NeuronGrowth, a Software for Automatic Quantification of Neurite and Filopodial Dynamics from Time-Lapse Sequences of Digital Images Zian Fanti,1,2 M. Elena Martinez-Perez,2 Francisco F. De-Miguel1 1
Instituto de Fisiologı´a Celular-Neurociencias, Universidad Nacional Auto´noma de Me´xico (UNAM), Me´xico
2
Departamento de Ciencias de la Computacio´n, Instituto de Investigaciones en Matema´ticas Aplicadas y en Sistemas, Universidad Nacional Auto´noma de Me´xico (UNAM), Me´xico
Received 6 October 2010; revised 6 December 2010; accepted 10 December 2010
ABSTRACT: We developed NeuronGrowth, a software for the automatic quantification of extension and retraction of neurites and filopodia, from time-lapse sequences of two-dimensional digital micrographs. NeuronGrowth requires a semiautomatic characterization of individual neurites in a reference frame, which is then used for automatic tracking and measurement of every neurite over the whole image sequence. Modules for sequence alignment, background subtraction, flat field correction, light normalization, and cropping have been integrated to improve the quality of the analysis. Moreover, NeuronGrowth incorporates a deconvolution filter that corrects the shadow-cast effect of differential interference contrast (DIC) images. NeuronGrowth was tested by analyzing the formation of outgrowth patterns by individual leech neurons cultured under two different conditions. Phase contrast images were obtained from neurons plated on CNS homogenates and DIC images were obtained from similar neurons plated on ganglion
INTRODUCTION Quantification of the dynamics of neurite growth and retraction during the formation of neuronal outgrowth patterns from time lapse series of images is done by Correspondence to: F.F. De-Miguel (
[email protected]). 2010 Wiley Periodicals, Inc. Published online 29 December 2010 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/dneu.20866 '
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capsules as substrates. Filopodia were measured from fluorescent growth-cones of chick dorsal root ganglion cells. Quantitative data of neurite extension and retraction obtained by three different users applying NeuronGrowth and two other manually operated software packages were similar. However, NeuronGrowth required less user participation and had a better time performance when compared with the other software packages. NeuronGrowth may be used in general to quantify the dynamics of tubular structures such as blood vessels. NeuronGrowth is a free plug-in for the free software ImageJ and can be downloaded along with a user manual, a troubleshooting section and other information required for its use from http://www.ifc.unam.mx or http://www.ifc.unam.mx/ffm/index.html. ' 2010 Wiley Periodicals, Inc. Develop Neurobiol 71: 870–881, 2011
Keywords: software; automatic neurite tracing; growth dynamics; quantitative time-lapse; neuronal regeneration and development
use of manual-based software packages applied to images acquired under different optical conditions, including phase-contrast (Al-Kofahi et al., 2006; Keenan et al., 2006), differential interference contrast (DIC), and fluorescence microscopy (Al-Kofahi et al., 2003; Meijering et al., 2004). Some algorithms developed for digital tracing of structures are already in use for semiautomatic quantitative studies in a variety of applications including the analysis of neurite outgrowth from DIC series of images (Fanti
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et al., 2008), neural responses to trauma and disease (Pool, 2008) and neurotoxicology assays (Meijering et al., 2004; Pool, 2008). However, an automated and versatile software for the tracing and quantitative measurement of neurite extension and retraction patterns from time-lapse series of images is still lacking. Here, we present the software package NeuronGrowth, which has been designed to quantify timelapse sequences of microscopy images in two dimensions and to generate quantitative descriptions of the time-dependent dynamics of the formation of neuronal outgrowth patterns. With this idea, we previously designed a software to quantify neurites from DIC series of images (Fanti et al., 2008), however, its performance required continuous user intervention due to the use of point to point ridge tracking. NeuronGrowth corrected that by using a combination of the livewire (Mortensen et al., 1992) and snakes algorithms (Kass et al., 1988) applied to neurite tracing and quantification in subsequent images. Moreover, the use of NeuronGrowth has been extended to images obtained under different types of optics. In NeuronGrowth, the detection of neurites is based on the characterization of tubular structures by use of differential geometry. After pre-processing of the whole sequence to correct for alignment, light intensity and contrast, one reference frame is selected by the user for a semiautomatic tracing of individual neurites. The program then tracks automatically the complete sequence of images and calculates the lengths of the individual neurite traces. Quantification of filopodia requires more user intervention, since they move along with the growth cone and they have continuous changes in length and direction. NeuronGrowth also has a filter for correcting the shadow-cast effect of DIC images. The data and the time performance obtained by the use of NeuronGrowth were validated by comparison with those obtained by image to image measurements made with aid of two other software packages.
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that can be visualized with different optics: to obtain sharp phase contrast images neurons were plated on dishes precoated with homogenates obtained from the central nervous system of the leech (CNS homogenates; De-Miguel and Vargas, 2002). To obtain DIC series of images, AP neurons were plated on the inner side of the ganglion capsules that enwrap the leech ganglia (Fernandez de Miguel, 1997). On this thick substrate DIC images have low contrast and signal to noise ratio (De-Miguel and Vargas, 2000; FloresAbreu et al., 2006).
Image Acquisition Time-lapse series were taken at a 403 magnification with a CCD camera (Hamamatsu C2400, Hamamatsu City, Japan) coupled to an inverted microscope (Nikon DIATHOPTMD). Each image frame had 640 3 480 pixels with a depth of 8 bits. Individual images were taken at userdefined intervals and stored on disk by using a costumedesigned system developed in our laboratory. The cultures were maintained at 188C. The formation of the outgrowth patterns on both substrates could be followed from timelapse image sequences obtained automatically at rates of 2–12 min per image during periods of several days without any damage to the neurons.
Sequence Preprocessing Frame misalignments, non-homogeneous illumination between frames and DIC shadow-cast effects were corrected before quantization. Not all of the pre-processing steps were needed for each image sequence, since they depend on the quality of the image sequence. Therefore, some sequences may not need illumination, brightness, and contrast adjustments or the use of filters to reduce the noise. Flat Field Correction. NeuronGrowth contains a function for flat field correction in which the uneven field illumination was corrected based on Expressions (2004). The corrected image was computed as:
Ic ðx; yÞ ¼
Iðx; yÞ Ib ðx; yÞ l ; Iff ðx; yÞ Ib ðx; yÞ
METHODS Isolation and Culture of Neurons Anterior Pagoda (AP) neurons were isolated from the central nervous system of adult leeches Hirudo sp. The procedure has been described elsewhere (Dietzel et al., 1986).
Outgrowth Patterns and Time-Lapse Microscopy Individual AP neurons were cultured on two different substrates to produce characteristic bipolar outgrowth patterns
where I(x,y) is the original image, Ib(x,y) is the reference image acquired with the microscope lights off, Iff(x,y) corresponds to an image of the scene without specimen, and l is the mean value of the image intensity obtained from the subtraction Iff(x,y) Ib(x,y). Light Normalization. To correct for variations in illumination during the time-lapse sequence, a reference frame Ib is selected and the mean lb and standard deviation rb values of illumination were computed and compared with those of each image Ik along the sequence (Radke et al., 2004). The corrected image Ik was obtained as follows: Developmental Neurobiology
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Fanti et al. rb Ik ðx; yÞ ¼ ðIk ðx; yÞ lk Þ þ lb : rk
Sequence Alignment. Misalignments between subsequent frames are corrected by use of a plug-in of ImageJ software (ImageJ, 2009) that minimizes the mean square intensity difference between the reference and the test frames (Thevenaz et al., 1998). The minimization is performed according to a variation of the Maquardt-Levenberg algorithm for nonlinear least-square optimization. DIC Correction. Filtering the images in the Fourier domain corrected the shadow-cast effect of DIC images (Van-Munster et al., 1997). In the reconstructed image, each gray value was represented by the optical path-length at that particular location, thus enabling the further quantitative analysis of the objects. Background Elimination. To reduce the background artifacts and improve the contrast of the neurites along the sequence, NeuronGrowth has the possibility to generate a background sequence by using the \running average" method (Lai and Yung, 1998), which for each image in the sequence provides an approximated average of the previous images. The background sequence is then subtracted from the original sequence through a \normal statistics of the difference" procedure (Cheung and Kamath, 2005) that allows to keep the illumination characteristics of the image and avoids pixel elimination or saturation.
Neurite Tracking Neurite tracking in a sequence involves two stages. The first is the detection and characterization of the neurites of interest in a selected reference frame. The second stage consists on the automatic tracking and measurement of the selected neurites forwards or backwards along the time-lapse sequence. Detection of Neurites in a Single Frame. Neurites are always considered as tubular structures and NeuronGrowth performs a ridge detection and tracing to characterize them individually in the reference frame. For this, NeuronGrowth identifies those pixels in which the intensity has a local maximum or minimum and follows the direction in which the gradient of the image undergoes the largest concavity (Eberly et al., 1994). The second derivative of the image renders the Hessian matrix of the intensity image I(x,y): H¼
@xxI @yxI
@xyI : @yyI
As @xyI ¼ @yxI, then the Hessian matrix is symmetric with real eigenvalues and orthogonal eigenvectors. A pixel belonging to a given neurite has its maximum eigenvector perpendicular to the neurite orientation. Because of the orthogonal nature of the system, the ridge of a neurite will be in the direction of the minimum eigenvector. Developmental Neurobiology
The ridge points are then linked to form a continuous centerline along the neurite. For this, NeuronGrowth uses a modification of the algorithm called \live-wire" (Mortensen et al., 1992; Meijering et al., 2004). To obtain the optimal centerline of the neurite, the user defines an initial point (or seed), and then, as the interactive movement of the cursor approaches the neurite, end the ridge behaves as a \live wire," thus adapting to the minimum cost path between the initial point and the cursor [Fig. 1(B)]. The procedure is based on the magnitudes of the eigenvalues and the direction of the eigenvectors and is similar to the procedure by Meijering et al. (2004), except for the sensitivity-increasing factor used for ridge-like structures. Suitable Movement of the cursor by the user causes the live-wire to follow the neurite [Fig. 1(B)] and the process finishes when the user selects the final point [Fig. 1(C)]. Neurite Characterization. Once a neurite is traced, a table (Table 1) displays the name and type of neurite (primary, secondary, etc.) as defined by the user. The table also contains the frame number, the neurite length and the color assigned to the overlay. The user can edit the table to characterize the reference frame before the automatic tracking is performed along the whole sequence. The automatic tracking process can proceed forwards, backwards, or both along the image sequence, depending on which image is chosen as the reference Ik. For this, NeuronGrowth finds all the \critical points" (Eberly et al., 1994) that indicate bifurcations, changes in the trajectory or thickness of a neurite, where it begins and where it ends. Commonly, the critical points are the centers of mass of pixel clouds that have all their eigenvalues either negative or positive, so to obtain a single point we used the center of mass of each pixel cloud [Fig. 1(D)]. NeuronGrowth associates each neurite with the critical points of the image that have Euclidean distances 2 pixels from the trace defined by the user [Fig. 1(E)]. Figure 1(E) shows a neurite selected by the user in the reference frame Ik and the critical points associated with that same neurite. Neurite Tracing in the Image Sequence. To find the initial point for the new trace in the next image NeuronGrowth first uses the initial point that had been defined by the user in the reference neurite. The initial point is readjusted in the new image by use of a descriptor d~ that contains the information on the eigenvalues and eigenvectors on a userdefined neighborhood, commonly a matrix of 3 3 3 pixels. This vector is then transferred to an equivalent position in the following frame, where NeuronGrowth defines another neighborhood with a window size set by default to 31 3 31 pixels (although it may be user defined). NeuronGrowth then searches for the critical points that fall inside that window and computes the vector d~0 for their center of mass, thus keeping only the critical point having the shortest ~ As a following step, the critical euclidian distance to d. points from the neurite of interest in the reference frame are transferred to that same neurite in frame Ik + 1. Note that the same procedure occurs in a backwards direction [Fig. 1(F)].
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Figure 1 Steps in the characterization and tracking of individual neurites. A: Phase contrast image of an AP neuron in culture with a primary neurite (P) bearing a growth cone with filopodia. The outof-focus soma of the neuron can be seen in saturated white at the bottom. B: \Live-wire" partial tracing of the primary neurite (cyan overlay) starting at the user-defined origin, in this case near the soma. The cyan trace follows the cursor and adapts to the minimum cost path function even when the cursor is still distant from the neurite. C: Overlay of the primary neurite when the user has defined the final point. D: Center of mass of the critical points superimposed on the neuron. E: Automatically selected critical points that will be used as an input to the snake are shown superimposed on the neurite overlay. F: The following image in the series (Ik + 1) displaying the center of mass of the critical points that have been transposed from the reference image. The black arrow shows a new neurite segment. G: Center of mass of critical points from the reference frame (blue circles) and the corresponding points that were automatically fitted by a snake (red diamonds) superimposed on image Ik + 1. H: Automatic overlay generation based on the critical points. I: Final automatic centerline trace in image Ik + 1 extended to the new neurite segment (arrow). The interval between images was 12 minutes. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] By using the initial and critical points as a reference, NeuronGrowth creates a snake (Kass et al., 1988) that adjusts the positions of the critical points to the new characteristics of the neurite and then uses the live wire approach to track the neurite automatically along its center [Fig. 1(G,H)]. Detection of Neurite Extension and Retraction. In case of neurite extension [arrow in Fig. 1(F)], NeuriteGrowth automatically makes a ridge point tracking until finding a stop condition (Ayward and Bullit, 2002) and extends the overlay along the new segment, provided that it accomplishes the
vectorial characteristics of the neurite [Fig. 1(H,I)]. The stop conditions may be adjusted by the user to determine that the tracing will not invade smaller structures, such as filopodia, that have different vectorial components. The entire centerline of the neurite is then uniformly smoothed by a userdefined pixel window (by default 9 3 9 pixels) and the length in pixels is determined. NeuriteGrowth may also provide distances in pre-calibrated units. The centerlines resulting from the tracking process are displayed as colored overlays on top of their corresponding neurite in each subsequent Developmental Neurobiology
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Table 1 Data Displayed By NeuronGrowth, with the User-Defined Name of the Structure [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] Name S-3 P-1 S-1 S-3 P-1 S-1 S-3 P-1 S-1 S-3 P-1 S-1 S-3 P-l S-1
Type
Frame
Length
Secondary Primary Secondary Secondary Primary Secondary Secondary Primary Secondary Secondary Primary Secondary Secondary Primary Secondary
15 15 15 16 16 16 17 17 17 18 18 18 19 19 19
15,464 68,755 43,327 10,622 69,825 44,396 8118 73,817 47,297 8008 72,968 46,017 5007 76,595 42,852
Color
In this case, P for primary neurite and S for secondary neurites. The table also displays the characteristics of the neurite (primary, secondary, etc.), the frame number, and the neurite length in micrometers obtained automatically upon a previous calibration. The colors displayed on the right side of the table are the same as those of the overlay generated by NeuronGrowth for the corresponding neurites (see also Fig. 2).
frame, thus allowing a visual inspection of the accuracy of the tracking process. This procedure can be done in parallel for all the neurites that had been selected in the reference image. The resulting analysis is matched with that of the previous image, and NeuronGrowth assigns the same name and color code that had been determined in the reference frame. Neurite retraction produces a line shrinking during the snake fit, and upon full retraction some of the critical points vanish along the image sequence, thus leaving only the starting point. At his point, NeuronGrowth eliminates the neurite and stops its further tracking. If new neurites are generated at any time of the image sequence, the user needs to aggregate the basic information as described before, and NeuronGrowth continues its automatic tracking along the image sequence. If the user detects that the overlays do not follow the neurite correctly, the tracking process can be stopped at anytime by the user to correct the neurite tracing manually in that frame. These corrections are followed by the execution of the automatic procedure again. At the end of the whole process, data can be exported to a user-preferred spreadsheet.
RESULTS Neurite Tracing in Phase Contrast Image Sequences In this section, we demonstrate the use of NeuronGrowth with phase contrast image series. However, Developmental Neurobiology
since the algorithms used to implement NeuronGrowth were designed for the quantification of tubular structures with high contrast, NeuronGrowth may be applied equally well to images taken with fluorescent optics. Certain preprocessing steps may be necessary depending on the characteristics of the image series. The characteristic outgrowth patterns developed by AP neurons in each substrate were as previously described (Fernandez de Miguel, 1997; De-Miguel and Vargas, 2000; De-Miguel et al., 2002; De-Miguel and Vargas, 2002). The sequence of phase contrast images in Figure 2 contains extension and retraction of primary and secondary neurites within a period of 8 h. The overlays of the primary (cian) and secondary (orange and magenta) neurites in the Figure were traced automatically with different colors by NeuronGrowth, having frame F.01 as the reference. Two secondary neurites were formed and retracted within 4 h and 48 min (F.01 and F.25) from opposite sites of the primary neurite. The tracking information for these neurites was added by the user in frame 7 (not shown) and was traced automatically backwards and forwards by NeuronGrowth along the rest of the sequence. Simultaneously, the longest secondary neurite was first elongated and after image 33 started retracting. During the same period, the primary neurite had some extension (De-Miguel and Vargas, 2002). Table 1 contains the neurite lengths for images 15–19 of the sequence shown in Figure 2 in the NeuronGrowth format.
Tracking Filopodia in Fluorescent Images Quantifying filopodia imposes additional challenges, since their origin changes as neurites grow, they are thinner, have lower contrast than neurites, and also have fast extension and retraction dynamics. This section presents quantification of filopodia from a sequence of 580 frames of a fluorescent growth cone of a cultured chick dorsal ganglion neuron that had been infected with HSV-tdTomato (kindly provided by Ed Van Veen, Rachel Neve, and Frank Gertler; http://techtv.mit.edu/videos/9111-watching-neurons-grow). The image series was opened with ImageJ and only the red component of the RGB series was kept for the analysis, since it contained all the relevant information. The scale of the images was doubled by the interpolation method previously described, thus rendering four times more pixels with smaller sizes, and therefore a better resolution in the quantifications. Figure 3 shows selected images from the sequence before (left) and after (right) the overlays were traced by NeuronGrowth on 16 filopodia. The right panel shows the length versus time plots for the neurite segment and the filopodia. It was possible to
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Figure 2 Overlays obtained by automatic neurite tracing of several neurites from a sequence of 70 frames (F. 70) taken at 12 min intervals. F. 01 was the reference frame; the arrow shows a primary neurite and the arrowhead shows one of the secondary neurites. The color overlays were obtained by NeuronGrowth. Cyan is a primary neurite, orange and purple are secondary neurites. The overlays in F.09–F.41 were obtained automatically. The time is indicated in each panel. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
trace the individual filopodia, although the process required more user intervention. The neurite and the filopodia were quantified separately since owing to the different thickness and gray scales they required different stop conditions. The quantification time of the neurite was 79 min with 36 images corrected by the user along the whole sequence. This was because of the continuously changing growth cone. After each correction, the program continued working automatically until the next stop. The quantification time for the filopodia was 125 min with 53 alternations between the manual and automatic modes for tracing corrections. We also quantified filopodia from the phase contrast images in the previous section, with similar accuracy (not shown). When images had low resolution, the accuracy of the measures was increased by dividing the pixel size by four with the high-quality interpolation method plugin for ImageJ (Mun˜oz Barrutia et al., 2001). A useful procedure was to initiate the tracing in the frame in which the structure had reached its maximum length and proceed with the automatic quantification forwards and backwards in the sequence.
Tracing Neurites in DIC Image Sequences Neurite tracing in DIC images requires more user intervention. Figure 4 shows images with low signal to noise ratio and the shadow cast effect with the
bimodal light profile across the neurites that characterizes images obtained with DIC optics. This effect eliminates the possibility of a direct neurite tracking by NeuronGrowth, since it requires a central concavity along the tubular structure. Removing the shadow cast effect produced an intensity profile with a major concavity or convexity across the neurite in the image and increased its contrast with respect to the background (Fanti et al., 2008). This can be seen in Figure 5, in which the series of DIC images shown on top were corrected for the shadow cast effect (bottom) and NeuronGrowth could then trace the neurites automatically along the series.
Comparison of Neurite Lengths Obtained with Other Software Packages Data obtained automatically by use of NeuronGrowth were similar to those obtained by tracing neurites image after image with the manual tools of NeuronGrowth, with the software ImageJ (Rasband, 2009) or NeuronJ (Meijering et al., 2004), which is a free plug-in for the software ImageJ. Figure 6 is a plot of the neurite length versus time made from average measurements obtained by three different users from phase contrast [Fig. 6(A)] or DIC [Fig. 6(B)] image sequences. One of the users was an expert in analyzing neuronal outgrowth patterns with experience with the three software packages. The two other users had Developmental Neurobiology
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Figure 3 Quantification of filopodia from a fluorescent image series. The vertical image series on the left shows different moments of a fluorescent time lapse series of a neurite with a growth cone and filopodia of a cultured chick dorsal root ganglion neuron infected with HSV-tdTomato virus (Available at: http://techtv.mit.edu/videos/9111-watching-neurons-grow). The frame number is indicated in the corner of each image. The middle row vertical image sequence is the same as those on the left, after the filopodia were traced with NeuronGrowth. The overlays are shown with different colors and the neurites are identified with numbers. The right row shows the length of the neurite (top) and the length of individual filopodia over time. Sprouting and absorption of individual filopodia can be seen. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
only programing experience. The colored traces superimposed in Figure 6 show that the average measures made with each software package were similar. However, the time employed by each user for the quantification was 55–70% shorter with NeuronDevelopmental Neurobiology
Growth operating automatically. As an example, for one of the users the operating average times with the automatic mode of NeuronGrowth was 11 min including the time used to define the reference neurite traces, correct inappropriate traces along the
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Figure 4 Shadow cast correction in a low signal to noise DIC image. Panels A–C are a dic image before (A) and after (C) the shadow cast correction. The arrow shows a primary neurite and the arrowhead shows a secondary neurite. The light intensity profiles obtained from the adjacent pixels along the black line are shown in images B and D, respectively. Note the reduction of the bimodal profile of the light intensity after the shadow cast correction.
sequence, manual addition of new neurites and sorting of the data for the length versus time plots. With NeuronGrowth tracing neurites frame by frame, the total time was 19 min and the times obtained with the other software packages were 42 min with NeuronJ and 60 min with ImageJ. An additional time-saving characteristic of NeuronGrowth that was absent from the other software packages is that data may be exported to a spread sheet for posterior analysis.
Accuracy of Measurements Obtained with Different Methods The three users contributed separately and independently to validate the accuracy of the measurements obtained with NeuronGrowth. As a first step, we obtained accurate reference measures from images series scaled up by a factor of 10 by using a high-quality interpolation method (Mun˜oz Barrutia et al., 2001). To obtain the reference measures, the users traced the neurites manually by using the segmented line tool of ImageJ. The number and type of neurites measured
per frame were defined before the measurements were done. Three tracings were made and averaged by each user. The final reference trace was obtained from the average of the measurements made each user by set back to the original scale. These reference traces were superimposed as overlays to the original images, thus yielding a precision of 1/10th of a pixel. The following part of the analysis was made on a phase contrast sequence of 25 images and one DIC sequence of 20 images in their original scale. Here, the observers repeated the measurements three times by using NeuronJ (Meijering et al., 2004) and NeuronGrowth automatically and also semiautomatically, tracing neurites frame by frame. The accuracy of the measurements was determined as a ratio in which the length difference between the reference and the individual measurements made with each software package was divided by the length of the reference measurement. A value larger than zero indicates an overestimation of the true length; conversely, a value smaller than zero indicated an underestimation of the measure. Figure 7(A) shows the error rates of measurements made from phase conDevelopmental Neurobiology
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Figure 5 Neurite tracing from high contrast DIC images. A–D: Raw frames numbers 1, 20, 33, and 70 from a sequence of 111 images taken at 12 min intervals. The neuron regenerated neurites with branches after being plated on a ganglion capsule as a substrate. A0 –D0 are the same images after the preprocess that included alignment, compensation of illumination, and shadow cast correction. The color overlays were traced automatically by NeuronGrowth after the semiautomatic tracing of the neurites in the reference frame. A primary neurite is cyan and a secondary neurite is pink after tracking the reference frame A0 . Another secondary neurite (orange in B0 ) emerged later and the reference trace was added by the user in a different frame. C0 –D0 : Tracking of the primary and secondary neurites over the rest of the sequence. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
trast and DIC image sequences. The error of automatic measurements of DIC images obtained with NeuronGrowth mode was the largest, reaching 10%. The rest of the errors were below 5% for all the cases. This measure, however, fails to give a good indication of how well the traces follow the neurite, since different traces may have the same length and yet have a different degree of dispersion over the neurite. As an accuracy estimate, we measured the average distance between the traces of individual neurites and Developmental Neurobiology
the reference trace (Fig. 7). Again, the best matches were obtained for phase contrast images, in which the differences were about 1 lm. Again, the dispersions were larger with automatic NeuronGrowth applied to DIC image series due to the inherently smaller signal to noise ratio of the DIC images. The asterisks on top of each bar in Figure 7 indicate the probability levels of two-sided paired Student’s t test. The asterisks above each bar in Figure 7 indicate for the different levels of probability that
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Figure 6 Neurite length versus time plots of the measurements obtained with the different software package indicated in the inset. A: Quantification of four neurites in a sequence of 70 images taken under phase contrast optics (see Fig. 2). B: Quantification of five neurites in a 70 image sequence obtained under DIC optics (see Fig. 4). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
consider that both measurements are similar with p < 0.01 (**), p < 0.05 (*), and any probability larger than 0.05 (no asterisk).
DISCUSSION We present NeuronGrowth, a software package for automated quantification of the dynamics of outgrowth pattern formation form time-lapse series of digital images. NeuronGrowth produces measurements statistically equivalent to those obtained with other software packages that operate manually or are manually aided, although with better time perform-
ance. The analysis made with NeuronGrowth shows the elongation and retraction of neurites and filopodia during development or regeneration of individual neurons from series of images taken under different optical methods. The full processing time obtained by using NeuronGrowth automatically in image sequences was substantially shorter than the manual measures of the same sequence made with other software packages. By reducing the user intervention, the automatic components of NeuronGrowth may avoid user errors that in our experience are produced upon long and repeated working sessions. Moreover, the software package \NeuriteTracer" (Pool, 2008), which is spe-
Figure 7 Comparison of neurite length and trace accuracy obtained with different software packages. A: Error percentage obtained with each software package with respect to the reference traces for phase contrast and DIC image series. B: Average differences between traces made with each software package and the reference traces for phase contrast and DIC image series. The boxes contain 75% of the measurements while the bars represent all of them. Asterisks represent the probability of obtaining similar measurements with p < 0.01 (**) and p < 0.05 (*). Larger probabilities are not indicated. Developmental Neurobiology
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cific for images containing stained neurons failed to trace the neurites of our phase contrast or DIC images. In addition, the shadow cast effect of DIC images does not allow NeuronJ to track and segment neurites in these types of images. However, after applying the DIC shadow-cast effect correction of NeuronGrowth, NeuronJ could operate in an adequate manner (data not shown). Since DIC image series are usually taken in situ, the differences in the focal planes of the structures may be corrected by full reconstruction of the neurites and growth cones through a superimposition of images obtained at different focal planes. NeuronGrowth was implemented in Java, as an independent and multi-platform system containing the entire digital image processing modules described in this paper, except for the sequence-alignment module, which can be obtained from http://bigwww.epfl.ch/thevenaz/ stackreg/. NeuronGrowth was tested in computers with Microsoft Windows, Linux, and Mac OS platforms using similar microprocessors (Intel Core 2 Duo 2.0 Ghz) and 1 GB of RAM memory. In all cases, NeuronGrowth had a similar performance, taking about 2 s to extract the neurite characteristics and measure the length of neurites in 640 3 480 pixel, 8 bit images. In the case of 14, 16, or 32 bit images, ImageJ has a module for their conversion to 8 bits. In addition, the time performance of NeuronGrowth is inversely proportional to the size of the images. We tested different sizes of images, up to 2000 3 4000 pixels and although NeuronGrowth became substantially slower when working with larger images, its tracking capabilities were similar to those displayed when measuring from smaller images. Computers with more powerful processors should have better performances since most of the working load of the algorithm depends on the calculation of the eigenvectors and eigenvalues, which is a highly demanding process relying mostly on the processing capabilities of the computer. The results presented here were obtained from 8 bit images. In case of higher resolution images, an 8 bit conversion is needed. In its present form, NeuronGrowth is restricted to the quantitative analysis of elongation and retraction and is not capable of detecting changes in the thickness of the branches. However, since the neurite thickness has a large error that stems from the similarity between the neurite diameters and the resolution of the light microscopes, the dominant parameters of the dynamics of the formation of outgrowth patterns are the extension and retraction of neurites and the formation of branches. These parameters are the focus of NeuronGrowth. This work was supported by an Ixtli Fellowship and PAPIIT IN223006-3 projects to F.F. De-Miguel and Z.F. Developmental Neurobiology
received a CONACYT fellowship. The authors are greatly indebted to Prof. Frank Gertler, Ed Van Veen, and Rachel Neve, from the Koch Institute of the Massachusetts Institute of Technology for kindly providing the image series of dorsal root ganglia used in this study. They express their gratitude to Mr. Bruno Mendez, Osvelia Gutie´rrez, and Javier Vargas for the implementation of the time-lapse video recording system and to Juan Barbosa, Francisco Perez, and Gerado Coello from the Computing Unit at the Institute for Cellular Physiology for their continuous support and for setting up the NeuronGrowth web page.
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Developmental Neurobiology
