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1.  Rapid automated cell quantification on HIV microfluidic devices† 
Lab on a chip  2009;9(23):10.1039/b911882a.
Lab-chip device analysis often requires high throughput quantification of fluorescent cell images, obtained under different conditions of fluorescent intensity, illumination, focal depth, and optical magnification. Many laboratories still use manual counting - a tedious, expensive process prone to inter-observer variability. The manual counting process can be automated for fast and precise data gathering and reduced manual bias. We present a method to segment and count cells in microfluidic chips that are labeled with a single stain, or multiple stains, using image analysis techniques in Matlab and discuss its advantages over manual counting. Microfluidic based cell capturing devices for HIV monitoring were used to validate our method. Captured CD4+ CD3+ T lymphocytes were stained with DAPI, AF488-anti CD4, and AF647-anti CD3 for cell identification. Altogether 4788 (76 × 3 × 21) gray color images were obtained from devices using discarded 10 HIV infected patient whole blood samples (21 devices). We observed that the automatic method performs similarly to manual counting for a small number of cells. However, automated counting is more accurate and more than 100 times faster than manual counting for multiple-color stained cells, especially when large numbers of cells need to be quantified (>500 cells). The algorithm is fully automatic for subsequent microscope images that cover the full device area. It accounts for problems that generally occur in fluorescent lab-chip cell images such as: uneven background, overlapping cell images and cell detection with multiple stains. This method can be used in laboratories to save time and effort, and to increase cell counting accuracy of lab-chip devices for various applications, such as circulating tumor cell detection, cell detection in biosensors, and HIV monitoring devices, i.e. CD4 counts.
PMCID: PMC3839566  PMID: 19904402
2.  A semi-automated technique for labeling and counting of apoptosing retinal cells 
BMC Bioinformatics  2014;15:169.
Retinal ganglion cell (RGC) loss is one of the earliest and most important cellular changes in glaucoma. The DARC (Detection of Apoptosing Retinal Cells) technology enables in vivo real-time non-invasive imaging of single apoptosing retinal cells in animal models of glaucoma and Alzheimer’s disease. To date, apoptosing RGCs imaged using DARC have been counted manually. This is time-consuming, labour-intensive, vulnerable to bias, and has considerable inter- and intra-operator variability.
A semi-automated algorithm was developed which enabled automated identification of apoptosing RGCs labeled with fluorescent Annexin-5 on DARC images. Automated analysis included a pre-processing stage involving local-luminance and local-contrast “gain control”, a “blob analysis” step to differentiate between cells, vessels and noise, and a method to exclude non-cell structures using specific combined ‘size’ and ‘aspect’ ratio criteria. Apoptosing retinal cells were counted by 3 masked operators, generating ‘Gold-standard’ mean manual cell counts, and were also counted using the newly developed automated algorithm. Comparison between automated cell counts and the mean manual cell counts on 66 DARC images showed significant correlation between the two methods (Pearson’s correlation coefficient 0.978 (p < 0.001), R Squared = 0.956. The Intraclass correlation coefficient was 0.986 (95% CI 0.977-0.991, p < 0.001), and Cronbach’s alpha measure of consistency = 0.986, confirming excellent correlation and consistency. No significant difference (p = 0.922, 95% CI: −5.53 to 6.10) was detected between the cell counts of the two methods.
The novel automated algorithm enabled accurate quantification of apoptosing RGCs that is highly comparable to manual counting, and appears to minimise operator-bias, whilst being both fast and reproducible. This may prove to be a valuable method of quantifying apoptosing retinal cells, with particular relevance to translation in the clinic, where a Phase I clinical trial of DARC in glaucoma patients is due to start shortly.
PMCID: PMC4063694  PMID: 24902592
Automated analysis; Cell counting; Cell quantification; Blob analysis; Image processing; Image analysis; Retinal ganglion cells; Retinal cell apoptosis; DARC; Glaucoma
3.  Comparison of manual and automated cell counts in EDTA preserved synovial fluids. Storage has little influence on the results 
Annals of the Rheumatic Diseases  1997;56(10):622-626.
OBJECTIVE—To determine the precision and agreement of synovial fluid (SF) cell counts done manually and with automated counters, and to determine the degree of variability of the counts in SF samples, kept in the tubes used for routine white blood cell (WBC) counts—which use liquid EDTA as anticoagulant—at 24 and 48 hours at 4°C, and at room temperature.
METHODS—To determine precision, cell counts were repeated 10 times—both manually and by an automated counter—in a SF sample of low, medium, and high cellularity. The variances were calculated to determine the interobserver variation in two manual (M1,M2) and two automated cell counts (C1,C2). The agreement between a manual (M1) and automated counter (C1) results, was analysed by the Bland and Altman method and the difference against the mean of the two methods was plotted. Then, the mean difference between the two methods was estimated and the standard deviation of the difference. To determine the effects of storage, SF samples were kept in a refrigerator at 4°C, and at room temperature; cell counts were done manually (M1) and automatically (C1) at 24 and 48 hours and the changes analysed by the Bland and Altman method. The variances were compared using an F test.
RESULTS—(1) Precision. With the manual technique, the coefficients of variation were 27.9%, 14%, and 10.7% when used for counting the SF with low (270), medium (6200), and high cellularities (25 000). With the automated technique the coefficients of variation were 20%, 3.4%, and 2.9% in the same SF samples. In the fluids of medium and high cellularity, the variances of the automated cell counts were significatively lower (F test, p<0.002) than those of the manual counts. (2) Interobserver variation. The variance between C1 and C2 (25 SF) was significatively lower (F test, p<0.002) than that of the manual counts (41 SF). (3) Agreement between the two techniques (100 SF). For cellularities above 2000 cells/mm3, the manual method gave results between +10% to −34% of the results obtained by the coulter. For cellularities below 2000 cells/mm3, manual cell counts were between +60 to −1280 cells/mm3 of those obtained by the automated counter. (4) Influence of storage. The coulter counts of SF samples preserved at 4°C showed less variance (F test, p<0.05) than the manual counts. The worst results were obtained in manual counts of SF samples kept at room temperature; these samples at 48 hours showed a variation between −47% to 42% of the initial results.
CONCLUSIONS—Automated cell count of the SF offers advantages: it gives higher precision and consumes less time. The stability of the samples preserved in the EDTA tubes used for routine WBC counts is of additional interest, because if delay cannot be avoided, the results of the WBC counts are still accurate at 24 and even at 48 hours, at least for clinical purposes.

PMCID: PMC1752268  PMID: 9389224
4.  Affordable CD4+-T-Cell Counting by Flow Cytometry: CD45 Gating for Volumetric Analysis 
The flow cytometers that are currently supported by industry provide accurate CD4+-T-cell counts for monitoring human immunodeficiency virus disease but remain unaffordable for routine service work under resource-poor conditions. We therefore combined volumetric flow cytometry (measuring absolute lymphocyte counts in unit volumes of blood) and simpler protocols with generic monoclonal antibodies (MAbs) to increase cost efficiency. Volumetric absolute counts were generated using CD45/CD4 and CD45/CD8 MAb combinations in two parallel tubes. The percentage values for the various subsets were also determined within the leukocyte and lymphocyte populations utilizing a fully automated protocol. The levels of agreement between the newly developed method and the present industry standards, including both volumetric and bead-based systems using a full MAb panel for subset analysis, were tested by Bland-Altman analyses. The limits of agreement for CD4 counts generated by the volumetric methods using either CD45/CD4 (in a single tube) or the full Trio MAb panel (in three tubes) on the CytoronAbsolute flow cytometer were between −29 and +46 cells/mm3 with very little bias for CD4 counts (in favor of the Trio method: +8 CD4+ lymphocytes/mm3; 0.38% of lymphocytes). The limits of agreement for absolute CD4 counts yielded by the volumetric CD45/CD4 method and the bead-based method were between −118 and +98 cells/mm3, again with a negligible bias (−10 CD4+ lymphocytes/mm3). In the volumetric method using CD45/CD8, the strongly CD8+ cells were gated and the levels of agreement with the full Trio showed a minor bias (in favor of the Trio; +40 CD8+ cells/mm3; 5.2% of lymphocytes) without a significant influence on CD4/CD8 ratios. One trained flow cytometrist was able to process 300 to 400 stained tubes per day. This workload extrapolates to a throughput of >30,000 samples per year if both CD45/CD4 and CD45/CD8 stainings are performed for each patient or a throughput of >60,000 samples if only CD45/CD4 counts are tested in a single tube. Thus, on the basis of the high efficiency and excellent agreement with the present industry standards, volumetric flow cytometers with automated gating protocols and autobiosamplers, complemented by generic CD45, CD4, and CD8 MAbs used in two-color immunofluorescence, represent the most suitable arrangements for large regional laboratories in resource-poor settings.
PMCID: PMC120051  PMID: 12204964
5.  CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation 
The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.
PMCID: PMC3850890  PMID: 23938087
Automated analysis; Cell segmentation; CellSegm; High-throughput; Nucleus staining; Surface staining
6.  Fluorescence in situ hybridization (FISH) signal analysis using automated generated projection images 
Fluorescence in situ hybridization (FISH) tests provide promising molecular imaging biomarkers to more accurately and reliably detect and diagnose cancers and genetic disorders. Since current manual FISH signal analysis is low-efficient and inconsistent, which limits its clinical utility, developing automated FISH image scanning systems and computer-aided detection (CAD) schemes has been attracting research interests. To acquire high-resolution FISH images in a multi-spectral scanning mode, a huge amount of image data with the stack of the multiple three-dimensional (3-D) image slices is generated from a single specimen. Automated preprocessing these scanned images to eliminate the non-useful and redundant data is important to make the automated FISH tests acceptable in clinical applications. In this study, a dual-detector fluorescence image scanning system was applied to scan four specimen slides with FISH-probed chromosome X. A CAD scheme was developed to detect analyzable interphase cells and map the multiple imaging slices recorded FISH-probed signals into the 2-D projection images. CAD scheme was then applied to each projection image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm, identify FISH-probed signals using a top-hat transform, and compute the ratios between the normal and abnormal cells. To assess CAD performance, the FISH-probed signals were also independently visually detected by an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots in four testing samples. The study demonstrated the feasibility of automated FISH signal analysis applying a CAD scheme to the automated generated 2-D projection images.
PMCID: PMC3496037  PMID: 22935778
Fluorescence in situ hybridization (FISH); Automated FISH signal analysis; Computer-aided detection (CAD); Molecular imaging biomarker
7.  Automated counting for Plasmodium falciparum cytoadherence experiments 
Malaria Journal  2011;10:91.
The ability of mature forms of Plasmodium falciparum infected erythrocytes to bind to a range of host receptors including those displayed on endothelial cells has been associated with the pathology of this infection. Investigations into this adhesive phenomenon have used protein and cell-based adhesion assays to quantify the ability of infected red blood cells to bind. These adhesion assays tend to have relatively high inherent variability and so require multiple experiments in order to provide good quantitation. This means that investigators doing these experiments must count many fields of adherent parasites, a task that is time-consuming and laborious. To address this issue and to facilitate cytoadherence research, developed automated protocols were developed for counting parasite adhesion.
Parasite adhesion assays were mainly carried out under static conditions using purified receptors, which is the simplest form of these assays and is translatable to the field. Two different software platforms were used, one commercial (Image Pro-Plus (Media Cybernetics)) and one available in the public domain (ImageSXM) based on the freely available NIH Image software. The adhesion assays were performed and parasite binding quantified using standard manual techniques. Images were also captured using video microscopy and analysed using the two automated systems. The results generated by each system were compared using the Bland and Altman method for assessing the agreement between two methods.
Both automated counting programs showed concordance compared to the 'gold standard' manual counting within the normal range of adhesion seen with these assays, although the ImageSXM technique had some systematic bias. There was some fall-off in accuracy at very high parasite densities, but this can be resolved through good design of the experiments. Cell based assays were also used as inputs to one of the automated systems (ImageSXM) and produced variable, but encouraging, results.
The automated counting programs are an accurate and practical way of quantifying static parasite binding assays to purified proteins. They are less accurate when applied to cell based systems, but can still provide a reasonable level of accuracy to give a semi-quantitative readout.
PMCID: PMC3094228  PMID: 21496305
8.  Current automated 3D cell detection methods are not a suitable replacement for manual stereologic cell counting 
Stereologic cell counting has had a major impact on the field of neuroscience. A major bottleneck in stereologic cell counting is that the user must manually decide whether or not each cell is counted according to three-dimensional (3D) stereologic counting rules by visual inspection within hundreds of microscopic fields-of-view per investigated brain or brain region. Reliance on visual inspection forces stereologic cell counting to be very labor-intensive and time-consuming, and is the main reason why biased, non-stereologic two-dimensional (2D) “cell counting” approaches have remained in widespread use. We present an evaluation of the performance of modern automated cell detection and segmentation algorithms as a potential alternative to the manual approach in stereologic cell counting. The image data used in this study were 3D microscopic images of thick brain tissue sections prepared with a variety of commonly used nuclear and cytoplasmic stains. The evaluation compared the numbers and locations of cells identified unambiguously and counted exhaustively by an expert observer with those found by three automated 3D cell detection algorithms: nuclei segmentation from the FARSIGHT toolkit, nuclei segmentation by 3D multiple level set methods, and the 3D object counter plug-in for ImageJ. Of these methods, FARSIGHT performed best, with true-positive detection rates between 38 and 99% and false-positive rates from 3.6 to 82%. The results demonstrate that the current automated methods suffer from lower detection rates and higher false-positive rates than are acceptable for obtaining valid estimates of cell numbers. Thus, at present, stereologic cell counting with manual decision for object inclusion according to unbiased stereologic counting rules remains the only adequate method for unbiased cell quantification in histologic tissue sections.
PMCID: PMC4019880  PMID: 24847213
automated cell segmentation; disector; FARSIGHT; Fractionator; ImageJ; stereology; stem cells
9.  Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network 
Sensors (Basel, Switzerland)  2011;11(11):10534-10556.
The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage and Fractal Dimension. A constant Region of Interest (ROI) was defined and background extraction by a Gaussian Blurring Filter was performed. Image subtraction within ROI was followed by the sum of the RGB channels matrices. Percent Coverage was calculated on the resulting image. Fractal Dimension was estimated using the box-counting method. The images were then resized to a dimension in pixels equal to a power of 2, allowing subdivision into sub-multiple quadrants. In comparisons of manual and automated Percent Coverage and Fractal Dimension estimates, the former showed an overestimation tendency for both parameters. The primary limitations on the automatic analysis of benthic images were habitat variations in sediment texture and water column turbidity. The application of filters for background corrections is a required preliminary step for the efficient recognition of animals and bacterial mat patches.
PMCID: PMC3274299  PMID: 22346657
cabled observatory; automated image analysis; squat lobster (Munida quadrispina); bacterial mat (Beggiatoa spp.); Scale-Invariant Feature Transform (SIFT); Fourier Descriptors (FD); Partial Least Square Discriminant Analysis (PLSDA); percentage of coverage, fractal dimension
10.  A flow cytometry-based workflow for detection and quantification of anti-plasmodial antibodies in vaccinated and naturally exposed individuals 
Malaria Journal  2012;11:367.
Antibodies play a central role in naturally acquired immunity against Plasmodium falciparum. Current assays to detect anti-plasmodial antibodies against native antigens within their cellular context are prone to bias and cannot be automated, although they provide important information about natural exposure and vaccine immunogenicity. A novel, cytometry-based workflow for quantitative detection of anti-plasmodial antibodies in human serum is presented.
Fixed red blood cells (RBCs), infected with late stages of P. falciparum were utilized to detect malaria-specific antibodies by flow cytometry with subsequent automated data analysis. Available methods for data-driven analysis of cytometry data were assessed and a new overlap subtraction algorithm (OSA) based on open source software was developed. The complete workflow was evaluated using sera from two GMZ2 malaria vaccine trials in semi-immune adults and pre-school children residing in a malaria endemic area.
Fixation, permeabilization, and staining of infected RBCs were adapted for best operation in flow cytometry. As asexual blood-stage vaccine candidates are designed to induce antibody patterns similar to those in semi-immune adults, serial dilutions of sera from heavily exposed individuals were compared to naïve controls to determine optimal antibody dilutions. To eliminate investigator effects introduced by manual gating, a non-biased algorithm (OSA) for data-driven gating was developed. OSA-derived results correlated well with those obtained by manual gating (r between 0.79 and 0.99) and outperformed other model-driven gating methods. Bland-Altman plots confirmed the agreement of manual gating and OSA-derived results. A 1.33-fold increase (p=0.003) in the number of positive cells after vaccination in a subgroup of pre-school children vaccinated with 100 μg GMZ2 was present and in vaccinated adults from the same region we measured a baseline-corrected 1.23-fold, vaccine-induced increase in mean fluorescence intensity of positive cells (p=0.03).
The current workflow advances detection and quantification of anti-plasmodial antibodies through improvement of a bias-prone, low-throughput to an unbiased, semi-automated, scalable method. In conclusion, this work presents a novel method for immunofluorescence assays in malaria research.
PMCID: PMC3545855  PMID: 23130649
Malaria; Flow cytometry-based IFA; Algorithmic data analysis; Anti-malarial antibodies; Human serum
11.  TNF-α and IL-1α but not MCP-1 and Rantes increase significantly the formation of p-H2AX foci in naïve BM-derived TNFR1/p55KO EPCs 
Journal of Radiation Research  2014;55(Suppl 1):i122-i123.
Background: Tumor necrosis factor-α (TNF) binds two receptors TNFR1/p55 and TNFR2/p75 and activates several signaling cascades. Ionizing radiation (IR) increases tissue levels of TNF. TNF signaling regulates numerous cytokines and chemokines that are known to mediate radiation-induced non-targeted effects (NTEs), a phenomenon where cells that are not directly ‘hit’ by IR exhibit IR effects as a result of signals received from nearby or distant IR cells. Little is known about the role of p55 or p75 in regulating NTE in bone marrow (BM) cells, specifically in BM-derived endothelial progenitor cells (EPCs). In media transfer experiments, we have previously shown that compared with WT EPCs, early NTEs (within 1–5 h) are inhibited in p55KO and p75KO EPCs, whereas delayed NTEs (within 3–5 days) are amplified in p55KO and to a lesser degree in p75KO EPCs, suggesting significant role of TNFR/p75 signaling (the remaining active receptor in p55KO EPCs) in mediating delayed NTEs. We hypothesized that signaling through TNFR2/p75 may alter radiation-induced TNF-mediated inflammatory response increasing tissue levels of various cytokines, chemokines and growth factors that could then induce NTE, possibly, via activation of NFkB and other stress response transcription factors.
Methods: To test our hypothesis ex vivo, expanded p55KO EPCs were irradiated with 1 Gy of γ-IR, then IR-conditioned medium (CM) was collected at 1, 5, 24 h, and 3, 5 days post-IR. CM from IR p55KO EPCs were processed for multiplex ELISA (12 proteins). After determining concentrations of each of 12 proteins in control and IR-CM media of p55KO EPCs over 5 days, we treated naïve p55KO EPCs with various concentrations of four mouse recombinant (rm) proteins that were steadily increased in IR-CM between Days 3–5. After 24 h incubation, naïve p55KO EPCs were stained with anti-p-H2AX antibodies and the formation of p-H2AX foci was visualized at ×100 magnification using laser scanning confocal microscopy. The p-H2AX foci were quantified manually by a single investigator blindfolded to the treatment conditions and were confirmed using computer-assisted algorithm.
Results: ELISA profiling of 12 proteins in IR-CM over 5 days post-IR showed 200–1600% increases (P < 0.02, at least, p55KO vs WT, Days 3–5) in cumulative levels of TNF, IFNr, IL1α, IL1β, IL6, EGF, MIP-1α, MCP-1, GCSF, GM-CSF, Rantes and Leptin. The steadiest and the highest increases between Days 3 and 5 were observed in IL-1α, MCP-1 and Rantes.
Naïve p55KO EPCs were then treated ex vivo with concentrations determined in the ELISA: IL-1α (290, 580 pg/ml), MCP-1 (580, 1160, 2900 pg/ml), Rantes (600, 1500 pg/ml) and TNF (100 pg/ml, 1, 40 ng/ml). After 24 h incubation with rm proteins, p55KO EPCs were stained with anti-p-H2AX antibodies. The cells were imaged and the quantification of the p-H2AX foci was performed as described above.
Results showed that the mean p-H2AX foci count of MCP-1 and Rantes was not significantly different from control which had a mean of 0.98 p-H2AX foci/cell count, with the exception of MCP-1 at 1160 pg/ml (P < 0.03, mean foci count of 1.9). TNF-treated naïve p55KO EPCs showed a significant increase in the mean p-H2AX foci/cell count at all concentration compared with the control, MCP-1 and Rantes (P < 0.0001 with the mean p-H2AX foci ranging from 2.8 to 3.9).
IL1α-treated p55KO EPCs showed the greatest increase in p-H2AX foci with the mean foci count of 7.1 at 290 pg/ml and 9.3 at 580 pg/ml, and was significantly different from all tested mouse recombinant proteins. Analysis of p-H2AX foci distribution of EPCs with one or more foci showed that in control p55KO EPCs, <1% of cells had a maximum of 4–9 p-H2AX foci/cell. Whereas in TNF- and IL-1α-treated p55KO EPCs, >2% and >4% of cells had 9–18 foci/cell, respectively. Remarkably, 1% of cells had as many as 18–31 foci/cell for TNF-treated cells and as many as 19–51 foci/cell for IL-1α-treated p55KO EPCs.
We conclude that TNF-TNFR2/p75 axis may induce NTEs in naïve BM-EPCs and suggest that blocking/neutralizing TNFR2/p75 signaling could represent a mitigating measure for prevention of delayed NTEs, specifically, in BM-derived EPCs and, conceivably, in BM milieu in general.
PMCID: PMC3941533
TNF; TNFR2/p75; BM-EPCs; non-targeted; IL-1α
12.  Web platform using digital image processing and geographic information system tools: a Brazilian case study on dengue 
Dengue fever is endemic in Asia, the Americas, the East of the Mediterranean and the Western Pacific. According to the World Health Organization, it is one of the diseases of greatest impact on health, affecting millions of people each year worldwide. A fast detection of increases in populations of the transmitting vector, the Aedes aegypti mosquito, is essential to avoid dengue outbreaks. Unfortunately, in several countries, such as Brazil, the current methods for detecting populations changes and disseminating this information are too slow to allow efficient allocation of resources to fight outbreaks. To reduce the delay in providing the information regarding A. aegypti population changes, we propose, develop, and evaluate a system for counting the eggs found in special traps and to provide the collected data using a web structure with geographical location resources.
One of the most useful tools for the detection and surveillance of arthropods is the ovitrap, a special trap built to collect the mosquito eggs. This allows for an egg counting process, which is still usually performed manually, in countries such as Brazil. We implement and evaluate a novel system for automatically counting the eggs found in the ovitraps’ cardboards. The system we propose is based on digital image processing (DIP) techniques, as well as a Web based Semi-Automatic Counting System (SCSA-WEB). All data collected are geographically referenced in a geographic information system (GIS) and made available on a Web platform. The work was developed in Gama’s administrative region, in Brasília/Brazil, with the aid of the Environmental Surveillance Directory (DIVAL-Gama) and Brasília’s Board of Health (SSDF), in partnership with the University of Brasília (UnB). The system was built based on a field survey carried out during three months and provided by health professionals. These professionals provided 84 cardboards from 84 ovitraps, sized 15 × 5 cm. In developing the system, we conducted the following steps:i.Obtain images from the eggs on an ovitrap’s cardboards, with a microscope.ii.Apply a proposed image-processing-based semi-automatic counting system. The system we developed uses the Java programming language and the Java Server Faces technology. This is a framework suite for web applications development. This approach will allow a simple migration to any Operating System platform and future applications on mobile devices.iii.Collect and store all data into a Database (DB) and then georeference them in a GIS. The Database Management System used to develop the DB is based on PostgreSQL. The GIS will assist in the visualization and spatial analysis of digital maps, allowing the location of Dengue outbreaks in the region of study. This will also facilitate the planning, analysis, and evaluation of temporal and spatial epidemiology, as required by the Brazilian Health Care Control Center.iv.Deploy the SCSA-WEB, DB and GIS on a single Web platform.
The statistical results obtained by DIP were satisfactory when compared with the SCSA-WEB’s semi-automated eggs count. The results also indicate that the time spent in manual counting has being considerably reduced when using our fully automated DIP algorithm and semi-automated SCSA-WEB. The developed georeferencing Web platform proves to be of great support for future visualization with statistical and trace analysis of the disease.
The analyses suggest the efficiency of our algorithm for automatic eggs counting, in terms of expediting the work of the laboratory technician, reducing considerably its time and error counting rates. We believe that this kind of integrated platform and tools can simplify the decision making process of the Brazilian Health Care Control Center.
PMCID: PMC4502932  PMID: 26178732
Dengue; Ovitraps; Image processing; Geographic information system
13.  Arraycount, an algorithm for automatic cell counting in microwell arrays 
BioTechniques  2009;47(3):x-xvi.
Microscale technologies have emerged as a powerful tool for studying and manipulating biological systems and miniaturizing experiments. However, the lack of software complementing these techniques has made it difficult to apply them for many high-throughput experiments. This work establishes Arraycount, an approach to automatically count cells in microwell arrays. The procedure consists of fluorescent microscope imaging of cells that are seeded in microwells of a microarray system and then analyzing images via computer to recognize the array and count cells inside each microwell. To start counting, green and red fluorescent images (representing live and dead cells respectively), are extracted from the original image and processed separately. A template-matching algorithm, is proposed in which pre-defined well and cell templates are matched against the red and green images to locate microwells and cells. Subsequently, local maxima in the correlation maps is determined and local maxima maps are thresholded. At the end, the software records the cell counts for each detected microwell on the original image in high-throughput. The automated counting was shown to be accurate compared with manual counting, with a difference of ~1–2 cells per microwell: based on cell concentration, the absolute difference between manual and automatic counting measurements was 2.5–13%.
PMCID: PMC2791213  PMID: 19852758
cell counting; microarray; high-throughput; microwell; image analysis
14.  Fully automated microvessel counting and hot spot selection by image processing of whole tumour sections in invasive breast cancer. 
Journal of Clinical Pathology  1999;52(3):184-192.
BACKGROUND: Manual counting of microvessels is subjective and may lead to unacceptable interobserver variability, which may explain conflicting results. AIMS: To develop and test an automated method for microvessel counting and objective selection of the hot spot, based on image processing of whole sections, and to compare this with manual selection of a hot spot and counting of microvessels. METHODS: Microvessels were stained by CD31 immunohistochemistry in 10 cases of invasive breast cancer. The number of microvessels was counted manually in a subjectively selected hot spot, and also in the same complete tumour sections by interactive and automated image processing methods. An algorithm identified the hot spots from microvessel maps of the whole tumour section. RESULTS: No significant difference in manual microvessel counts was found between two observers within the same hot spot, and counts were significantly correlated. However, when the hot spot was reselected, significantly different results were found between repeated counts by the same observer. Counting all microvessels manually within the entire tumour section resulted in significantly different hot spots than manual counts in selected hot spots by the same observer. Within the entire tumour section no significant differences were found between the hot spots of the manual and automated methods using an automated microscope. The hot spot was found using an eight connective path search algorithm, was located at or near the border of the tumour, and (depending on the size of the hot spot) did not always contain the field with the largest number of microvessels. CONCLUSIONS: The automated counting of microvessels is preferable to the manual method because of the reduction in measurement time when the complete tumour is scanned, the greater accuracy and objectivity of hot spot selection, and the possibility of visual inspection and relocation of each measurement field afterwards.
PMCID: PMC501077  PMID: 10450177
15.  Automated estimation of parasitaemia of Plasmodium yoelii-infected mice by digital image analysis of Giemsa-stained thin blood smears 
Malaria Journal  2010;9:348.
Parasitaemia, the percentage of infected erythrocytes, is used to measure progress of experimental Plasmodium infection in infected hosts. The most widely used technique for parasitaemia determination is manual microscopic enumeration of Giemsa-stained blood films. This process is onerous, time consuming and relies on the expertise of the experimenter giving rise to person-to-person variability. Here the development of image-analysis software, named Plasmodium AutoCount, which can automatically generate parasitaemia values from Plasmodium-infected blood smears, is reported.
Giemsa-stained blood smear images were captured with a camera attached to a microscope and analysed using a programme written in the Python programming language. The programme design involved foreground detection, cell and infection detection, and spurious hit filtering. A number of parameters were adjusted by a calibration process using a set of representative images. Another programme, Counting Aid, written in Visual Basic, was developed to aid manual counting when the quality of blood smear preparation is too poor for use with the automated programme.
This programme has been validated for use in estimation of parasitemia in mouse infection by Plasmodium yoelii and used to monitor parasitaemia on a daily basis for an entire challenge infection. The parasitaemia values determined by Plasmodium AutoCount were shown to be highly correlated with the results obtained by manual counting, and the discrepancy between automated and manual counting results were comparable to those found among manual counts of different experimenters.
Plasmodium AutoCount has proven to be a useful tool for rapid and accurate determination of parasitaemia from infected mouse blood. For greater accuracy when smear quality is poor, Plasmodium AutoCount, can be used in conjunction with Counting Aid.
PMCID: PMC3245511  PMID: 21122144
16.  Semi-Automated Atlas-based Analysis of Brain Histological Sections 
Journal of neuroscience methods  2010;196(1):12-19.
Quantifying the location and/or number of features in a histological section of the brain currently requires one to first, manually register a corresponding section from a tissue atlas onto the experimental section and second, count the features. No automated method exists for the first process (registering), and most automated methods for the second process (feature counting) operate reliably only in a high signal-to-noise regime. To reduce experimenter bias and inconsistencies and increase the speed of these analyses, we developed Atlas Fitter, a semi-automated, open-source MatLab-based software package that assists in rapidly registering atlas panels onto histological sections. We also developed CellCounter, a novel fully-automated cell counting algorithm that is designed to operate on images with non-uniform background intensities and low signal-to-noise ratios.
PMCID: PMC3075115  PMID: 21194546
Histology; Mapping; Atlas; Analysis; Software; Cell Counting; IEG; Arc
17.  INsPECT, an Open-Source and Versatile Software for Automated Quantification of (Leishmania) Intracellular Parasites 
Intracellular protozoan parasites are causative agents of infectious diseases that constitute major health problems for developing countries. Leishmania sp., Trypanosoma cruzi or Toxoplasma gondii are all obligate intracellular protozoan parasites that reside and multiply within the host cells of mammals, including humans. Following up intracellular parasite proliferation is therefore an essential and a quotidian task for many laboratories working on primary screening of new natural and synthetic drugs, analyzing drug susceptibility or comparing virulence properties of natural and genetically modified strains. Nevertheless, laborious manual microscopic counting of intracellular parasites is still the most commonly used approach. Here, we present INsPECT (Intracellular ParasitE CounTer), an open-source and platform independent software dedicated to automate infection level measurement based on fluorescent DNA staining. It offers the possibility to choose between different types of analyses (fluorescent DNA acquisitions only or in combination with phase contrast image set to further separate intra- from extracellular parasites), and software running modes (automatic or custom). A proof-of-concept study with intracellular Leishmania infantum parasites stained with DAPI (4′,6-diamidino-2-phenylindole) confirms a good correspondence between digital results and the “gold standard” microscopic counting method with Giemsa. Interestingly, this software is versatile enough to accurately detect intracellular T. gondii parasites on images acquired with High Content Screening (HCS) systems. In conclusion, INsPECT software is proposed as a new fast and simple alternative to the classical intracellular Leishmania quantification methods and can be adapted for mid to large-scale drug screening against different intracellular parasites.
Author Summary
Research on intracellular parasites require using non-invasive technologies to follow up parasite proliferation inside their natural host cells by staying in the more physiological conditions as possible. High Content Screening (HCS) technology has recently emerged as a powerful image-based approach to screen new anti-parasitic compounds or to test parasite susceptibility to existing drugs in vitro. Nevertheless, such equipments will remain poorly accessible for most of academic and clinical diagnostic laboratories that mostly use more affordable, but laborious, microscopic counting procedures. The current work proposes new image-based, open-source software which provides a fast and accurate solution for investigating intracellular parasite quantification. Through an easy-to-use interface, cells' and parasites' information are dug out from DNA fluorescent images, and host cells' boundaries are extracted from corresponding phase contrast image set. Parasites are then reassigned to their related cells and intra/extracellular parasites are discriminated for each cell. The software further automatically calculates all data required for most of experimental infection studies. INsPECT software is proposed as a free substitute or complement to the available quantification methods for measuring Leishmania infection level in vitro. It may be enlarged, however, to different intracellular trypanosomatids or unrelated parasites such as T. gondii.
PMCID: PMC4022486  PMID: 24831235
18.  Mitosis detection in breast cancer histological images An ICPR 2012 contest 
In the framework of the Cognitive Microscope (MICO) project, we have set up a contest about mitosis detection in images of H and E stained slides of breast cancer for the conference ICPR 2012. Mitotic count is an important parameter for the prognosis of breast cancer. However, mitosis detection in digital histopathology is a challenging problem that needs a deeper study. Indeed, mitosis detection is difficult because mitosis are small objects with a large variety of shapes, and they can thus be easily confused with some other objects or artefacts present in the image. We added a further dimension to the contest by using two different slide scanners having different resolutions and producing red-green-blue (RGB) images, and a multi-spectral microscope producing images in 10 different spectral bands and 17 layers Z-stack. 17 teams participated in the study and the best team achieved a recall rate of 0.7 and precision of 0.89.
Several studies on automatic tools to process digitized slides have been reported focusing mainly on nuclei or tubule detection. Mitosis detection is a challenging problem that has not yet been addressed well in the literature.
Mitotic count is an important parameter in breast cancer grading as it gives an evaluation of the aggressiveness of the tumor. However, consistency, reproducibility and agreement on mitotic count for the same slide can vary largely among pathologists. An automatic tool for this task may help for reaching a better consistency, and at the same time reducing the burden of this demanding task for the pathologists.
Subjects and Methods:
Professor Frιdιrique Capron team of the pathology department at Pitiι-Salpκtriθre Hospital in Paris, France, has selected a set of five slides of breast cancer. The slides are stained with H and E. They have been scanned by three different equipments: Aperio ScanScope XT slide scanner, Hamamatsu NanoZoomer 2.0-HT slide scanner and 10 bands multispectral microscope. The data set is made up of 50 high power fields (HPF) coming from 5 different slides scanned at ×40 magnification. There are 10 HPFs/slide. The pathologist has annotated all the mitotic cells manually. A HPF has a size of 512 μm × 512 μm (that is an area of 0.262 mm 2 , which is a surface equivalent to that of a microscope field diameter of 0.58 mm. These 50 HPFs contain a total of 326 mitotic cells on images of both scanners, and 322 mitotic cells on the multispectral microscope.
Up to 129 teams have registered to the contest. However, only 17 teams submitted their detection of mitotic cells. The performance of the best team is very promising, with F-measure as high as 0.78. However, the database we provided is by far too small for a good assessment of reliability and robustness of the proposed algorithms.
Mitotic count is an important criterion in the grading of many types of cancers, however, very little research has been made on automatic mitotic cell detection, mainly because of a lack of available data. A main objective of this contest was to propose a database of mitotic cells on digitized breast cancer histopathology slides to initiate works on automated mitotic cell detection. In the future, we would like to extend this database to have much more images from different patients and also for different types of cancers. In addition, mitotic cells should be annotated by several pathologists to reflect the partial agreement among them.
PMCID: PMC3709417  PMID: 23858383
Automated mitotic cell detection; breast cancer; H and E stained histological slides
19.  Automated Enumeration of Groups of Marine Picoplankton after Fluorescence In Situ Hybridization 
We describe here an automated system for the counting of multiple samples of double-stained microbial cells on sections of membrane filters. The application integrates an epifluorescence microscope equipped with motorized z-axis drive, shutters, and filter wheels with a scanning stage, a digital camera, and image analysis software. The relative abundances of specific microbial taxa are quantified in samples of marine picoplankton, as detected by fluorescence in situ hybridization (FISH) and catalyzed reporter deposition. Pairs of microscopic images are automatically acquired from numerous positions at two wavelengths, and microbial cells with both general DNA and FISH staining are counted after object edge detection and signal-to-background ratio thresholding. Microscopic fields that are inappropriate for cell counting are automatically excluded prior to measurements. Two nested walk paths guide the device across a series of triangular preparations until a user-defined number of total cells has been analyzed per sample. A backup autofocusing routine at incident light allows automated refocusing between individual samples and can reestablish the focal plane after fatal focusing errors at epifluorescence illumination. The system was calibrated to produce relative abundances of FISH-stained cells in North Sea samples that were comparable to results obtained by manual evaluation. Up to 28 preparations could be analyzed within 4 h without operator interference. The device was subsequently applied for the counting of different microbial populations in incubation series of North Sea waters. Automated digital microscopy greatly facilitates the processing of numerous FISH-stained samples and might thus open new perspectives for bacterioplankton population ecology.
PMCID: PMC154506  PMID: 12732531
20.  Reliable enumeration of malaria parasites in thick blood films using digital image analysis 
Malaria Journal  2009;8:218.
Quantitation of malaria parasite density is an important component of laboratory diagnosis of malaria. Microscopy of Giemsa-stained thick blood films is the conventional method for parasite enumeration. Accurate and reproducible parasite counts are difficult to achieve, because of inherent technical limitations and human inconsistency. Inaccurate parasite density estimation may have adverse clinical and therapeutic implications for patients, and for endpoints of clinical trials of anti-malarial vaccines or drugs. Digital image analysis provides an opportunity to improve performance of parasite density quantitation.
Accurate manual parasite counts were done on 497 images of a range of thick blood films with varying densities of malaria parasites, to establish a uniformly reliable standard against which to assess the digital technique. By utilizing descriptive statistical parameters of parasite size frequency distributions, particle counting algorithms of the digital image analysis programme were semi-automatically adapted to variations in parasite size, shape and staining characteristics, to produce optimum signal/noise ratios.
A reliable counting process was developed that requires no operator decisions that might bias the outcome. Digital counts were highly correlated with manual counts for medium to high parasite densities, and slightly less well correlated with conventional counts. At low densities (fewer than 6 parasites per analysed image) signal/noise ratios were compromised and correlation between digital and manual counts was poor. Conventional counts were consistently lower than both digital and manual counts.
Using open-access software and avoiding custom programming or any special operator intervention, accurate digital counts were obtained, particularly at high parasite densities that are difficult to count conventionally. The technique is potentially useful for laboratories that routinely perform malaria parasite enumeration. The requirements of a digital microscope camera, personal computer and good quality staining of slides are potentially reasonably easy to meet.
PMCID: PMC2761936  PMID: 19775454
21.  An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy 
BMC Bioinformatics  2013;14:297.
In recent years, high-throughput microscopy has emerged as a powerful tool to analyze cellular dynamics in an unprecedentedly high resolved manner. The amount of data that is generated, for example in long-term time-lapse microscopy experiments, requires automated methods for processing and analysis. Available software frameworks are well suited for high-throughput processing of fluorescence images, but they often do not perform well on bright field image data that varies considerably between laboratories, setups, and even single experiments.
In this contribution, we present a fully automated image processing pipeline that is able to robustly segment and analyze cells with ellipsoid morphology from bright field microscopy in a high-throughput, yet time efficient manner. The pipeline comprises two steps: (i) Image acquisition is adjusted to obtain optimal bright field image quality for automatic processing. (ii) A concatenation of fast performing image processing algorithms robustly identifies single cells in each image. We applied the method to a time-lapse movie consisting of ∼315,000 images of differentiating hematopoietic stem cells over 6 days. We evaluated the accuracy of our method by comparing the number of identified cells with manual counts. Our method is able to segment images with varying cell density and different cell types without parameter adjustment and clearly outperforms a standard approach. By computing population doubling times, we were able to identify three growth phases in the stem cell population throughout the whole movie, and validated our result with cell cycle times from single cell tracking.
Our method allows fully automated processing and analysis of high-throughput bright field microscopy data. The robustness of cell detection and fast computation time will support the analysis of high-content screening experiments, on-line analysis of time-lapse experiments as well as development of methods to automatically track single-cell genealogies.
PMCID: PMC3850979  PMID: 24090363
22.  γH2AX foci as a measure of DNA damage: a computational approach to automatic analysis 
Mutation research  2011;711(1-2):49-60.
The γH2AX focus assay represents a fast and sensitive approach for detection of one of the critical types of DNA damage – double-strand breaks (DSB) induced by various cytotoxic agents including ionising radiation. Apart from research applications, the assay has a potential in clinical medicine/pathology, such as assessment of individual radiosensitivity, response to cancer therapies, as well as in biodosimetry. Given that generally there is a direct relationship between numbers of microscopically visualised γH2AX foci and DNA DSB in a cell, the number of foci per nucleus represents the most efficient and informative parameter of the assay. Although computational approaches have been developed for automatic focus counting, the tedious and time consuming manual focus counting still remains the most reliable approach due to limitations of computational approaches.
We suggest a computational approach and associated software for automatic focus counting that minimises these limitations. Our approach, while using standard image processing algorithms, maximises the automation of identification of nuclei/cells in complex images, offers an efficient way to optimise parameters used in the image analysis and counting procedures, optionally invokes additional procedures to deal with variations in intensity of the signal and background in individual images, and provides automatic batch processing of a series of images. We report results of validation studies that demonstrated correlation of manual focus counting with results obtained using our computational algorithm for mouse jejunum touch prints, mouse tongue sections and human blood lymphocytes as well as radiation dose response of γH2AX focus induction for these biological specimens.
PMCID: PMC3101310  PMID: 21216255
23.  High-throughput Physical Mapping of Chromosomes using Automated in situ Hybridization 
Projects to obtain whole-genome sequences for 10,000 vertebrate species1 and for 5,000 insect and related arthropod species2 are expected to take place over the next 5 years. For example, the sequencing of the genomes for 15 malaria mosquitospecies is currently being done using an Illumina platform3,4. This Anopheles species cluster includes both vectors and non-vectors of malaria. When the genome assemblies become available, researchers will have the unique opportunity to perform comparative analysis for inferring evolutionary changes relevant to vector ability. However, it has proven difficult to use next-generation sequencing reads to generate high-quality de novo genome assemblies5. Moreover, the existing genome assemblies for Anopheles gambiae, although obtained using the Sanger method, are gapped or fragmented4,6.
Success of comparative genomic analyses will be limited if researchers deal with numerous sequencing contigs, rather than with chromosome-based genome assemblies. Fragmented, unmapped sequences create problems for genomic analyses because: (i) unidentified gaps cause incorrect or incomplete annotation of genomic sequences; (ii) unmapped sequences lead to confusion between paralogous genes and genes from different haplotypes; and (iii) the lack of chromosome assignment and orientation of the sequencing contigs does not allow for reconstructing rearrangement phylogeny and studying chromosome evolution. Developing high-resolution physical maps for species with newly sequenced genomes is a timely and cost-effective investment that will facilitate genome annotation, evolutionary analysis, and re-sequencing of individual genomes from natural populations7,8.
Here, we present innovative approaches to chromosome preparation, fluorescent in situ hybridization (FISH), and imaging that facilitate rapid development of physical maps. Using An. gambiae as an example, we demonstrate that the development of physical chromosome maps can potentially improve genome assemblies and, thus, the quality of genomic analyses. First, we use a high-pressure method to prepare polytene chromosome spreads. This method, originally developed for Drosophila9, allows the user to visualize more details on chromosomes than the regular squashing technique10. Second, a fully automated, front-end system for FISH is used for high-throughput physical genome mapping. The automated slide staining system runs multiple assays simultaneously and dramatically reduces hands-on time11. Third, an automatic fluorescent imaging system, which includes a motorized slide stage, automatically scans and photographs labeled chromosomes after FISH12. This system is especially useful for identifying and visualizing multiple chromosomal plates on the same slide. In addition, the scanning process captures a more uniform FISH result. Overall, the automated high-throughput physical mapping protocol is more efficient than a standard manual protocol.
PMCID: PMC3671838  PMID: 22782181
Genetics; Issue 64; Entomology; Molecular Biology; Genomics; automation; chromosome; genome; hybridization; labeling; mapping; mosquito
24.  Measurement of TLR-Induced Macrophage Spreading by Automated Image Analysis: Differential Role of Myd88 and MAPK in Early and Late Responses 
Sensing of infectious danger by toll-like receptors (TLRs) on macrophages causes not only a reprogramming of the transcriptome but also changes in the cytoskeleton important for cell spreading and motility. Since manual determination of cell contact areas from fluorescence micrographs is very time-consuming and prone to bias, we have developed and tested algorithms for automated measurement of macrophage spreading. The two-step method combines identification of cells by nuclear staining with DAPI and cell surface staining of the integrin CD11b. Automated image analysis correlated very well with manual annotation in resting macrophages and early after stimulation, whereas at later time points the automated cell segmentation algorithm and manual annotation showed slightly larger variation. The method was applied to investigate the impact of genetic or pharmacological inhibition of known TLR signaling components. Deficiency in the adapter protein Myd88 strongly reduced spreading activity at the late time points, but had no impact early after LPS-stimulation. A similar effect was observed upon pharmacological inhibition of MEK1, the kinase activating the mitogen-activated protein kinases (MAPK) ERK1/2, indicating that ERK1/2 mediates Myd88-dependent macrophages spreading. In contrast, macrophages lacking the MAPK p38 were impaired in the initial spreading response but responded normally 8–24 h after stimulation. The dichotomy of p38 and ERK1/2 MAPK effects on early and late macrophage spreading raises the question which of the respective substrate proteins mediate(s) cytoskeletal remodeling and spreading. The automated measurement of cell spreading described here increases the objectivity and greatly reduces the time required for such investigations and is therefore expected to facilitate larger throughput analysis of macrophage spreading, e.g., in siRNA knockdown screens.
PMCID: PMC3198511  PMID: 22028692
macrophage; spreading; TLR; image analysis
25.  A methodology to ensure and improve accuracy of Ki67 labelling index estimation by automated digital image analysis in breast cancer tissue 
Immunohistochemical Ki67 labelling index (Ki67 LI) reflects proliferative activity and is a potential prognostic/predictive marker of breast cancer. However, its clinical utility is hindered by the lack of standardized measurement methodologies. Besides tissue heterogeneity aspects, the key element of methodology remains accurate estimation of Ki67-stained/counterstained tumour cell profiles. We aimed to develop a methodology to ensure and improve accuracy of the digital image analysis (DIA) approach.
Tissue microarrays (one 1-mm spot per patient, n = 164) from invasive ductal breast carcinoma were stained for Ki67 and scanned. Criterion standard (Ki67-Count) was obtained by counting positive and negative tumour cell profiles using a stereology grid overlaid on a spot image. DIA was performed with Aperio Genie/Nuclear algorithms. A bias was estimated by ANOVA, correlation and regression analyses. Calibration steps of the DIA by adjusting the algorithm settings were performed: first, by subjective DIA quality assessment (DIA-1), and second, to compensate the bias established (DIA-2). Visual estimate (Ki67-VE) on the same images was performed by five pathologists independently.
ANOVA revealed significant underestimation bias (P < 0.05) for DIA-0, DIA-1 and two pathologists’ VE, while DIA-2, VE-median and three other VEs were within the same range. Regression analyses revealed best accuracy for the DIA-2 (R-square = 0.90) exceeding that of VE-median, individual VEs and other DIA settings. Bidirectional bias for the DIA-2 with overestimation at low, and underestimation at high ends of the scale was detected. Measurement error correction by inverse regression was applied to improve DIA-2-based prediction of the Ki67-Count, in particular for the clinically relevant interval of Ki67-Count < 40%. Potential clinical impact of the prediction was tested by dichotomising the cases at the cut-off values of 10, 15, and 20%. Misclassification rate of 5-7% was achieved, compared to that of 11-18% for the VE-median-based prediction.
Our experiments provide methodology to achieve accurate Ki67-LI estimation by DIA, based on proper validation, calibration, and measurement error correction procedures, guided by quantified bias from reference values obtained by stereology grid count. This basic validation step is an important prerequisite for high-throughput automated DIA applications to investigate tissue heterogeneity and clinical utility aspects of Ki67 and other immunohistochemistry (IHC) biomarkers.
PMCID: PMC4053156  PMID: 24708745

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