BACKGROUND
a precise determination of cell death model is essential for biomedical researches as cell death pathways are intimately associated with normal physiology and disease-related pathogenesis.
the widely used colormetric cytotoxicity assays such as lactate dehydrogenase  release, mtt /mts  based assays, etc., can only evaluate the viability of cell ensemble.
thus there is a strong demand for sensitive, quantitative, reliable and automated methods for the accurate assessment of cellular proliferation status with high contents of cellular information.
as a modern drug discovery tool, high content screening   <cit>  using automated fluorescence microscope is becoming an important and widely used research tool to assist researchers understanding complex cellular processes in disease pathogenesis, drug target validation and drug lead identification  <cit> .
using the hcs technology, abundant spatial and temporal morphologic information can be extracted from the cellular images, and the information can be used to determine whether a potential drug affects the functions of proteins or genes involved in a disease process.
however, it has been a challenge to perform quantitative analysis of the complex cellular images, and this significantly restricts the potential of hcs in drug discovery  <cit> .
thus, the availability of fully automated cellular image analysis systems is critical to the success of hcs.
the fluorescent images of human h <dig> neuroglioma cells exposed to different concentrations of cuo nanoparticles were collected by a high content fluorescence microscope – in cell analyzer  <dig>  using these cellular images, investigators can investigate the influences of the cuo nanoparticles to the cell viability and determine cell death mode by analyzing the percentage of dead/live cells.
however, there are thousands of cellular images generated in one experiment, and thousands of cells appear in each image.
it is impractical to count and quantify the cells manually.
therefore, a fully automated and robust cellular image analysis system is needed urgently.
for a fully automated cellular images analysis system, the detection and segmentation of nuclei are the two essential components  <cit> .
the problems of nuclei segmentation originate from uneven illumination, artifacts, nuclei clustering and low intensity contrast between the nuclei and the background  <cit> .
as seen in figure  <dig>  nuclei are of different sizes, intensities and shapes, which pose a problem in segmentation.
secondly, the intensity contrast of dark nuclei is very low, and the bright nuclei are found to cluster together.
the problem is further aggravated due to the presence of high-intensity noises in the dark nuclei region.
although some methods for fluorescent cellular image detection and segmentation have been proposed, a general purpose system that can perform the detection and segmentation tasks for all kinds of fluorescence microscopy images without any fine tuning is still not available.
this has motivated us to design a novel system to serve the purpose.
in  <cit>  some nuclei segmentation methods were proposed.
they combined the intensity gradient information with the shape information to separate the clustered nuclei by using a statistical model to merge the fragments of nuclei.
since the bright nuclei cluster together heavily, and the dark nuclei cannot be accurately separated from the background, these methods tend to fail because the shape information is not accurate.
in addition, edge based segmentation methods will fail due to the noisy and discontinuous edges  <cit> .
thresholding methods cannot separate the clustered nuclei  <cit> .
moreover, the contours' initialization of the snake and level set methods is much more challenging work  <cit> .
the goal of the present work is to develop an automated cellular image analysis system for quantitative analysis of viability of h <dig> neuroglioma cells exposed to cuo nanoparticles.
figure  <dig> provides a flowchart of the proposed system.
we first used a background correction method  <cit>  as a multiple adaptive thresholding technique to classify the nuclei image into three classes: bright nuclei, dark nuclei and background.
then we implemented a nuclei detection method based on the gaussian filtering and gradient vector field  <cit>  followed by the seeded watershed  <cit>  based region-growing algorithm to segment the clustered nuclei.
finally we proposed a statistical model based splitting method to reduce the under-segmentation problem.
RESULTS
materials
human h <dig> neuroglioma cells purchased from the atcc  were seeded into 96-well cell culture plates and cultured in dulbecco's modified eagle medium  supplemented with 10% fetal bovine serum, 1% penicillin-streptomycin solution .
the cells were incubated for  <dig> hours under the cell culture conditions , together with cuo nanoparticles at a concentration range of  <dig> – <dig> μm.
then a live/dead assay kit  for cell viability was applied to the cells according to the manufacturer's instruction.
in brief, the cells were cultured at 37°c for  <dig> min, with ethidium homodimer- <dig> , and hoechst dye  in each well.
high-content cellular fluorescence images were acquired using an automatic fluorescence microscope – in cell analyzer  <dig> .
the objective magnification is 10×.
numerical aperture is  <dig> , pixel width is  <dig>  μm and pixel height is  <dig>  μm for all the images taken.
the size of each image is  <dig> ×  <dig> pixels.
choice of parameters
validation and comparison of segmentation
to evaluate the accuracy of the proposed nuclei segmentation method, we randomly picked up ten nuclei images as the testing data set.
figure 3- and figure  <dig> show the representative detection and segmentation results respectively.
four possible segmentation errors were considered: over-segmentation, under-segmentation, false negative and false positive .
the false positive rate  and the false negative rate  are defined as follows:
fpr=#offalsenuclei#oftotalnucleiandfnr=#ofmissednuclei#oftotalnuclei
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=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@9071@
to further evaluate the effectiveness of proposed method, we compared the segmentation results of the proposed protocol with the cellprofiler  <cit> , which is free available software and based on watershed method.
we compared the two methods using: correctly segmented nuclei, fnr and fpr.
as indicated in table  <dig>  the proposed method outperforms the watershed method.
both the fnr and fpr values of the watershed method are much higher than that of the proposed method.
the reason is that watershed algorithm missed some dark nuclei, and under-segmented the bright nuclei clusters.
the high fpr values of the watershed method were caused by the fluorescent noises.
we also tested the cell counting tool in imagej – itcn.
this tool was developed by thomas kuo and jiyun byun .
the itcn uses laplacian of gaussian  filtering as the nuclei detector.
there is a parameter, i.e.
diameter of a cell, in itcn, and we tested the tool with two different diameter values of the cell:  <dig>  micron  and  <dig>  micron .
the results are shown in figure 5- and 5- respectively.
as seen in figure 5-, there are too many noises  and over-detection because we used the smaller diameter .
in figure 5-, there are some bright nuclei are under-segmented  when we use larger diameter .
in addition, many noisy points still exist .
in conclusion, the itcn results are very sensitive to the initial values of the diameter of cell and the intensity noises.
it works well if the cells or nuclei have similar size, intensity and round shape.
however, in our study, the two kinds of nuclei have different size, intensity and shape.
the bright nuclei clustered together, and the contrast of the dark nuclei is very low.
these difficulties limit the performance of itcn.
the detection result of the proposed method is provided in figure 5-.
analysis of cell death induced by cuo nanoparticles
in this study, we applied the proposed system to analyze the toxic response of the human h <dig> cells exposed to the cuo nanoparticles.
we treated the human h <dig> neuroglioma cells with five different concentrations of cuo nanoparticles:  <dig> ,  <dig> ,  <dig>   <dig>  and  <dig> μm for  <dig> hours.
we used two fluorescence dyes, hoechst  <dig> and ethidium homodimer- <dig>  for staining total cells and dead cells , respectively, following the vendor suggested protocols.
the blue fluorescent hoechst dye  are cell permeable nucleic acid stains that have multiple applications, including determination of cell number.
the fluorescence of the dye is very sensitive to dna conformation in both live and dead cells.
ethd- <dig>  however, enters cells only with damaged membranes and undergoes a 40-fold enhancement of fluorescence upon binding to nucleic acids, thereby producing a bright red fluorescence in dead cells .
thus, entry of ethd- <dig> into living cells with intact plasma membrane is prohibited.
the dead and total cells in each well were stained for  <dig> hours by ethd- <dig>  and hoechst dye , respectively.
we acquired the cellular images using the in cell analyzer  <dig>  an automatic fluorescence microscope after the staining step.
the toxic effects of the cuo nanopaticles upon the human h <dig> neuroglioma cells can be quantified by determining the percentages of dead/total cells treated with different concentrations of cuo nanoparticles.
thus we only need to count the number of total and dead cells using our automatic image analysis system.
figure 6-,  and 6- give the mean and standard deviation of total cells, dead cells, and percentage of dead/total cells in the untreated and the five different concentrations of cuo nanoparticles treated wells.
to determine the statistical significance, we also performed the student's t-tests for cell death ratios  between the cuo nanoparticle treated and untreated human h <dig> neuroglioma cells.
table  <dig> gives the p-values of these t-tests.
from figure  <dig> and table  <dig>  we conclude that the increases of cell death ratio is statistically significant in h <dig> cells treated by > <dig> μm of cuo nanoparticles .
the computational results are consistent with our biological finding that human h <dig> neuroglioma cells have a dose-dependent toxic response to the insult of cuo nanoparticles.
interestingly, benson jm., et al.
<cit>  reported that in vitro cytotoxicity of the ni-cuo compounds to pulmonary alveolar macrophages is correlated to their increased cu content and decreased ni content.
as such, our results, which are in good agreement with benson et al., indicate cuo nanoparticles are cytotoxic to human brain cells.
CONCLUSIONS
herein we present a fully automated cellular image analysis system for quantitative analysis of the viability of human h <dig> neuroglioma cells exposed to cuo nanoparticles with different concentrations .
a multiple thresholding method was used to classify nuclei image into three classes: bright nuclei, dark nuclei, and background, based on the background correction algorithm.
following this, a method for fining local image intensity maxima using the gaussian filtering and gradient vector field was developed to detect the nuclei.
a statistical model based splitting method was proposed to reduce the under segmentation problem.
the experimental results show that  <dig> % nuclei are segmented correctly using the proposed image analysis protocol.
its application on our experimental data sets further indicates that the human h <dig> neuroglioma cells have a concentration-dependent toxic response to the insult of cuo nanoparticles.

 a precise determination of cell death model is essential for biomedical researches as cell death pathways are intimately associated with normal physiology and disease-related pathogenesis. the widely used colormetric cytotoxicity assays such as lactate dehydrogenase release, mtt /mts based assays, etc., can only evaluate the viability of cell ensemble. thus there is a strong demand for sensitive, quantitative, reliable and automated methods for the accurate assessment of cellular proliferation status with high contents of cellular information. as a modern drug discovery tool, high content screening using automated fluorescence microscope is becoming an important and widely used research tool to assist researchers understanding complex cellular processes in disease pathogenesis, drug target validation and drug lead identification. using the hcs technology, abundant spatial and temporal morphologic information can be extracted from the cellular images, and the information can be used to determine whether a potential drug affects the functions of proteins or genes involved in a disease process. however, it has been a challenge to perform quantitative analysis of the complex cellular images, and this significantly restricts the potential of hcs in drug discovery. thus, the availability of fully automated cellular image analysis systems is critical to the success of hcs. the fluorescent images of human h neuroglioma cells exposed to different concentrations of cuo nanoparticles were collected by a high content fluorescence microscope – in cell analyzer using these cellular images, investigators can investigate the influences of the cuo nanoparticles to the cell viability and determine cell death mode by analyzing the percentage of dead/live cells. however, there are thousands of cellular images generated in one experiment, and thousands of cells appear in each image. it is impractical to count and quantify the cells manually. therefore, a fully automated and robust cellular image analysis system is needed urgently. for a fully automated cellular images analysis system, the detection and segmentation of nuclei are the two essential components. the problems of nuclei segmentation originate from uneven illumination, artifacts, nuclei clustering and low intensity contrast between the nuclei and the background. as seen in figure nuclei are of different sizes, intensities and shapes, which pose a problem in segmentation. secondly, the intensity contrast of dark nuclei is very low, and the bright nuclei are found to cluster together. the problem is further aggravated due to the presence of high-intensity noises in the dark nuclei region. although some methods for fluorescent cellular image detection and segmentation have been proposed, a general purpose system that can perform the detection and segmentation tasks for all kinds of fluorescence microscopy images without any fine tuning is still not available. this has motivated us to design a novel system to serve the purpose. in some nuclei segmentation methods were proposed. they combined the intensity gradient information with the shape information to separate the clustered nuclei by using a statistical model to merge the fragments of nuclei. since the bright nuclei cluster together heavily, and the dark nuclei cannot be accurately separated from the background, these methods tend to fail because the shape information is not accurate. in addition, edge based segmentation methods will fail due to the noisy and discontinuous edges. thresholding methods cannot separate the clustered nuclei. moreover, the contours' initialization of the snake and level set methods is much more challenging work. the goal of the present work is to develop an automated cellular image analysis system for quantitative analysis of viability of h neuroglioma cells exposed to cuo nanoparticles. figure provides a flowchart of the proposed system. we first used a background correction method as a multiple adaptive thresholding technique to classify the nuclei image into three classes: bright nuclei, dark nuclei and background. then we implemented a nuclei detection method based on the gaussian filtering and gradient vector field followed by the seeded watershed based region-growing algorithm to segment the clustered nuclei. finally we proposed a statistical model based splitting method to reduce the under-segmentation problem.  materials human h neuroglioma cells purchased from the atcc were seeded into 96-well cell culture plates and cultured in dulbecco's modified eagle medium supplemented with 10% fetal bovine serum, 1% penicillin-streptomycin solution. the cells were incubated for hours under the cell culture conditions, together with cuo nanoparticles at a concentration range of – μm. then a live/dead assay kit for cell viability was applied to the cells according to the manufacturer's instruction. in brief, the cells were cultured at 37°c for min, with ethidium homodimer-, and hoechst dye in each well. high-content cellular fluorescence images were acquired using an automatic fluorescence microscope – in cell analyzer. the objective magnification is 10×. numerical aperture is, pixel width is μm and pixel height is μm for all the images taken. the size of each image is × pixels. choice of parameters validation and comparison of segmentation to evaluate the accuracy of the proposed nuclei segmentation method, we randomly picked up ten nuclei images as the testing data set. figure 3- and figure show the representative detection and segmentation results respectively. four possible segmentation errors were considered: over-segmentation, under-segmentation, false negative and false positive. the false positive rate and the false negative rate are defined as follows: fpr=#offalsenuclei#oftotalnucleiandfnr=#ofmissednuclei#oftotalnuclei mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=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@9071@ to further evaluate the effectiveness of proposed method, we compared the segmentation results of the proposed protocol with the cellprofiler, which is free available software and based on watershed method. we compared the two methods using: correctly segmented nuclei, fnr and fpr. as indicated in table the proposed method outperforms the watershed method. both the fnr and fpr values of the watershed method are much higher than that of the proposed method. the reason is that watershed algorithm missed some dark nuclei, and under-segmented the bright nuclei clusters. the high fpr values of the watershed method were caused by the fluorescent noises. we also tested the cell counting tool in imagej – itcn. this tool was developed by thomas kuo and jiyun byun. the itcn uses laplacian of gaussian filtering as the nuclei detector. there is a parameter, i.e. diameter of a cell, in itcn, and we tested the tool with two different diameter values of the cell: micron and micron. the results are shown in figure 5- and 5- respectively. as seen in figure 5-, there are too many noises and over-detection because we used the smaller diameter. in figure 5-, there are some bright nuclei are under-segmented when we use larger diameter. in addition, many noisy points still exist. in conclusion, the itcn results are very sensitive to the initial values of the diameter of cell and the intensity noises. it works well if the cells or nuclei have similar size, intensity and round shape. however, in our study, the two kinds of nuclei have different size, intensity and shape. the bright nuclei clustered together, and the contrast of the dark nuclei is very low. these difficulties limit the performance of itcn. the detection result of the proposed method is provided in figure 5-. analysis of cell death induced by cuo nanoparticles in this study, we applied the proposed system to analyze the toxic response of the human h cells exposed to the cuo nanoparticles. we treated the human h neuroglioma cells with five different concentrations of cuo nanoparticles:,, and μm for hours. we used two fluorescence dyes, hoechst and ethidium homodimer- for staining total cells and dead cells, respectively, following the vendor suggested protocols. the blue fluorescent hoechst dye are cell permeable nucleic acid stains that have multiple applications, including determination of cell number. the fluorescence of the dye is very sensitive to dna conformation in both live and dead cells. ethd- however, enters cells only with damaged membranes and undergoes a 40-fold enhancement of fluorescence upon binding to nucleic acids, thereby producing a bright red fluorescence in dead cells. thus, entry of ethd- into living cells with intact plasma membrane is prohibited. the dead and total cells in each well were stained for hours by ethd- and hoechst dye, respectively. we acquired the cellular images using the in cell analyzer an automatic fluorescence microscope after the staining step. the toxic effects of the cuo nanopaticles upon the human h neuroglioma cells can be quantified by determining the percentages of dead/total cells treated with different concentrations of cuo nanoparticles. thus we only need to count the number of total and dead cells using our automatic image analysis system. figure 6-, and 6- give the mean and standard deviation of total cells, dead cells, and percentage of dead/total cells in the untreated and the five different concentrations of cuo nanoparticles treated wells. to determine the statistical significance, we also performed the student's t-tests for cell death ratios between the cuo nanoparticle treated and untreated human h neuroglioma cells. table gives the p-values of these t-tests. from figure and table we conclude that the increases of cell death ratio is statistically significant in h cells treated by > μm of cuo nanoparticles. the computational results are consistent with our biological finding that human h neuroglioma cells have a dose-dependent toxic response to the insult of cuo nanoparticles. interestingly, benson jm., et al. reported that in vitro cytotoxicity of the ni-cuo compounds to pulmonary alveolar macrophages is correlated to their increased cu content and decreased ni content. as such, our results, which are in good agreement with benson et al., indicate cuo nanoparticles are cytotoxic to human brain cells.  herein we present a fully automated cellular image analysis system for quantitative analysis of the viability of human h neuroglioma cells exposed to cuo nanoparticles with different concentrations. a multiple thresholding method was used to classify nuclei image into three classes: bright nuclei, dark nuclei, and background, based on the background correction algorithm. following this, a method for fining local image intensity maxima using the gaussian filtering and gradient vector field was developed to detect the nuclei. a statistical model based splitting method was proposed to reduce the under segmentation problem. the experimental results show that % nuclei are segmented correctly using the proposed image analysis protocol. its application on our experimental data sets further indicates that the human h neuroglioma cells have a concentration-dependent toxic response to the insult of cuo nanoparticles. 
computational results indicate that % nuclei can be detected and segmented correctly by the proposed image analysis system. the computational results confirmed our biological finding that human h neuroglioma cells had a dose-dependent toxic response to the insult of cuo nanoparticles. a multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. during the development of our image analysis methodology, we have achieved the followings: the gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; a novel local intensity maxima detection method based on the gradient vector field has been established; and a statistical model based splitting method was proposed to overcome the under segmentation problem. the cellular images of h neuroglioma cells exposed to different concentrations of cuo nanoparticles were sampled using in cell analyzer a fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. the proposed automated image analysis system can effectively segment the images of human h neuroglioma cells exposed to cuo nanoparticles. high content screening -based image analysis is becoming an important and widely used research tool. capitalizing this technology, ample cellular information can be extracted from the high content cellular images. in this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human h neuroglioma cells exposed to metal oxide nanoparticles.
 high content screening -based image analysis is becoming an important and widely used research tool. capitalizing this technology, ample cellular information can be extracted from the high content cellular images. in this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human h neuroglioma cells exposed to metal oxide nanoparticles. this system has been proved to be an essential tool in our study.  the cellular images of h neuroglioma cells exposed to different concentrations of cuo nanoparticles were sampled using in cell analyzer a fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. a multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. during the development of our image analysis methodology, we have achieved the followings: the gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; a novel local intensity maxima detection method based on the gradient vector field has been established; and a statistical model based splitting method was proposed to overcome the under segmentation problem. computational results indicate that % nuclei can be detected and segmented correctly by the proposed image analysis system.  the proposed automated image analysis system can effectively segment the images of human h neuroglioma cells exposed to cuo nanoparticles. the computational results confirmed our biological finding that human h neuroglioma cells had a dose-dependent toxic response to the insult of cuo nanoparticles. 