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A Semiautomatic Cell Counting Tool for Quantitative Imaging of Tissue Engineering Scaffolds

A Semiautomatic Cell Counting Tool for Quantitative Imaging of Tissue Engineering Scaffolds
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  A Semiautomatic Cell Counting Tool for QuantitativeImaging of Tissue Engineering Scaffolds Sebastian De Boodt, PhD, 1,2 Ahmad Poursaberi, MSc, 1 Jan Schrooten, PhD, 2,3 Daniel Berckmans, PhD, 1 and Jean-Marie Aerts, PhD 1,2 Automatic image analysis algorithms are in general dedicated quantification tools used for very specific types of microscopic cell images, but are not robust enough to accurately quantify the cell number and distribution in thewide variety for fluorescence images that exist in the field of tissue engineering (TE) today, where cell–material(scaffold) interactions are being evaluated more and more. In this study, a semiautomatic algorithm was de-veloped that allows the user to manually count a limited part of a TE scaffold image, and then automaticallycounts the cells of the full image based on that calibration dataset. The algorithm was validated on images of cells on a two-dimensional (2D) titanium (Ti) substrate, in a three-dimensional (3D) Ti scaffold and in a fibrinhydrogel by comparison with manual cell counting and with an indirect cell counting using metabolic assay. Theaverage relative error between this semiautomatic and the manual approach was 3.4% for the 2D Ti substrates,5.9% for the 3D Ti scaffolds, and 14.1% for the fibrin hydrogels. Hereby a proof of concept was delivered thatcould lead to an increased use of automated cell imaging as a reliable 2D and 3D quantitative tool for both basic biological research and process control of clinical TE products. Introduction A s part of the tissue engineering  (TE) cycle, cells aregenerally seeded into a three-dimensional (3D) openporous scaffold material, and then cultured in a bioreactor tocontrol the proliferation and differentiation of the cells suchthat the construct will be able to integrate and eventuallyheal a patient’s tissue defect after implantation. 1 In the entirecascade from obtaining sufficient biological knowledge at thelaboratory scale to up-scaled and automated clinical TEproducts, noninvasive and quantitative imaging of cells in both space and time inside 3D scaffolds is regarded as animportant tool to assess cell behavior. 2,3 Cell visualizationhas evolved to a widely spread and very powerful tool dueto the development of a large array of fluorescent labels tostudy cell proliferation, viability, and gene expression. 4,5 Technological advances in optical equipment and micro-scope stage incubators make that fluorescence is no longerlimited to destructive imaging of histological sections, but can be used for repeated live cell imaging to follow uplarge amounts of samples at the same time. 6–12 Despite theenormous amount of available images generated using thepresent advanced technology, cell imaging is not being usedat its full potential because it is often only used for qualita-tive inspection. 13 Therefore, thorough image analysis caneasily increase the amount of data obtained from a singleexperiment.In TE and in biological research in general, there is a largevariety in fluorescence images that is caused by differences inthe image quality, cell density, resolution, illumination,scaffold structure and material and also the 3D geometry,which makes that not all cells are in focus. To obtain quan-tities like cell number and local cell density, cells should bedistinguished from the background, a process called seg-mentation. The image processing steps needed for this seg-mentation are very much dependent on the image featuresmentioned above, which makes it impossible to have oneuniversal cell counting algorithm for all of them. For severalspecific applications, dedicated cell detection softwarehas been developed. 14–25 In the absence of such software,commercial and open source software provide easy-to-use building blocks that allow the user to make a cell countingalgorithm that performs best for his specific application. Thelimitation of many of these automatic cell counting algo-rithms is that they are very prone to systematic under or overestimation of the cell number 26,27 and as an outsider it is verydifficult to know the reliability of the data. Polzer  et al. , forexample, thoroughly validated their automatic cell countingalgorithm with manual counting ( R 2 = 0.975), counting afternuclei staining ( R 2 = 0.997), and hemocytometer cell counting 1 Division M3-BIORES: Measure, Model & Manage Bioresponses, KU Leuven, Heverlee, Belgium. 2 Prometheus, Division of Skeletal Tissue Engineering Leuven, KU Leuven, Leuven, Belgium. 3 Department of Metallurgy and Materials Engineering, KU Leuven, Heverlee, Belgium. TISSUE ENGINEERING: Part CVolume 19, Number 9, 2013 ª  Mary Ann Liebert, Inc.DOI: 10.1089/ten.tec.2012.0486 697  ( R 2 = 0.629) and concluded their algorithm was robust,fast, and reproducible for their specific application. 16 This is,however, a laborious approach that strongly reduces theadvantage of automatic cell counting and therefore limitsits use.To use fluorescence cell imaging at its full potential, imageprocessing software needs to catch up with the evolution influorescence labeling and microscope technology. 13 Existingalgorithms are often accurate, but not robust enough to beused for a wide variety of images. The objective of this studywas to develop and validate a semiautomatic cell countingalgorithm that  allows the user to manually count a limited part of a TEscaffold image, and then automatically counts the cellsof the full image based on that calibration dataset,  provides an objective measure for the accuracy of the semiautomatic counting compared to the manualcounting of the user,  without any adaptations can be used for a variety of commonly used TE scaffold types. Materials and Methods Fluorescence images  Choice of image types.  Three image types that are rep-resentative for TE research and which have an increasingcomplexity for automatic cell counting were selected: (1) atwo-dimensional (2D) titanium (Ti) substrate, (2) a 3D Tiscaffold, and (3) a fibrin hydrogel. The 2D Ti substrates werechosen to develop the algorithms. In a 3D scaffold geometry,it is not possible to have all the cells in a scaffold in focus inone image. Therefore, any image-based cell counting methodcan be used for counting all the cells in the image, but not allcells in the scaffold. The 2D Ti substrate was included in thisstudy as an intermediate between a 2D cell culture and a 3Dscaffold. Ti was chosen over a transparent microscope glass because of its use as TE scaffold material and because it is nottransparent for fluorescent light, making it a more realisticimage for TE scaffolds. Because of the flat surface, all cells onthe substrate were visible in the images, and therefore, thecounted cell number could be correlated to the total cellnumber that was obtained by a metabolic assay. In the 3D Tiscaffold images, the cells that are in focus are smaller andhave a sharper outline, while the cells out of focus appear bigger and less sharp in the image. These 3D features in-crease the complexity for automatic cell counting comparedto the 2D substrate. Cells that are encapsulated in a hydrogelgenerally appear rounder than cells that are attached to asurface. Depending on the optical properties of the material,hydrogels often generate an uneven background in fluores-cent images, which increases the complexity further for au-tomatic cell counting compared to the 2D substrates and 3DTi scaffolds. 2D Ti substrates.  Six Ti substrates were produced withselective laser melting (SLM) and had a surface for the cellsto attach of 0.5mm · 2mm (Fig. 1, column 1). 28 Humanperiosteum-derived stem cells (hPDCs) were stained withred fluorescent CellTracker   CMDiI (Invitrogen). After ex-pansion, cells were detached from the flasks, centrifuged,and suspended in 2 m M of the CMDiI solution. Cells wereincubated for 5min at 37  C, and then for 15min at 4  C tolimit endocytosis of the dye. Cells were centrifuged and re-suspended twice in phosphate-buffered saline (PBS) (LonzaGroup Ltd.) to remove residual dye. Six substrates were dropseeded with 200 m L stained cell suspension at six differentdensities (0/2000/5000/8000/10,000/15,000 cells/cm 2 ). Thecells were incubated overnight in a growth medium [theDulbecco’s modified Eagle’s medium (DMEM) with 10%fetal bovine serum (FBS)] at 37  C, 5% CO 2 , and 95% relativehumidity (RH) to allow cell attachment to the substrate.Next, the substrates were rinsed with PBS to remove non-adherent cells. 3D Ti scaffolds.  The regular cylindrical 3D Ti scaffoldswere produced with SLM and measured 10mm in heightand 6mm in diameter (Fig. 1, column 2). 28 Five scaffoldswere prepared and seeded with hPDCs according to themethod of Impens  et al. 29 After seeding and overnight FIG. 1.  Representativepictures of the three scaffoldtypes and the fluorescentimage of the cell-seededscaffolds (rows 1 and 2: scale bars = 5mm; row 3: scale bars = 500 m m). Color imagesavailable online at 698 DE BOODT ET AL.  incubation, samples were rinsed with PBS to remove non-adherent cells. Live cells were then stained with 2 m M CalceinAM (Invitrogen). After placing the samples at 37  C for20min in the dark, the dye solution was discarded and theresidual stain was washed away with 2mL of PBS. Fibrin hydrogels.  Cells were encapsulated in fibrin seal-ant (Tisseel VH S/D) to create cell-seeded carriers for 3Dculture.Five cylindricalhydrogels (8-mm diameter and 4-mmheight) were prepared with a final fibrinogen concentrationof 33mg/mL and a thrombin concentration of 1 unit/mL(Fig. 1, column 3). The cell density was 1 · 10 6 cells/mL. Afterexpansion, cells were detached from the flasks, centrifuged,and resuspended in the thrombin component. The cell–thrombin solution was added to an equal volume of thefibrinogen component, vortexed briefly, and pipetted into acustom-made stainless steel mould. Subsequently, the mouldwas placed at 37  C for 1h. After removing the hydrogelsfrom the mould, they were rinsed with 2mL PBS and placedin 12-well plates in a 2mL medium. Wells were coated withagarose to prevent cell attachment. After rinsing with 2mL of PBS, samples were first cut in half, and then stained with2 m M Calcein AM. After placing the samples at 37  C for20min in the dark, the dye solution was discarded and theresidual stain was washed away with 2mL of PBS. Microscopic imaging  Overview images of cells seeded on/in 2D/3D Ti sub-strates and encapsulated in fibrin hydrogels were taken witha stereomicroscope (SteREO Discovery.V8; Carl Zeiss Mi-croImaging, Inc.) equipped with a cooled charge-coupleddevice camera (SPOT Insight 2MP Firewire Colour Mosaiccamera; Diagnostic Instruments, Inc.). During imaging, allsamples were submerged in PBS to prevent drying. Four top-view images (1200 · 1600 pixels), which were partiallyoverlapping the longitudinal direction, were taken from each2D Ti substrate. These four images were manually combinedusing Matlab (The Mathworks, Inc.) to make one overviewimage of the entire sample substrate and cropped to a size of 4000 · 1200 pixels. The 3D Ti scaffold images were taken inone top-view image (1200 · 1600 pixels). Two partiallyoverlapping images (1200 · 1600 pixels) were taken of eachfibrin hydrogel cross section. These two images were man-ually combined using Matlab to make one overview image of the entire cross section of each fibrin hydrogel (2000 · 1100pixels). All images were stored as 24-bit Red-Green-BlueTIFF files. Semiautomatic cell counting algorithm  The semiautomatic cell counting algorithm, can be split upin four modules as depicted in Figure 2: (1) selection of arectangular patch of the image and manual counting of thecell in the patch, (2) automatic cell counting of the patch forall image processing parameter value combinations, (3) de-termining the best parameter value combination by means of the total mismatch, and (4) automatic cell counting of thesrcinal image with the selected parameter values. Figure 2also illustrates that only module 1 requires a manual inter-action by the user and all the other modules are done auto-matically by the computer, hence, the term semiautomatic.The algorithm was made in Matlab. This paragraph explainsthe four modules subsequently. Selection of a rectangular patch of the image and manualcounting of the cell in the patch.  In the first module, theuser selects a rectangular image patch. The patches used here FIG. 2.  Schematic explanation of the semiautomatic cell counting algorithm, indicating the manual module on top and threeautomatic modules below. The images and numbers shown here are illustrative. The same algorithm was used to count thecells on the two-dimensional titanium (Ti) substrates, the three-dimensional (3D) Ti, and the fibrin hydrogel scaffold images.Color images available online at SEMIAUTOMATIC CELL COUNTING FOR TISSUE ENGINEERING 699  were 200 · 200 pixels. In this image patch, the user clicks onall the centers of what he/she considers to be cells, thusstoring the manual cell coordinates. Automatic cell counting of the patch for all image proces-sing parameter value combinations.  To determine thenumber and coordinates of the cells in the fluorescent image,the image has to be converted to a binary image of whitecells (pixel values one) against a black background (pixelvalues zero). The image processing sequence that was usedfor this can be found in Figure 3. There are three parametersin this sequence that are sensitive to the differences in cellappearance in the different images: (1) size of the isotropicsecond-order Gaussian derivative filter, (2) gray valuethreshold, and (3) use of watershed. The second-orderGaussian filter enhanced the contrast of cell-shaped pixelareas, which had a higher gray value in the center and alower gray value at the edges. Preliminary tests showed thatthis was a good filter to increase the contrast between cellsand background and between adjacent cells in the image.This allowed for the gray value threshold to be used effec-tively for separating cells from the background. In general,when two cells are touching each other, the region wherethey touch is less wide than the diameter of the cell. There-fore, the watershed was a good algorithm to separate cellsthat were in contact with each other after the gray valuethreshold. The possible values of these parameters and theirmeaning with respect to the automatic cell counting aregiven in Table 1. The second-order Gaussian derivative filterincreased the contrast of the cells, and was implementedaccording to Geusebroek  et al.  and Freeman and Adelson. 30,31 Watershed, which is available in the Image ProcessingToolbox in Matlab, is an algorithm that creates a distancemap (for each white pixel, the distance to the closest blackpixel is calculated) of a binarized image, and then splits thisdistance map where local minima are found. The algorithmperforms the image processing sequence (Fig. 3) on themanually selected image patch, for all 5610 combinations( = 11 · 255 · 2) of the values of these three parameters(Table 1). The output of this module is a library of 5610 sets of cell coordinates. Determining the best parameter value combination bymeans of the total mismatch.  The goal of this step is toselect the best parameter value combination (Table 1) forautomatic cell counting in the image patch. This is done bydetermining which set of automatically determined cell co-ordinates (output of module 2) are most similar to themanually determined cell coordinates (output of module 1).To quantify the similarity between two sets of cell coordi-nates, the total mismatch was defined as follows: if the dis-tance between an automatically and a manually detected cellis less than five pixels, than this is assumed to be the samecell and the manual–automatic cell duo is classified as amatched cell. The five-pixel distance is to take into accountthat the user will not click the cell center every time, but thisdistance will never be equal or more than five pixels. Everymanually and automatically detected cell can only be mat-ched once. After the matching step, the total mismatch(  MM total ) is calculated as follows:  MM total ¼ N   auto þ N   man N   auto þ N   man þ N  match   · 100%, (1)with  N   auto  the number of unmatched automatically detectedcells,  N   man  the number of unmatched manually detectedcells, and  N  match  the number of matched cells. The parametervalue combination that results in the lowest total mismatch isthen selected as optimal and is used to count the cells in theentire image. The total mismatch of the image patch is givenas an output of the algorithm and is an estimate of the errormade by the automatic cell counting. Automatic cell counting of the srcinal image with the se-lected parameter values.  The selected parameter valuecombination (output of module 3) is used to automati-cally count the cells in the entire image. The output of thecell counting algorithm is the cell number and the cell FIG. 3.  Example images after subsequent image processing steps:  (a)  srcinal image,  (b)  after conversion to gray value,  (c) after morphological opening with a 15 pixel diameter disk-shaped structuring element to equalize the background intensitylevel,  (d)  after isotropic second-order Gaussian derivative filter increases the contrast of cell-shaped structures,  (e)  after grayvalue threshold,  (f)  after watershed to separate touching cells,  (g)  each cell labeled with a different color, and  (h)  overlay of the cell center points on the srcinal image. Color images available online at 700 DE BOODT ET AL.  coordinates in the entire image and the total mismatch of thecell count in the image patch used for manual counting. Accuracy detection by manual cell counting  The accuracy of the semiautomatic cell counting algorithmwas quantified by comparison with manual cell counting.The standard deviation of manual cell counting was quan-tified. The same images used for semiautomatic cell countingwere also manually counted and the total mismatch wasused as an accuracy measure. Intra- and interoperator variability of manual counting. Since the manual cell counting is also prone to errors, we firstaimed at quantifying the interoperator and intraoperatorvariability of the manual cell counting on the 2D substrateimages by three experienced operators. Ten image patches(100 · 100 pixels) were taken from the 2D substrate images. Aseries of 30 patches was created using all 10 images threetimes in a randomized order. This same series was thenmanually counted by three experienced operators. The in-traoperator variability of one operator for one patch wascalculated as follows: Intraoperator variability ¼ S : D : [ N  cells ]  Average [ N  cells ]  · 100% (2)where  S.D. [ N  cells ] is the standard deviation of the three cellnumbers that were counted by the same operator on thesame patch, and  Average [ N  cells ] is the average of these threecell numbers. The interoperator variability for one patch wascalculated as follows: Interoperator variability ¼ S : D : [  Average [ N  cells ]]  Average [  Average [ N  cells ]]  · 100% (3)where  S.D. [  Average [ N  cells ]] is the standard deviation of theaverage cell counts of each operator for this patch and  Average [  Average [ N  cells ]] is the average of all the cell counts of all operators for this patch. Manual cell counting of the entire scaffolds.  All imagesused in this study were also completely manually counted byone experienced operator. A user interface was made thatenabled the person to magnify the image and click the cells. Accuracy measures.  The total mismatch (Eq. 1) wascalculated for the manual and semiautomatic cell countingfor the entire scaffolds. The total mismatch consists of an unmatched manually counted cell fraction and anunmatched semiautomatically counted cell fraction calcu-lated as follows:  MM man ¼ N   man N   auto þ N   man þ N  match   · 100% (4)  MM auto ¼ N   auto N   auto þ N   man þ N  match   · 100% (5)These are measures for the underestimation and theoverestimation of the semiautomatic cell counting algorithm,respectively. Metabolic activity on the 2D Ti substrate  One reason to use the 2D Ti substrates (instead of the3D Ti or the fibrin hydrogel scaffolds) to develop thecell counting algorithm was that all the cells on the 2Dsubstrates were visible in one image. Consequently, thesemiautomatically counted cell number could be comparedwith a biological assay that is related to the cell number. AnalamarBlue  assay was performed on the 2D Ti substratesafter microscopic imaging to indirectly quantify the cellnumber based on the metabolic activity of the cells on thesubstrates. The scaffolds were put in a 500 m L culture me-dium (the DMEM with 10% FBS) with 10% alamarBlue andwere incubated for 4h at 37  C, 5% CO 2 , and 95% RH. Ab-sorbance was measured using a 544nm excitation wave-length and a 590nm emission wavelength. A standard curvewith known cell numbers was used to indirectly estimate thecell number. The standard curve was created by seedingknown cell numbers (500, 1000, 2000, 5000, 10,000, 20,000cells) in 12-well plates (triplicate samples), which were in-cubated overnight in the growth medium at 37  C, 5% CO 2 ,and 95% RH to allow cell attachment. Next, the metabolicactivity was measured as described above. Statistics  Quantitative results are represented as mean – standarderror of the mean. Comparative studies of means were per-formed using a balanced one-way analysis of variance. Sig-nificant differences were determined with probability of 0.05and a significance level of 0.05. Results 2D Ti substrates  Manual counting.  The average intraoperator variability,which is a measure for the repeatability of manual cell Table  1.  Tuneable Parameters of Image Processing Sequence Parameter Values Influence on the counted cell number Filter size 2/2.2/2.4/ . /4 Determines the contrast between the cells and the background.Gray valuethreshold1/2/3/ . /255 When set too high, parts of the background will be labeled as cells, leading toan overestimation. When set too low, some cells will be labeled as backgroundand will not be counted, leading to an underestimation.Watershed On/Off Used to separate connected cells ( = segmentation). Can lead to over-segmentationand consequently to overestimation.Specifically at low cell density, a better result is often obtained without watershed. SEMIAUTOMATIC CELL COUNTING FOR TISSUE ENGINEERING 701
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