stem cell therapies are promising treatments for many diseases , and preclinical trials in ischemic heart disease show enhanced recovery of function [ 1 , 2 ] .
while mechanisms for homing are being defined , exogenous stem cell therapies are being implemented in clinical trials , despite a limited understanding of mechanisms . due to limitations of current methods such as histology , qpcr , and in vivo imaging
, one can not reliably determine efficiency and dose response of exogenous stem cell therapy . in order to determine dose response of stem cell treatments
, imaging must be performed with single cell sensitivity and resolution over large regions of an entire specimen .
the utility of imaging small numbers of cells over a large specimen is not limited to stem cell research , but is needed in immune response , cancer , and many other fields of research .
cryo - imaging , as defined below , provides single cell resolution and sensitivity over an entire specimen , which is not possible with in vivo , small animal imaging systems such as ct , mri , pet , spect , whole animal fluorescence , or bioluminescence .
optical imaging modalities such as intravital imaging   do not offer the field of view ( fov ) or depth of field of cryo - imaging . by imaging with high resolution and sensitivity , it is possible to identify fluorescently - labeled single cells or cell clusters within a mouse .
once cells are identified , cell locations can be mapped relative to the tissue anatomy in the high contrast , 3d cryo - image color volumes .
cryo - imaging consists of a modified , bright - field / fluorescence microscope , a robotic imaging system positioner , a customized , whole mouse motorized cryomicrotome , control system , and analysis / visualization software . by alternately sectioning and imaging the specimen , the system acquires brightfield color and fluorescent image volumes , providing micron - scale resolution , detailed views of anatomy , and cells labeled with fluorescent reporter genes or exogenous fluorophores [ 58 ] .  
once images have been collected of an entire mouse at sufficient resolution to detect individual fluorescently labeled cells , methods must be developed to detect and segment fluorescently labeled cell clusters and quantify the number of cells per cluster .
a cryo - image volume consists of tens of thousands of images and over 80  gb , necessitating the need for an automated approach .
to put our task in perspective , finding a one - voxel cell in a whole mouse cryo - image volume is equivalent to finding one voxel in 23  10 voxels , or about one needle in 1900 haystacks .
typical cellular quantification algorithms range in sophistication from simple manual dot counting , to automated deformable models   and neural network - based segmentation .
the latter methods require images of sufficient resolution , such that the cell boundary is distinguishable . in order to limit the number of images required to image an entire mouse , cryo - imaging
is performed with a 15.6  m pixel size , as compared to the smallest available pixel size of 1.2  m . at this resolution , cells are approximately the size of a single pixel , making it impossible to resolve cell boundaries in large clusters of cells . for this reason ,
it is necessary to utilize quantification algorithms that do not rely on high resolution to perform quantification .
there are reports of model - based segmentation and quantification algorithms when the size and shape of cells varies greatly and the boundary between cells is indistinguishable due to low resolution or contrast .
the cell model is used to incorporate prior information about cell size , shape , and intensity , as well as the transfer function of the imaging system .
cells are approximately the size of a pixel , which appears as a single bright point .
this point is then blurred during imaging due to pixilation , the transfer function of the imaging system , and light scattering by the tissue . by modeling a cell as a gaussian ,
we are accounting for the spherical shape of the cells , as well as blurring due to the imaging process .
currently , model - based algorithms have been used for segmentation of cells , cell clusters , and subcellular structures [ 1113 ] .
possible cells or cell clusters are identified and fit to a range of gaussians with varying  's [ 11 , 13 ] or an optimal  was estimated through optimization .
noordmans and smeulders attempted to isolate overlapping spots by subtracting a preliminary fit of one of the spots from the image of the overlapping spots .
two spots that overlapped to a large extent were erroneously counted as single cells and estimates of the individual spot sizes were inaccurate . to our knowledge , no attempt has been reported in the literature to separate more than two cells using model - based algorithms . in this paper , we develop and evaluate a model - based algorithm for enumerating 1000 's of fluorescently labeled cells in a cryo - image volume .
knowledge - based image processing is performed to remove false positives ( fp 's ) , where rules are created based on known properties of cell clusters . in experiments , we image homogeneous phantoms containing fluorescent microspheres or quantum dot - labeled mouse mesenchymal stem cells msc 's .
results are validated against high - resolution imaging and against human interpretation . in sections that follow ,
we develop the algorithms , describe the software and parameter estimation , validate our method , and process cryo - image volumes .
we divide cell processing into three major steps : preprocessing , detection / segmentation of cell clusters , and estimation of the number of cells per cluster .
later , we describe modifications required for cells labeled with fluorescent proteins . we tile and align cryo - images using semiautomated gray - scale registration algorithms previously described .
briefly , to account for attenuation and scatter in tissue , we blur and attenuate the next image in the stack and subtract it from the current image . for optimal processing with high microscope resolution and thin sections , we estimate parameters from images using an optimization algorithm .
careful fine tuning of tissue - specific parameters is less critical at lower microscope resolution and thicker sections .
for example , in our application experiment with 40  m sections , quantum dot - labeled stem cells appear as a single pixel and are visible over at most 2 - 3 images at greatly diminished intensity . in such experiments ,
the subsurface fluorescence intensity is much lower , 1520% of the intensity of fluorescent cells at the surface , and any subsurface fluorescence not removed by next - image processing will be too dim to be confused as a fluorescently labeled cell .
next - image processing greatly reduces any subsurface fluorescence from labeled cells as well as general background autofluorescence .  
we found that morphological reconstruction gave better results than morphological grayscale opening , as it reduced artifacts , such as  blotching ,  where regions of pixels have unnaturally uniform intensity   and corrected the background intensity under the clusters .
morphological reconstruction takes as an input a marker image and the original image , sometimes called a mask image in the literature . in order to determine the autofluorescent background ,
a marker image is chosen with fluorescent clusters removed . this marker image is repeatedly dilated until the contour of the marker image fits under the original image .
this results in a reconstructed image that contains only the background autofluorescence . in our application , we process each color channel separately , with the original 2d color fluorescent image as the original image . in order to determine the autofluorescent background ,
we determine the marker image by performing morphological grayscale erosion on the original image , removing all possible cell clusters . to make certain that all fluorescent clusters are removed during erosion
, the structuring element must be larger than all fluorescent clusters present in the image .
erosion was performed with a disk structuring element of radius 10 pixels , which was experimentally determined to remove all clusters .
following morphological reconstruction , the reconstructed image contains only background ; all areas of local maxima ( cell clusters and fp 's ) are removed . by subtracting this reconstructed background image from the original image , we are left with an image containing fluorescent clusters as well as some fp 's . for cells containing red quantum dots , we compute the ratio of the red to green fluorescence digital image intensities , ir / ig .
fluorescence images excited in the range of 390465  nm contain predominately green autofluorescence , giving ir / ig  0.4 .
cells labeled with red ( 625  nm ) quantum dots are much redder than green , giving ir / ig  8.0 . by thresholding the ratio image ir / ig with a threshold value tr / g ( 1 ) , we are able to remove nearly all effects of autofluorescence .
the threshold tr / g is interactively determined by a user to conservatively include all cells and possibly some fp 's , which will be later pruned away .
the thresholded ratio image is multiplied by ir ( 1 ) , the red channel intensity of the original image , creating a grayscale image , as below , where binary is the creation of a binary image using the enclosed operation . 

hysteresis thresholding first segments igray by a high threshold ( th ) , leaving only the brightest pixels in each cluster of cells
. a low threshold ( tl ) is then applied , which includes all pixels in the fluorescent clusters , as well as fp 's due to autofluorescence .
pixels selected by the low threshold are retained if they are connected to a pixel selected by the high threshold .
the user selects a high threshold , such that all clusters contain at least one thresholded pixel .
for this reason , the high threshold is typically determined from an image of cells in culture .
the high threshold is determined based on the maximum intensity of each cell cluster in culture , described below in  ( 2 ) 
 ( 2)th=.6min[n : cmax(n ) ] , 
 where cmax(n ) is the maximum intensity of a cell cluster n in culture .
the high threshold can be increased to reduce the number of false positives . however , this is often avoided to ensure that all cell clusters are segmented . the low threshold is typically selected to exclude the autofluorescent background .
the low threshold is determined based on the mean and standard deviation of the background as described below in ( 3 ) 
 ( 3)tl=img+cimg , 
 where img is the mean value of the digital image intensity values , img is the standard deviation , and c is a constant . from experimental analysis ,
a value of c = 1.4 or greater was found to reduce the inclusion of autofluorescence , while including the entirety of all cell clusters .
a user may elect to increase the low threshold to reduce the inclusion of dim autofluorescent pixels .
connected component analysis ( cca ) is performed to uniquely label each segmented fluorescent cluster . from each uniquely labeled cluster ,
cluster features consisting of volume , integrated intensity , bounding box , integrated intensity to volume ratio , and center of mass are determined .
these features are later used to reject fp clusters and to initiate model - based analysis . in general , the above thresholds are set to  over call  fluorescent clusters , ensuring that all cells are identified .
since cells are well separated prior to injection and since we are focusing on stem cell studies over relatively short - time periods ( 1 hour to 6 days ) with minimal cell division , cell clusters are typically small with many one and two cell clusters and very few with as many as 15 cells in a cluster . the vast majority of fps due to autofluorescence can be removed by setting a maximum cluster size .
very large clusters due to bright autofluorescent regions of the intestine , bone , and stomach are easily removed in this way .
small autofluorescent structures ( e.g. , remnants of the intestine and small bones ) may not be removed by the maximum size rule .
however , we have determined that by setting a minimum and maximum intensity - to - volume ratio , we can easily remove such regions . in this way , it is possible to remove small structures that are too bright or too dim to be labeled cells .
rules are ( 1 ) fluorescent clusters with a volume ( cv ) larger than a user defined threshold ( tv ) are classified as fp 's ( cv > tv ) ; ( 2 ) fluorescent clusters with an integrated intensity ( ci ) to volume ( cv ) ratio outside of a specified range ( tr , l : low - ratio threshold , tr , h : high - ratio threshold ) are classified as fp 's ( ci / cv < tr , l , ci / cv > tr , h ) .
additional fp removal is performed following model - based quantification to further reduce fp 's .
the first step in our model - based analysis is to determine average parameters for a single cell .
histograms of cluster features ( volumes and integrated intensities ) give qualitative information about the number of cells per cluster .
the largest peaks in the histograms correspond to single cells . because the average stem cell diameter is 1015  m , 3 - 4 times smaller than the slice thickness ( 40  m )
only fluorescent clusters contained within a single 2d cryo - image are assumed to be single cells .
each single cell is fit to a gaussian with free parameters ;  , integrated intensity ( it ) , and the center of the gaussian ( xc and yc ) .
parameters are estimated using a least squares error objective function and levenberg - marquardt or nelder - mead nonlinear optimizer [ 20 , 21 ] .
model - based analysis is performed to estimate the number of cells per cluster for all detected clusters in the volume . for
any given cluster , we first estimate the number of cells in a cluster ( ni ) as shown in  ( 4 ) : 
 ( 4)ni=ciit , 
 where ci is the integrated intensity of cluster i and it is the total intensity of the model single cell . a range of integer numbers of cells around this estimate is used to determine the range for fitting the cluster .
the range of number of cells tested is given by  ( 5 ) : 
 ( 5)nimax(2,ni.30 ) . 

for each integer number of cells tested , n , the optimal placement of n model cells that most closely resembles the 3d image of the cluster is determined . for each model cell there are free parameters xc , yc , and zc , where xc , yc , and zc are real valued parameters setting the center of the cell and xc and yc set the location of a 2d gaussian . recall that we use thick sections ( 40  m ) as compared to the resolution in the xy - plane ( 8.815.6  m ) and that there is a potential of sectioning a single cell .
the model cell is set at a  height  zc . to account for sectioned cells
the division of the cell between adjoining slices is used to divide the total intensity ( it ) between the slices .
the center of two cells may not be closer than dmin , the approximate diameter of a single cell .
optimal placement of the n model cells is determined through minimization of an objective function using the nonlinear optimizer .
the objective function , fobj , is least square error , as given below 
 ( 6)fobj=m(igraynigauss)2 , 
 where igray is the grayscale fluorescent image ; igauss is a single model cell with input parameters xc , yc , zc , it , and  ; m is the number of pixels in the cluster ; n is the total number of model cells .
the number of cells n that best matches the 3d cluster image , as determined through least square error , is designated as the number of cells in the cluster
. please note that ( 4 ) is simply used to obtain an initial guess and that ( 6 ) is the working equation for determining the actual number of cells in a cluster .
optimal algorithm parameters will depend at least upon spectral characteristics of the fluorophore , cell size , and intensity . at this time , parameters are interactively selected by a user .
we have found that similar experiments can be analyzed using the same parameters . in our interface , a series of 2d images from a selected cryo - image volume are displayed for the user to interact with and determine the threshold values by examining the affect of changing tr / g , th , and tl on the number of clusters and fp 's included , as well as which pixels are included within a cluster . as described above , a threshold
tr / g is required to reduce the color fluorescent image volume to the grayscale image volume igray .
. initial guesses for tr / g , th , and tl are made by the user based on images of cells in the dish as described above , prior experiments , or observed cell intensities in the displayed images .
initial guesses are made low to include all cells and fp 's . using these original threshold values ,
the original image is processed to create the hysteresis thresholded image ihyst . in separate windows ,
zoomed in views of possible cell locations of the original color cryo - image are displayed along with statistical measurements of the object and the necessary tr / g , th , and tl to include or reject the object . due to the brightness of labeled cells , the repetitive features of autofluorescence in different tissues , and the known tissue location of the object , a user is able to confirm or reject that a given object is a cell .
user predictions can be compared to multispectral images of the same location to confirm that a given object is indeed a labeled cell .
the case whole mouse cryo - imaging test bed system has been previously described [ 4 , 15 ] .
briefly , it consists of a modified large section cryo - microtome , xyz robotic positioner carrying an imaging system which consists of a stereo microscope , low - light digital camera , and brightfield and fluorescent light sources .
the cryo - imaging system is controlled by a control computer running labview ( national instruments , austin , tx ) .
the stereo microscope uses multiple objectives ( 0.036  na and 0.11  na ) and zoom settings ( 790x ) , and the fov can be varied to cover an entire mouse or down to a small organ with a maximum in - plane resolution of 1.2  m . to enable very high - resolution imaging over a large fov , the xyz robotic positioner moves the imaging system over the entire specimen , creating a micronscale - tiled image acquisition .
once collected , images were automatically aligned to correct for small ( micron - scale ) misalignments using registration software and corrected by hand where necessary .
preprocessing and cell quantification software were written in matlab ( mathworks , natick , ma ) .
processing was done on imaging workstations having with as much as 32  gb ram and intel xeon 8-core 3.00  ghz processor running windows xp 64 bit . to develop the quantification algorithm and to test its accuracy , two phantoms were created .
first , we embedded 15  m diameter green fluorescent microspheres ( fluospheres , invitrogen , carlsbad , ca ) within optimal cutting temperature compound ( oct ) ( tissue tek , ted pella , inc .
the intensity and size of fluorescent microspheres varies by less than 5% and provides an ideal alternative to live cells . to further validate our method , mouse mesenchymal stem cells ( msc 's )
were labeled with quantum dots ( qtracker 625 cell labeling kit , invitrogen , carlsbad , ca ) and embedded within oct .
microspheres were snap frozen in liquid nitrogen and cryo - imaged with a section thickness of 40  m . phantoms were imaged at normal operating resolution ( 7x , 15.6  m pixels ) and high resolution ( 63x , 1.6  m pixels for microspheres and 20x , 5.4  m pixels for cells ) . at high resolution , the number of microspheres / cells per cluster was visually apparent , while the number of microspheres / cells per cluster was not distinguishable at the lower resolution .
the computer - estimated number of microspheres / cells at the normal operating resolution was compared to the number counted by an expert reader using high - resolution images .
the expert reader was not the algorithm developer . to determine any variability in the manual analysis , the research team spot - checked results of the expert reader . in all cases
, consensus determined that the reader unequivocally determined counts per cluster using the high - resolution images . since intraobserver variability in manual reading would be negligible , we deemed it unnecessary to assess variability of our  gold standard  using multiple readers .
all animal protocols were approved by the animal research committee and all animals were housed in the aaalac animal facility of the cleveland clinic .
all mice used in this study were c57bl/6j male mice obtained from the jackson laboratory ( bar harbor , me ) at 46 weeks of age .
briefly , animals were endotracheal intubated and ventilated with room air at 100 breaths per minute using a rodent ventilator ( harvard apparatus ) .
sternotomy was performed and the proximal lad was identified using a surgical microscope ( m500 , leica microsystems , bannockburn , il , usa ) after retraction of the left atrium and ligated with 70 prolene .
after lad ligation , the animals received 100  l of suspension of 1  10 mouse mscs through a tail vein injection .
six - week - old c57bl/6j mice were sacrificed and the hind limbs were removed .
their femurs were carefully cleaned of adherent soft tissue and bone marrow was flushed into a 50  ml falcon with flush medium ( alpha medium with 2  g / l nahco3 , 10% horse serum , 10% fbs , 1% l - glutamine , 1% penicillin - streptomycin ) .
the cells were filtered through a 70  m nylon mesh filter followed by centrifugation for 5  min at 260  g and washed with pbs .
nonadherent cells were removed by replacing the medium after 24  h. the cells were cultured in a monolayer at 37c and 5% co2 and medium was refed every 3 - 4 days .
when cells reached 80% confluence , adherent cells were detached after incubation with 0.05% trypsin and 2  mm edta ( invitrogen ) for 5  min .
cells were depleted of cd45 , cd34 cells by negative selection using 10  l per 10 cells of each of the after primary pe - conjugated antibodies : mouse anti - rat cd45 ( bd biosciences , san diego , ca , usa ) and mouse anti - cd34 antibodies ( santa cruz biotechnology , inc .
pe - positive cells were negatively selected using the easysep pe selection kit according to the manufacturer 's instructions ( stem cell technologies , vancouver , b.c . ,
cells were labeled with qtracker cell labeling kit ( invitrogen ) according to the manufacturer 's instructions .
100,000 labeled mscs suspended in 100  ml of pbs or 100  ml of pbs alone were infused via tail vein injection 24  h after myocardial infarction .
the organs were then separately embedded in optimal cutting temperature compound ( oct ) ( tissue tek , ted pella , inc . ,
the entire mould is snap - frozen in liquid nitrogen for five minutes to reduce ice crystal formation . following this ,
the mould assembly was removed from the liquid nitrogen bath and placed inside the cryomicrotome chamber to equalize the specimen temperature to that of the cryomicrotome . after three hours , the mould was removed and the frozen specimen mounted on the microtome stage and the slice thickness was set to 40  m .
heart specimens were prepared as described and imaged at 12.5x magnification , with a pixel size of 8.8  m .
lung specimens were prepared as described and imaged at 10x magnification , with a pixel size of 11  m .
detection of quantum dot - labeled cell clusters was compared to detection by a user in an infracted mouse heart .
it became quickly apparent that it was very difficult to visually detect single cell clusters that were often 1 - 2 pixels on a video screen .
the operator detected 80 clusters in 50 images , whereas the algorithm detected many more ( 250 ) clusters .
the operator did not detect any cells that the algorithm missed , which would have indicated a false negative ( fn ) .
all cells detected by the algorithm were displayed and verified as cells by the user , giving no false positive ( fp ) detections .
hence , as best as we could determine , the algorithm had no fns or fps in these 50 images of heart tissue .
tissues having more autofluorescence ( e.g. , bone and gastrointestinal tract ) could have detection errors .
we characterized microspheres and cell images in the oct phantom . at the normal operating resolution ,
visual appearances of microspheres can change dramatically , depending upon the position of the microsphere with respect to the pixel grid ( figure 1 ) .
we are confident that all fluorescent clusters in figure 1 are due to a single microsphere because the integrated intensity is very nearly equal ( within 5% ) .
histograms of the integrated intensity of clusters showed a clear separation between single - microsphere / cell clusters and multiple - microsphere / cell clusters for both cells ( figure 2(a ) ) and microspheres ( figure 2(b ) ) .
note that a clear , smaller peak corresponds to an intensity expected for two cells / microspheres . to test the ability of our software to correctly estimate the number of cells / microspheres in a cluster at normal operating resolution , we compared results to high - resolution images ( figures 3 and 4 ) .
individual microspheres and cells were clearly visible in high - resolution ( 1.6  m pixels for microspheres and 5.4  m pixels for cells ) images ( figure 3(a ) and figure 4(a ) , resp . ) .
however , at normal operating resolution , images of the same microspheres or cells are joined into single connected clusters ( figures 3(b ) and 4(b ) , resp . ) .
the algorithm correctly estimated the number of cells / microspheres in the normal operating - resolution images ( figures 3(d ) and 4(c ) , resp . ) .
for example , in figure 5 , we modified the algorithm to test 200 initial cell configurations to fit the detected cluster . in each case , the nonlinear optimization algorithm rapidly converged in 5070 iterations , many fewer than the maximum ( 10,000 ) .
results for 3 , 4 , and 5 cells are shown in figure 5 . clearly , 4 is the best result both visually and quantitatively from the objective function .
almost all cell / microsphere positions changed < 0.1 pixels with different initializations , and in the case of the best number of cells , results typically changed < 0.001 pixels .
we very rarely ( 0.1% ) reached the maximum number of iterations , due to oscillatory behavior of the algorithm . in tests , we added a line in the code to determine the number of times that the objective function differed by less than 20% , a threshold giving similar visual results . in a run over 656 fluorescent clusters
, we determined that only 1.2% of clusters gave such an  ambiguous  result .
we tested the quantification algorithm at the operating resolution against an expert using high - resolution images over a large numbers of microsphere ( 229 ) and cell ( 218 ) clusters .
model - based processing was first performed on a fluorescent microsphere phantom to evaluate the algorithm in the best case scenario with nearly identical fluorescent sources .
a small percentage of clusters ( 5% ) were visually ambiguous even at this high resolution , and these were excluded from our analysis . in figure 6 , we present a contingency table comparing the algorithm versus an expert reader .
perfect agreement consists of a diagonal in a contingency table . in all cases but one , the algorithm was within 1 microspheres of the actual number present in a cluster ( figure 6 ) .
model - based processing estimated the total number of microspheres present to be 497 , compared to 499 counted by an expert , an error of only 0.4% .
fleiss ' kappa score for the contingency table was 0.78  0.04 . when fleiss ' kappa is 1 , there is perfect agreement between the expert and the algorithm , with a value of zero corresponding to a random guess at the number of cells per cluster .
sensitivity varied depending on the number of microspheres per cluster , reaching a maximum at n = 1 of 0.85 and a minimum of 0.75 at n = 3 .
model - based processing was also performed on cells in oct . in all cases but two ,
the algorithm was within 1 cells of the actual number present in a cluster ( figure 7 ) .
the total number of cells counted by the expert was 393 compared to 386 by the algorithm , giving an error rate of only 1.7% .
the sensitivity varied depending on the number of microspheres per cluster , reaching a maximum at n = 1 of 0.82 and a minimum of 0.625 at n = 5 .
as an example application of our quantitative stem cell techniques , we show results from a study of cardiac stem cell therapy with mscs .
following surgery to induce myocardial infarction in c57bl/6j male mice , we injected red quantum dot - labeled mscs via the tail vein . at 24 hours postadministration , we reliably imaged mscs .
autofluorescence was minor , with a small amount of green autofluorescence in the infarct zone and relatively no autofluorescence elsewhere in the heart .
the reduction of blood flow to the left ventricle caused by lda ligation resulted in increased concentration of mscs on the right side of the heart ( figure 8) .
the relative distribution of mscs in the heart is shown relative to the infarct location ( figure 8) .
cells detected by the algorithm were displayed , verified by a user and found to contain only quantum dot - labeled cells . in total ,
554 clusters were detected and 748 mscs were estimated to be in the heart by model - based processing .
we determined that 74% of clusters contained single cells , with no cluster containing more than 5 cells .
we have developed a useful , well - validated method for detecting fluorescent clusters and determining the number of cells per cluster in cryo - image data .
the numbers of cells or microspheres in a given cluster was verified through visual inspection , giving an excellent  gold standard  with which to compare . in the case of microspheres , in only one instance was the algorithm off by more than one microsphere .
there was no substantive bias towards high or low numbers of microspheres within a cluster , and 497 and 499 microspheres were counted by the algorithm and manual inspection , respectively , giving a 99.6% accurate microsphere count .
with quantum dot - labeled cells , there were more errors , but results were nevertheless quite impressive .
cell counts were 386 and 393 , for algorithm and manual , respectively , giving a 98.3% accurate cell count .
remarkably , the algorithm was within 1 cells of the actual number of cells in a cluster all but two times .
the small decrease in accuracy with cells is most likely due to variability in the size and intensity of the cells .
cells were found to have a measured standard deviation of 15% from the mean cell integrated intensity , corresponding to the 15% window used for facs sorting . to achieve accuracy in cell and microsphere counting , drift and fluctuations in exciting light
a major innovation is the use of model - based processing to estimate the number of cells per cluster .
our approach fits the spatial intensity pattern of a cluster with a collection of cell models having known spatial extent and integrated intensity .
pixelation of a cluster results in surrounding pixels of varying intensity , dependent upon the percentage of the fluorescent cluster in that pixel ( figure 1 ) .
a pixel containing a small fragment of a cluster may not be included by the low - threshold tl , resulting in a decrease in the recorded integrated intensity and volume of the cluster .
this error in the recorded integrated intensity and volume affects the corresponding cluster histograms , resulting in an increased spread around the single - microsphere / cell cluster peak ( figure 2 ) . because the initial estimate for the number of cells / microspheres in a cluster is dependent upon the cluster 's integrated intensity ( 4 )
however , this will be corrected with the model - based estimate which uses a region two pixels larger in x and y than the thresholded component of the cluster .
alternative cell segmentation approaches based on shape would necessarily require much higher resolution images impractical for our application .
ligation of the lda reduced blood flow to the front and bottom of the left ventricle , resulting in no fluorescently - labeled msc 's detected in the infarct zone ( area surrounding the suture ) in the heart ( figure 8) .
mscs were found preferentially located in the myocardium surrounding the right ventricle ( figures 8(a ) and 8(c ) ) .
all cell clusters found in the heart passed through the capillaries in the lung prior to entering the heart . for this reason
while there were multiple - cell clusters present , clusters contained predominately single cells ( 74% ) , perhaps a result of filtering in the passage through the lung . despite relatively small numbers of cells getting through the lung to the heart , there is evidence of cardiac recovery due to msc therapy , probably due to a paracrine effect .
cryo - imaging and quantitative stem cell analysis should greatly aid interpretation of future studies .
in conclusion , the accuracy of the model - based quantification software allows us to count cells over large regions of cryo - image volumes .
the algorithms can be reliably applied on bright cells in homogeneous tissue . however , in highly autofluorescent regions ( e.g. , gastrointestinal tract and bones ) , very bright cells and/or improved autofluorescence rejection in image acquisition are required to ensure automated detection of all cells .
cryo - imaging allows us to capture high - resolution images with high sensitivity over large areas , while simultaneously imaging the tissue with color brightfield .
preliminary experiments demonstrate the effectiveness of cryo - imaging and model - based processing to detect , segment , and quantify stem cells in cell therapy .
we believe that this new methodology will be useful in a myriad of studies on cell source ( msc , adipose stem cell , cord blood , and many more ) , cell treatments , dosing regimens , delivery method , and so forth , in a quantitative manner heretofore unavailable .
 we developed and evaluated an algorithm for enumerating fluorescently labeled cells ( e.g. , stem and cancer cells ) in mouse - sized , microscopic - resolution , cryo - image volumes . 
 fluorescent cell clusters were detected , segmented , and then fit with a model which incorporated a priori information about cell size , shape , and intensity . 
 the robust algorithm performed well in phantom and tissue imaging tests , including accurate ( < 2% error ) counting of cells in mouse . 
 preliminary experiments demonstrate that cryo - imaging and software can uniquely analyze delivery , homing to an organ and tissue distribution of stem cell therapeutics . 
1. Introduction
2. Algorithm
3. Experimental Methods
4. Results
5. Discussion