Time-Lapse Phase-Contrast Microscopy FibroblastAutomated Tracking

V. Paduano†¶, L. Sepe‡, C. Cantarella‡‖ C. Sansone∗, G. Paolella‡§ and M. Ceccarelli†¶† Bioinformatics CORE Lab, IRGS Istituto di Ricerche Genetiche G. Salvatore, c/o BioGeM s.c.a r.l. Ariano Irpino (AV), Italy‡ DBBM Dipartimento di Biochimica e Biotecnologie Mediche, University of Naples “Federico II”, Naples (NA), Italy

∗ Dipartimento di Informatica e Sistemistica, University of Naples “Federico II”, Naples (NA), Italy‖ SEMM European School of Molecular Medicine — Naples site, Naples (NA), Italy

§ CEINGE Biotecnologie Avanzate s.c.a r.l., Naples (NA), Italy¶ Dipartimento di Studi Biologici e Ambientali, University of Sannio, Benevento (BN), Italy

Abstract—High-throughput applications on time-lapse mi-croscopy allows us to follow the in vitro temporal and spatialevolution of cell populations; analysis on those kind of data willreveal cell motion parameters such as average speed, persistenceand directionality, important informations for many research andtherapeutic applications such as drug development or woundhealing. The large quantity of frames usually acquired containingmultiple cells require automated analysis methods dealing witha cell tracking process. In this work an either semi- or fullyautomated cell tracking system is proposed, developed to dealespecially with time-lapse phase-contrast microscopy of fibrob-lasts, which are difficult to follow cells in some cases also for ahuman expert.

Fig. 1: An example frame of phase-contrast microscopy of in vitroMEF fibroblasts.

I. INTRODUCTION

Time-lapse microscopy allows to follow through time theevolution of tissues in vitro and in vivo via a series of imagesthat may be both two-dimensional or three-dimensional (alsocalled z-stacks)1; this kind of material is often referred to as2D+time and 3D+time (or, when not ambiguous in the contest,4D). Studies supported by this kind of material usually need— and therefore include — the systematic tracking of manycells in the image series to extract characteristics or detect andmark changes in the cell behavior. In particular cell trackingis employed in many research fields and in medical solutionsdevelopment such as wound healing, embryo development,cell migration (immune system cells, cancerous cells), parasite

1E.g. phase contrast microscopy for 2D and fluorescence confocal mi-croscopy for 3D

spreading, tissue engineering, stem cell research and drugresearch and testing.In this context automated systems that can autonomously trackall the cells (or only cells of interest) in the images obtainedby high-throughput applications can significantly improve thecapability of understanding the processes at study.

Various cell tracking models and systems have been pro-posed in literature, involving different methods. The first ismanual tracking by an expert operator as in [1], which isthe most efficient solution but also the most effort demandingand time consuming, resulting inapplicable in high-throughputcontexts. Other methods include cross-correlation between thecell to be followed and the whole image [2], fluorescence-based methods that track cells or particles based on fluores-cence intensity using a cooled slow-scan CCD camera [3], invivo tracking of therapeutic cells using MRI [4] and complexsystems that track phase-contrast microscopy cell populationsusing multiple-model dynamics filters and spatiotemporal op-timization [5].Always concerning with phase-contrast microscopy images,there were in vitro tracking methods using a 2-state HiddenMarkov Model describing the random behavior of the cells[6], in vivo tracking of Escherichia coli with kernel-baseddetection [7], tracking endothelial cells using multiframe pointcorrespondence [8], seeded watershed [9] and methods usingtime as an extra spatial dimension [10].The only work of this kind dealing directly with fibroblastsis [11], which uses an iterated approach to multiassignmentusing successive one-to-one assignments of decreasing sizewith modified costs. Anyway, though developed with fibroblasttracking in mind, no testing on real data was performed.Finally, techniques dealing with z-stacks have also been pro-posed, as in [12] which use a real-time capable approach byfollowing the mass center of cellular fluorescence, or the three-dimensional tracking of zebrafish embryo nuclei using a 3DHough transform for the identification of spherical shapes [13].

We will propose a new approach for tracking phase-contrasttime-lapse microscopy cells which combines Motion Estima-tion, Motion Prediction and artificial intelligence techniques.The relative algorithm has then been developed in orderto be integrated into the MotoCell system [1], an on line

978-1-4244-6494-4/10/$26.00 ©2010 IEEE

application developed to evaluate and statistically analyze themotility of cell populations maintained in various experimentalconditions.This paper is structured as follows: in section II we will seethe materials and methods employed in this work, in sectionIII we will see the results of the algorithm running on realimage stacks (a pseudocode is given in II-G) and in sectionIV conclusions will be drawn, along with consideration onfuture improvements and developments.

II. MATERIALS AND METHODS

A. Dataset

As a test bed 2D time-lapse phase-contrast microscopyimage stacks of MEF (Mouse Embryonic Fibroblasts) havebeen used (figure 1). Being responsible for the building andmaintenance of stroma in animal tissue and playing a criticalrole in wound healing, fibroblasts are very mobile; they arealso very active, dividing quickly, especially in wound healingcases. For this reason our approach uses many a prioriinformations such as nuclear morphologies or the plateletdetachment behavior during mitosis.

1) Microscopy and image acquisition: Images of differentsamples have been acquired through the Zeiss Cell Observersystem; it involves a motorized inverted microscope (Axiovert200M), an incubator chamber for observation of living cellsand a digital camera (Axiocam H/R). The instrument isequipped with phase-contrast optics and provides an incubatorchamber to control the temperature (maintained at 37o C), theCO2 (5%) and the humidity, for the long time observation ofliving cells. In order to study the dynamic of migration, MEFcells were seeded in monolayers by plating 25000 cells/ml incomplete medium. After adhering, cells were followed for 10hours by acquiring one 16 bit digital frame (650×514 binned)every 10 minutes for 10 hours. From each movie, x/y cell co-ordinates were acquired with MotoCell [1], a semi-automatictool specifically developed to study cell migration parameterssuch as average speed, persistence and directionality.

2) Image Enhancement: The images were very low incontrast and, in some areas, the cells were difficult to distin-guish from the background or among one another. An imageenhancement step has then been employed with a 3×3 unsharpcontrast enhancement filter:

1

α + 1

[−α α− 1 −α

α− 1 α + 5 α− 1−α α− 1 −α

](1)

where α ∈ [0, 1].The image stack is firstly processed with this filter before thetracking phase. For α we used the value 0.2.

B. Cell Recognition

Cells have to be distinguished from the background andamong one another, and identified one by one. While thismay accomplished quite easily with regularly shaped cell typesin high contrast images, with fibroblast it turns to be waymore tricky because of the ever-changing shape attitude ofthose cells, which may change their shape from elongated to

expanded to circular and are in general very irregular.Two recognition methods will now be proposed.

1) Manual Recognition: The first one is manual recognitionof cells, just as happening in the original MotoCell implemen-tation. A human expert clicks on the centroid of each cell, thusmarking their position; this initialization action will then bethe only non automated step, thus making the system semi-automatic.

2) Automated Recognition: The second method is insteadfully automated: after the contrast enhancement step cellsresult to be the darkest objects in the image, which can bedetected by a thresholding algorithm. To avoid the detectionof too many points (i.e. noise and impurities) but only of largeareas (the cells) the image is first passed through a rotationallysymmetric Gaussian lowpass filter to remove impurities andsmaller objects. The filter is defined as:

h(n1, n2) =e−(n2

1+n22)

2σ2∑n1

∑n2e−(n2

1+n2

2)2σ2

(2)

where n1×n2 are the dimensions of the filter window and σis the standard deviation. We used the values of 12×12 for n1

and n2 and 4 for σ.After the filtering a thresholding is locally applied due to

the difference of intensity in many different zones of this kindof images. The thresholding detects some zones that overlapthe cells; in the process some background areas may alsofall under the threshold as well, due to noise concentrationor bigger impurities. A binary mask of the thresholded areasis extracted, and each area is labeled through a connectedcomponent algorithm, then the center of each area is identifiedand that point represent the pointer to a candidate cell.

The last step discriminates real cells from background arti-facts: for each point an SVM classifier distinguishes betweencell and non-cell objects, thus returning the pointers to cellsin the image. For further informations on the classificationprocess see section II-E.An example is shown in figure 2.

C. Motion Estimation

After the cell positions are obtained either manually orautomatically, the tracking process begins. Each time twoframes are taken into consideration: the frame Fi which isthe current frame, where all the cell positions are known, andthe frame Fi+1 which is the next frame, and on which the newcell positions are calculated. The tracking model is composedof different phases, involving distinct methods: a MotionEstimation, a Motion Prediction and a Nuclei Recognition,plus other support methods.The Motion Estimation (ME) algorithm [14] works in thisway: for each cell k in the frame Fi it extracts a portion of theimage centered in the cell position pi

k; this portion is calleda block and has dimensions b1× b2. Then, in the followingframe Fi+1 it is compared with all the other blocks centeredin the points belonging to a circular area around pi

k of radius

(a) (b)

Fig. 2: Automatic Cell Recognition on the first frame. In (a) the thresholding (green areas) and the points of possible cells (red circles);in (b) the SVM analysis on the previously found candidate points: green circles displays the cells, while red circles the non-cell objects.Squares indicate the image area used to extract the features and to classify.

(a) (b)

(c) (d)

Fig. 3: Four frames (cropped) are shown here, from (a) to (d) taken every two. The colored points identify the cells along with the colorednumber. The circles show the search area for the ME; their diameter varies along with the uncertainty of the Kalman filter. Finally, the redasterisks show the Kalman prediction for the cell position in the next frame.

rik; the most similar block indicates the point where cell k has

moved in the transition between frames Fi and Fi+1. To dothe comparison a distortion value is calculated, the block inFi+1 with the smaller distortion value is chosen and its centerbecomes the new cell position pi+1

k .The most used criteria for the distortion value are Sum ofAbsolute Differences (SAD), Sum of Squared Differences (SSD)and Sum of Absolute Transformed Differences (SATD); weused a SAD criteria with a block size of 20×20 pixels.

The time-lapse between the frames was a little bit high, thusmaking the cells “jump” from a position to another, and con-sidering that they usually changed their shape in the processan interpolation process on the frames was added to generate

fictitious frames, doubling the total number. At the end of thealgorithm the extra frames and all the values calculated onthem will be discarded, thus restoring the original stack.

1) Darkest Area Centering: In order to improve the place-ment on the cell center of the cell follower, the point pi+1

k

returned by the ME algorithm is fine-tuned by positioning it onthe darkest area in the close proximity because, as said earlier,cells are the darkest elements in the images. This algorithmsearches the pixels in a circular area of radius dsrcD centeredon pi+1

k , and for each of them calculates the average of thesurrounding circular area of radius davrgD. We chose a valueof 5 pixels for both dsrcD and davrgD.

D. Motion PredictionA Motion Prediction algorithm was then used to both en-

hance the following phase and take care of sudden changes incell direction or velocity which could lead to a follower losingits cell. The whole tracking algorithm is then supported by aKalman filtering [15] for trajectory following and prediction.The Kalman filter is a recursive estimator, which means thatonly the estimated state from the previous time step and thecurrent measurement are needed to compute the estimate forthe current state.

For each frame the filter keeps into consideration all the pastmovements of the cell k and predicts the next position pi+1

k

along with an uncertainty value ui+1k . When Fi+1 becomes the

current frame pi+1k is used as the center and the uncertainty

value as the radius of the ME search area in the next frameFi+2.The Kalman filter is not employed immediately: in fact, forthe very first points it has got not enough data to make agood prediction, thus resulting in a high uncertainty value. Soa fnoKalman value, which is the number of the first framesbefore the Kalman filter is used to modulate the ME searcharea, is specified. We set fnoKalman to 3 frames, and we useda 3 pixels default value for ri

k, which is replaced by uik when

the current frame number is higher than fnoKalman. The co-variance of the observation noise was set to

(10−3 10−5

10−5 10−3

).

E. Nuclei RecognitionAfter the previous algorithms, some cell follower may also

lose their cell. As already stated in section II-A fibroblasts arevery active cells following an always-moving, always-shape-shifting behavior which makes it impossible to recognize themusing morphology operators, as for example in [5]. They havethe characteristic, though, that even if their shape may varyin an enormity of different variations, their nuclei always dokeep some recognizable patterns. They are always enclosed bya recognizable membrane, they are darker and always showingdense nuclear bodies in variable number.Such a recognizable pattern may be detected by artificialintelligence algorithms such as classifiers or statistical learn-ing methods. The idea was that, if a classifier was trainedwith nuclei and non-nuclei images, then it would be able todistinguish them. For this kind of task the 13 Haralick texturefeatures are ideal [16]. There were extracted from the stacksportions of images containing nuclei and non-nuclei objects;those portions were 20×20 pixel, the average area occupied bythe biggest nuclei. 100 nuclei and 200 non-nuclei were taken,including also very nuclei-like structures which were formedby noise and/or impurities. They were also rotated 90, 180 and270 degrees to create new samples, for a total of 1200 samples,400 nuclei and 800 non-nuclei, used to train the classifier.The chosen classifier was a Support Vector Machine (SVM)[17] and the relative selected implementation was LIBSVM2,used with a polynomial kernel:

K(xi, xj) = (γxTi xj + r)d, γ > 0 (3)

2http://www.csie.ntu.edu.tw/∼cjlin/libsvm/

The SVM values were 1.2 for γ, 0.6 for r and 3 for d. ‘T’stands for transposed.

1) Initialization Phase: In the Cell Recognition phase (sec-tion II-B), when a set of candidate points have been found inthe first frame F1, for each one of them the 20× 20 areaHaralick features are extracted and the areas are classifiedwith the SVM; those corresponding to nuclei being keptand becoming initial cell positions p1

k, with the others beingdiscarded.

2) Tracking Phase: During the tracking the 20×20 areacentered in each cell position is extracted and the Haralickfeatures calculated. Then the SVM classifies it and tells if itis a nucleus area or not. The most well classified one is chosenand taken as pi+1

k .

F. Mitosis Recognition

Fibroblast mitosis takes place in the following way: the cellbecomes round and detaches itself from the platelet basis.Due to this behavior, in phase-contrast microscopy the cellsuddenly appears as a bright, circular shape occupying theprevious position the stranded cell had. The Mitosis Recogni-tion algorithm is therefore based on blob detection: it detectslarge light blobs in the area previously occupied by the cellk by thresholding the block, identifying the objects in it bya connected component labeling and checking their size; ifone’s dimensions exceed a preset threshold, it is considered amitosis. A priori knowledge to set the threshold is of courseneeded here. If a mitosis is found, the tracking of cell k ends,and two new cells are created.

1) Brightest Area Centering: Due to the bright blob, thenewborn cells cannot be tracked with the above method. Infact for a number of frames nmit the new cells still appear liketwo connected bright blobs. For this reason from frame Fk toFk+nmit in order to continue tracking the same principle insection II-C1 is used, but it is reversed, aiming at the brightestarea.Of course new values dsrcB and davrgB are used. The mitosisduration is few frames anyway, so the value for nmit was setto 4; dsrcB was set to 7 and davrgB to 28.

G. Algorithm

So the pseudocode for the full algorithmis:

1: Read image stack2: Image Enhancement3: Cell Recognition %in the first frame, either manually or automati-

cally4: for each frame do5: for each cell do6: if the cell is still tracked then7: Motion Estimation8: Motion Prediction9: Darkest Area Centering

10: Nuclei Recognition11: Mitosis Recognition12: if a cell is mitotic then

13: Stop tracking mitotic cell14: Start tracking daughter cells15: end if16: end if17: if the cell is newborn (≤ nmit frames) then18: Brightest Area Centering19: end if20: end for21: end for22: print the resultThe result is shown in an image stack containing the trackingprocess and a three-dimensional diagram to give an immediatevisualization of the spatiotemporal evolution of the wholestack [5] is created.

Fig. 5: The three-dimensional spatiotemporal diagram of cell move-ments.

III. EXPERIMENTAL RESULTS

The algorithm testing was performed on real data; thedataset was an image stack of phase-contrast microscopy ofin vitro MEF fibroblast activity. The stack consisted of 73grayscale frames with dimensions 650×514 pixels. The stackwas first analyzed with MotoCell with a manual trackingto achieve a ground-truth tracking to compare the automatictracking with. For the same reason for the Cell Recognitionphase the first manual selection was used. The algorithm’soutput was then examined visually to see if each cell followersucceeded in correctly tracking the cell from start to finish.In manual tracking the cell at some point may encounter an endfor many reasons: the cell may divide, may become apoptotic,may exit the observation area or may become confused in atight clustering below other cells. Each cell was tracked bothmanually and automatically until one of those destinies, andthe resulting trajectories were analyzed. The manual trackinghad a time interval of four frames, i.e. the cell position wasregistered each four frames, while the automatic one registeredthe cell position for each frame. The total number of trackedcells was 40.

1) Global Accuracy: To give a global numerical estimationof the algorithm performances in each frame the number ofcorrectly tracked cells were taken, then the average for allthe frames was calculated obtaining the average number of

correctly tracked cells in each frame, and then were dividedfor the total cells:

accuracy =

∑total frames

k=1correctly tracked cells in frame k

number of frames

number of cells

resulting in a global accuracy of 70.75%.Of course cells correctly tracked may end their existencebefore the last frame; it their destiny was detected correctlythey were considered being correctly tracked in all the frames.

2) Precision and Recall: Precision and recall have beencalculated the following way: for each frame the number offollowers on cells (True Positives), the number of followersnot on cells (False Positives) and the number of untrackedcells (False Negatives) have been counted. The first frame wasskipped from the calculus, because it was initialized manually.On each frame precision and recall have been calculated, andtheir averages was extracted. The values were 0.78 and 0.60 forprecision and recall respectively. This precision value indicatesthat, chosen an arbitrary follower, the probability that it isactually following a cell is high. On the other hand the recallis not very high; this is due to the fact that not all the cellsare followed since the beginning and some of them may alsodivide, increasing the untracked cell number. Also, new cellsincreasingly enter the observation area because fibroblasts inthe other unseen part of the platelet are dividing too.

3) Accuracy Categories: The trajectories were divided inthree categories: Full for the cell correctly tracked along theentire stack, Partial for the cells correctly tracked only for aportion of the stack and None for the cells incorrectly tracked.The first frame was correct because manually initialized by ahuman expert and the three categories were divided as follows.None if the cell was lost in the first 20% of the stack (i.e. thefirst 15 frames); Full if tracked until the last 10% of the stack(i.e. until the last 7 frames) and Partial if the cell was lostalong the remaining frames.The results were that 55% of the cells were correctly trackedalong the whole stack (Full), 30% were Partial and theremaining 15% were None. Additionally, the Partials weretracked correctly for an average of 46% of the whole stack,and 42% of them (half of them less one) were lost due toan undetected mitosis; this was due to the fact that the cells,detaching during mitosis, often abruptly changed their positionfrom a frame to another, “appearing” in a distant location.Also, one of the cells was correctly tracked until it exited theobservation area but the algorithm wasn’t build to handle thoseexceptions yet, so it was considered a Full.When not Full (i.e. in Partial ∪ None) we may also dividein Lost (cell lost with follower stuck on the background)and Mistaken (follower jumped to another cell) which whererespectively 30% and 15% of total cells, divided as follows:Partial was composed of 75% Lost and 25% Mistaken andNone was composed of 50% Lost and 50% Mistaken.

It has to be noticed that all the Fulls were correctly trackeduntil the last frame. Examples of the tracking process may beseen in figure 3, while in figure 5 the stack’s spatiotemporalevolution is shown.

ImageStack

Image En-hancement

CellRecognition

MotionEstimation

MotionPrediction

DarkestArea

Centering

NucleiRecognition

MitosisRecognition

TrackingResults

BrightestArea

Centering

Fig. 4: Block diagram for the algorithm phases.

IV. CONCLUSIONS AND FUTURE WORKS

We presented a model for a system capable of automaticallytracking difficult cells (such as fibroblasts) in in vitro time-lapse phase-contrast microscopy. It is composed of varioussequential phases which may interact between them: a Mo-tion Estimation phase which search the position the cell hastraveled to from a frame to another; a Motion Prediction phasewhich predicts the cell position in the next frame, and helpadjust the Motion Estimation search area; a Darkest AreaCentering which keeps the follower in the center of the cell; aNuclei Recognition phase which identifies the nuclei to makesure the tracked object is a cell and a Mitosis Recognitionphase to track and manage mitotic cells.

Future improvement of this work may cover a wide area. Inthe manual recognition phase (section II-B1) an improvementin the SVM management may be done. At present, in fact,the SVM needs a set up step that has to be done before anytracking which is the collecting of the images and featuresfor the training process. A possible solution is that duringthe manual identification of cells, also non-cell objects areselected, thus providing the SVM a collection of positive andnegative examples to train the SVM [18]. Another importantimprovement would be the addiction of a segmentation step,useful for studying mechanisms such as variations in cellmovement, shape abnormalities, pseudopodia behavior, etc..This could be realized with energy functional minimizationmodels, using algorithms capable of managing touching andclustered cells [19]. Also, a different Mitosis Recognitionphase may be engineered differently, utilizing edge detectionalgorithms to track the ellipsoidal bright mitotic blob suchas the Canny edge detection algorithm [5]. Finally, detectionof other cell destinies than mitosis (apoptosis, exiting theobservation area) will be desirable.

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