of the DAPI signals at such pre-programmed filed and a mean pixel value at each position being determined at step  36 ). At step  37  the position with the largest mean pixel value (upper bound) is selected for each pre-programmed location field, as reiterated at steps  38 / 39 ,  41 / 42  and  43 / 44 . Using the positions identified as having the largest mean pixel value, an enclosed boundary is defined (step  46 ). Within such defined enclosed boundary there is then assigned a low magnification yield visit sequence starting form the center of the defined boundary (for example, circle) with the sequence number increasing as one spirals out (step  47 ). 
     Turning back to  FIG. 1 , a low magnification scan is then performed at step  40 . Such low magnification scan may entail discrete steps as set forth at  FIG. 3 . At step  49  magnification is set to a low value, for example, to an objective lens having 10× magnification. Quality control measures, such as Objective repeatability, or other forms of quality checks may then be determined at step  51 , using methodology, for example, as set forth at  FIG. 5 . 
     Objective repeatability may be determined using the embodiment methodology as shown at  FIG. 5 . First, binning mode is set for each magnification level (for example, 10× or 100× as set forth at  139 ) which will be used to scan the scan area. For example, binning mode may be set to 2×2 ( 141 ) or alternatively 4×4 ( 142 ) as shown in  FIG. 5 . With the objective set to the appropriate magnification, e.g.,  10 × as set forth at  143 , the interrogation is sent to a predefined position that has been determined to include some features of potential interest  144 . Autofocus and autoexposure are performed (step  146 ) with one image grabbed and at least one feature is identified as, for example, by determining a gradient, such as an optical gradient (step  147 ). If a feature is not determined at step  148  the low magnetic field is lowered more and autofocus and autoexposure of step  146  is repeated. If a feature is determined at step  148  the magnification is verified at step  149 , features of interest are centered applying a pre-defined parfocality offset (step  152 ) and the objective magnification changed, as for example, to 100× as at step  153 . Again, autofocus and autoexposure are performed (step  154 ) and a gradient used to find the feature of interest (step  155 ). A template may then be generated around the feature isolated for correlation matching (step  157 ). The objective is then changed once more to the original objective and position, the image is grabbed and the offset determined from the previous image based on correlation (step  159 ). If the offset is acceptable (step  161 ) and offset is acceptable multiple consecutive times, such as, three times (step  162 ) the objective repeatability test is terminated (step  164 ). If acceptability does not reach offset acceptability in a consecutive predetermined maximum number of attempts (step  163 ) then there is change of the objective back to the original position (step  158 ). If a feature is not found at  148 , then there may be a move down of one low magnification field ( 151 ) and the path continued at step  146 . 
     Turning back to  FIG. 3 , after objective repeatability is confirmed at step  51 , an image processing thread is created (step  52 ). As a simultaneous process, the image processing thread is first initialized (step  73 ), and images saved (step  76 ) after waiting for image processing jobs in the queue (step  74 ). The images are then processed and in accord with an algorithm candidate nuclei are selected and x-y positions of each candidate nuclei target are determined (step  77 ). From the x-y positions determined, the interrogation strategy is set based on the high magnification to be used, so as to maximize the number of nuclei per field and minimize the total number of high magnification fields necessary to visit such nuclei candidates (step  78 ). A determination is made upon receipt of images whether the thread should be terminated (step  79 ), if not image processing continues (step  74 ), and if termination is determined (step  81 ), then based on the test screening protocol, for example, as illustrated, AneuVysion or UroVysion (step  83 ), the fields are sorted in a manner to provide required information. For example, with respect to an AneuVysion test (step  82 ), the list of high magnification fields may be sorted based on a number of nuclei in the filed (step  86 ), and with respect to a UroVysion test (step  84 ), the list of high magnification fields may be sorted on largest nucleus size in the field (step  87 ), followed by termination (step  88 ). 
     Now turning to step  53  of  FIG. 3 , after creating the image processing thread (step  52 ) as discussed above, the system is set for acquiring images. First parameters necessary for imaging are checked, for example, disk space and activating source (e.g., lamp). The sample is then visited with a low magnification field search in the pre-determined visit sequence order (step  54 ). In conjunction, filters may be effectuated, for example a DAPI filter for determining nuclear tags, and the binning mode adjusted for appropriate resolution (step  56 ). The low magnification objective lens is then adjusted for focus (step  57 ), for example, by a methodology such as described at  FIG. 10 . 
     In  FIG. 10 , there is shown a method for adjusting low magnification focus. First there is a determination of whether the low magnification field is the first low magnification field in the sequence order (step  232 ). If the low magnification field is the first low magnification field in the sequence order at step  236  the z-range at the low magnification field is recalculated by interpolation using database(s) incorporating z-focus range found from the “find focus on slide edge” ( 233 ) and z-difference from the top edge to bottom edge ( 234 ) if possible if not (step  237 ) there is termination (step  186 ). If the low magnification field is not the first low magnification field in the sequence order, then the neighborhood of potential structures of interest is set to a defined number (step  239 ) and each neighborhood is inquired in low magnification (step  241 ) to determine if there is one or more neighborhoods with a valid z focus value (step  244 ), and if so, the average of all the z focus values is taken (step  247 ), and if not, the number or size of neighborhoods are expanded (step  243 ) until there are no more neighbors to expand ( 243 ), and a flag ( 237 ) is sent to complete ( 186 ) the string. 
     Returning back to  FIG. 3 , at step  58  autofocus and autoexposure are performed The binning mode may then be changed (step  59 ), for example, to 1×1 as illustrated, an image, for example a DAPI image (step  71 ), acquired. Depending on the test used to elucidate objects of interest, such as, for example, an Aneuvyision test ( 72 ), one may need to alter other microscopic parameters to elucidate such objects. For example, there may be need to alter filtering (step  61 ) of emanating signals from the sample, and change the exposure value of the sample (step  62 ). Once an image is acquired (step  63 ) it may be processed using the processing thread discussed supra (step  64 ) and once all candidates are located (step  66 ), and each of the fields interrogated (step  67 ), the imaging process thread is terminated (step  81 ). 
     Depending upon the test protocol used (e.g., AneuVysion or UroVysion  82 ,  83 ,  84 ), the processed images are handled in a predetermined manner, for example, with respect to an AneuVysion test by sorting the list of high magnification fields based on the number of nuclei in a field (step  86 ) and with respect to a UroVysion test, sorting the list of high magnification fields on the basis of the largest nucleus size in the field (step  87 ). If all candidates are not located (step  66 ), and each of the fields is not interrogated (step  67 ), and the scan area may be redefined (steps  68 ,  69 ). 
     Redefinition of the scanner area may be by the methodology of  FIG. 8  wherein a central point is selected from which spiral scanning techniques such as in the order set forth in  FIG. 14  are performed. Such spiral scanning may be defined by the equation of step  181 . In such methodology, at step  179 , obtain the number of nuclei, Ny, in each field scanned along the vertical central line. At step  182 , calculate the y-coordinate of the center, Cy, using weighted average. Subsequently at step  183 , calculate the x-coordinate, Cx, where the vertical central axis of the slide lies. Then at step  184 , define the scanning area centered around (Cx, Cy) with its diameter about the width of the slide. Finally at step  185 , before termination (step  187 ), assign scanning sequence number for each low mag field inside the circle. Sequence number starts from the center of the area and increases as it spirals out. It should skip the area which was scanned already. 
     Once the low magnification scan area is defined (step  35  of  FIG. 1 ) and the sample is scanned at low magnification (step  40  of  FIG. 1 ), a scan at high magnification may be performed (step  45  of  FIG. 1 ). 
     High magnification scanning may employ a methodology such as portrayed at  FIG. 4 . The objective is set to high magnification, and camera gain set to highest gain (step  89 ). The imaging processing thread for high magnification is then created (step  91 ) by first initialization (step  129 ), waiting for image processing jobs in the queue (step  131 ), saving the image (step  132 ), processing image stacks (step  133 ) (such as DAPI and FISH images), updating the high magnification field probability map (step  134 ), classifying the targets of interest (step  136 ), e.g., nuclei, and finally ending the thread if appropriate (steps  137 / 124 ) and continuing at  126 . The updating of the high magnification field probability map of step  134  may be by a method as set forth in the flow chart set forth at  FIG. 12 . 
     As shown, at step  300 , there is provided input as to the probability that a object (such as a DAPI object) has other objects of interest associated (such as FISH objects) and input pertaining to the number of objects for each high magnification field. Next there is calculation of the expected value of the number of signals of interest having other objects of interest associated therewith (step  305 ) such as DAPI objects having Fish Signals, in each high magnification field. The high magnification fields are then sorted (step  310 ) according to the number of useful objects, such as DAPI objects. (step  310 ), the high magnification fields with the largest number of useful objects, such as DAPI objects, are scanned and the probability of useful objects, such as DAPI objects, for the low magnification fields are adjusted (step  315 ). The expected valve of the number of objects having a desired signal (e.g. DAPI objects having FISH signals) in each of the high magnification fields are calculated at step  320 . 
     For example, the high magnification field probability map with respect to DAPI objects having FISH signals may be determined. DAPI objects for high magnification scanning may be sorted based on the number of objects contained in the high magnification field in order to reduce the number of fields to be scanned to find enough useful DAPI objects within the least amount of time. DAPI objects having good FISH signals (i.e. objects containing the most number of useful DAPI objects) may be further sorted to reduce the time necessary of high magnification analysis. Assuming the probability for a high magnification field being properly processed to have FISH objects to be p=m/n, every time a DAPI object is found to contain FISH objects, the probability can be addressed to be p=(m+1)/(n+1). Every time a DAPI object is found to contain FISH objects, adjust the probability to be p=m/(n+1). The expected value of the number of useful objects in each high magnification field is then the multiplication of the number of DAPI objects and the probability. The high magnification field with the largest expected value of the number of objects may be chosen to be scanned. Note that, the value of p can be obtained statistically by experiments on typical slides. With a fixed p, the value of m (or n) needs to be carefully chosen so that each object, no matter it has FISH signals or not, can have a proper impact factor on the probability adjustment. 
     The pseudo code of an algorithm for a DAPI/FISH system that may be used is set forth below:
         1. Let the initial lowmag field quality indicator be p i =m i /n i =p=m/n.   2. Calculate the expected value of the number of objects in each himag field and sort them.   3. Choose the himag field with the largest expected number of objects.   4. If the expected number of objects is less than N min , stop.   5. Scan and analyze the himag field chosen.   6. For each object in the himag field, decide if it contains FISH signals. Let n i =n i +1. If the object contains FISH signals, then m i =m i +1.   7. If enough useful DAPI objects have been found, stop.   8. Calculate the new field quality indicator p i =m i /n i .   9. Update the expected value of the number of objects based on the field quality indicator in the remaining himag fields within the current lowmag field.   10. Sort the remaining himag fields and go to 3.       

     By choosing appropriate values from in and n, one can achieve a large variety of scanning strategies. For high magnification scanning application, it may be desired that the algorithm be able to abandon the field where there are objects without FISH signals. To do so, one may choose small values for m and n (for example, m=1, n=2; or if one wants to abandon fields faster, m=0.5, n=1). The N min  may be chosen, for example, to =0.2˜0.3. 
     In respect of the classification of nuclei at step  136 , classification may be directed by the particular testing protocol being employed, such as, for example, AneuVysion/UroVysion ( 209 ,  211 ,  212 ) of  FIG. 11 . For example, when nuclei on a AneuVysion test slide are being counted, a simple determination of whether the dot count in any of the FISH channels does not contain a countable flag (step  213 ) may be used to determine whether the proposed nuclei dot should be counted ( 216 ) or not counted ( 214 ). Similarly, when nuclei on an UroVysion test slide are being counted, channel count may be used in respect to classification of the nuclei. For example, if two or more channels in a plurality of channels, for example three channels, have more than two dots ( 217 ), then an abnormal classification ( 223 ) may be given, or the first three channels have two dots and the last (e.g. gold) channels has zero dots ( 219 ), a classification of abnormal ( 226 ) may be given, while if the first three channels have two dots and the last (e.g., gold) channel has two dots ( 221 ), then a classification of normal ( 227 ) may ensue. If only one channel in the first of the plurality of channels has more than two dots ( 218 ) then the classification may be singlegain ( 224 ), while if at least two channels in the first three channels has more than one dot and zero dot in gold ( 222 ), then a classification of zerogold ( 228 ) or unclassified ( 229 ) may be rendered. Upon classification of each nuclei the classification process may be terminated ( 231 ). 
     A scan at high magnification (step  45  of  FIG. 1 ) employing the methodology as set forth at  FIG. 4 , after creation of the image processing thread (step  91 ) may transact an object repeatability test ( 92 ), for example, as discussed with respect to  FIG. 5  supra. Again parameters of the microscope such as disk space and lamp (step  93 ) may be performed and the stop condition checked ( 94 ). 
     Stop condition checking ( 94 ) may depend on the particular testing protocol being employed, for example, AneuVysion or UroVysion ( 166 ,  167 ,  168 ; see  FIG. 6 ). 
     If AneuVysion ( 167 ), for example, a determination may be made if the total scanning area has been scanned ( 169 ) and if it is so having the stop condition being set ( 173 ) and the process terminated ( 174 ). On the other hand, if a determination is made that the total scanning area has not been made ( 169 ), then the total nuclei collected at high magnification may be compared to a threshold, such as equal to or greater than 500 ( 171 ). If this threshold has been met, the stop condition may be determined to be met ( 173 ). If the threshold has not been found to be met, and the highest nuclei number in all the cell categories is determined to be above a predetermined minimum threshold (such as equal to or greater than 50) ( 172 ), the stop condition may also be determined to have been met ( 173 ). If it is below the predetermined minimum threshold, the stop condition may be determined not to have been met ( 176 ). 
     If UroVysion is the particular protocol employed ( 168 ), a determination may be made if the total scanning area has been scanned ( 177 ), and if so the stop condition being met, and if not another parameter being sued to meet the stop condition ( 173 ). For example, one might make as a condition of a stop condition being met ( 173 ) that the total nuclei collected at high magnification be equal to or greater than the value the user specified ( 178 ) (if not the stop condition is not met  176 ). 
     Turning back to  FIG. 4 , the type of test performed on the sample (for example, AneuVysion (step  96 )) may influence the step of high magnification scanning (step  45  of  FIG. 1 ). For example, if AneuVysion is the test (step  96 ) one might choose the high magnification field with the next highest expected number of nuclei (step  138 ) for scanning, while if such test was not employed, the next high magnification field in the list (step  97 ) might be scanned. It may be necessary in the process to periodically adjust parameters of the microscope, for example, resenting the lamp timer at every 50th high magnification field (step  98 ). Before taking an image it is advantageous to confirm that the image processing queue is available (step  99 ). Appropriate filters (step  102 ) may need to be set, the shutter set to on (step  103 ) and the high magnification field entered (step  101 ). The exposure time to an appropriate interrogation wavelength may then be estimated with a setting of a binning mode (step  104 ). After adjusting autoexposure and autofocus (step  106 ), an image, such as a DAPI image, may be taken at the focus position and the exposure values found (step  107 ). Parcentricity should be confirmed by determining parcentricity offset (step  108 ) and if the offset is too much (step  109 ) the objective turned between low and high magnification (step  127 ), the check process repeated, or if there is a determination that the last high magnification field has been reached (step  123 ) the image processing thread terminated (step  124 ). If the offset is not too much, then other mask may be employed, such as a DAPI mask and the parcentricity offset updated (step  111 ). After requiring a stack of images, for example nine slices, the best focused plane may be determined (step  112 ), further filters set (step  113 ), such as a filter for detecting FISH signals, and exposure time recalculated and binning mode set (step  114 ). Autoexposure on the best focused plane may be effected (step  116 ) followed by resetting of the binning mode to a new value and applying exposure (step  117 ) to obtain a stack of images of the signals to which the filter has been set (step  118 ), for example FISH signals, until the desired number of filters to produce the stack has been completed (step  119 ). The shutter of the image obtaining device may then be set to off (step  121 ), the images obtained sent to the image processing thread (step  122 ) with the image processing thread being terminated (step  124 ) after determining the last high magnification field has been queried (step  123 ). Finishing of the high magnification scan (step  126 ) upon a stop condition check (step  50  of  FIG. 1 ) —such as described above with respect to  FIG. 6 , may prompt the automated microscope to generate a testoutcome (step  55  of  FIG. 1 ). 
     A exemplary automated method for determining a testoutcome (step  55  of  FIG. 1 ) with respect to a Aneuvyision or UroVysion test ( 188 ,  189 ,  191 ) is set forth at  FIG. 9 . As depicted with respect to a AneuVysion test ( 189 ) each fluorescent taggant (CEP v. LSI) ( 192 ) is analyzed with respect to binding with the target chromosomal regions for such taggants. For example, with respect to CEP ( 193 ) the X, Y and 18 dotcounts are determined (step  196 ), and with respect to LSI ( 194 ) the dotcounts with respect to chromosomes 13 and 21 are obtained (step  197 ). The dotcounts determined are then matched (step  198 ) against a database of possible outcomes for CEP labeling ( 201 ) or LSI labeling ( 202 ). If the dotcount obtained matches a possible dotcount outcome for valid CEP labeling ( 201 ) then the output matched is sent as the testoutcome. However if the dotcount obtained does not match with a possible dotcount outcome for valid CEP labeling ( 201 ), then there is a determination if the reason for the failure of the match is due to the analysis of too few nuclei (step  199 ), and if yes the testoutcome output is sent as “less than 50 nuclei images” ( 206 ), and if no the testoutcome is output as “review recommended” ( 204 ). Testoutcome is terminated at  208 . 
     Turning back to  FIG. 1 , after generation of a testoutcome (step  55 ), the slide having been interrogated is unloaded (step  60 ) and a new slide from the cassette is loaded (step  85 ) if the slide is not the last slide in the cassette (steps  65 ,  70 ). If it is the last slide in the cassette (step  70 ) then the next cassette may be loaded if such is available (step  80 ), or if not the run may be terminated (step  75 ). 
     STATEMENT REGARDING PREFERRED EMBODIMENTS 
     While the invention has been described with respect to preferred embodiments, those skilled in the art will readily appreciate that various changes and/or modifications can be made to the invention without departing from the spirit or scope of the invention as defined by the appended claims. All documents cited herein are incorporated by reference herein where appropriate for teachings of additional or alternative details, features and/or technical background.