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An algorithm for neurite outgrowth reconstruction
Journal of Neuroscience Methods 124 (2003) 197 /205 www.elsevier.com/locate/jneumeth
An algorithm for neurite outgrowth reconstruction Christina M. Weaver a, John D. Pinezich b, W. Brent Lindquist a,*, Marcelo E. Vazquez c a
Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA b Advanced Acoustic Concepts, Hauppauge, NY 11788, USA c Medical Department, Brookhaven National Laboratory, Upton, NY 11973, USA Received 2 October 2002; received in revised form 14 January 2003; accepted 14 January 2003
Abstract We present a numerical method which provides the ability to analyze digitized microscope images of retinal explants and quantify neurite outgrowth. Few parameters are required as input and limited user interaction is necessary to process an entire experiment of images. This eliminates fatigue related errors and user-related bias common to manual analysis. The method does not rely on stained images and handles images of variable quality. The algorithm is used to determine time and dose dependent, in vitro, neurotoxic effects of 1 GeV per nucleon iron particles in retinal explants. No neurotoxic effects are detected until 72 h after exposure; at 72 h, significant reductions of neurite outgrowth occurred at doses higher than 10 cGy. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Neurite outgrowth quantification; Explant; Image analysis; Retinal ganglion; High-LET; Neurotoxicity
1. Introduction The characterization of the central nervous system (CNS) as relatively radiation tolerant to low-linear energy transfer (LET) radiation exposure is one which has arisen from cell survival and/or morphological studies (Walden and Farzaneh, 1991). However, little is known regarding its sensitivity to high-LET particulate radiation. One method for assaying the effects of toxicity agents on the CNS exploits the quantitative assessment of the ability of isolated retinal explants to develop neurites (Carri and Ebendal, 1983; Hamdorf et al., 1992; Goldberg, 1987; Vazquez et al., 1994). Neurite outgrowth, considered one of the main components of synaptogenesis, has been well characterized both in vivo and in vitro, and can be considered an indicator of functional integrity (Purves and Lichtman, 1985). Neurite outgrowth is quantified typically by neurite length, neurite density, and the area occupied by the neurite outgrowth. Due to its noninvasive nature, neurite outgrowth assays utilize optical imaging of cultured tissues
* Corresponding author. Tel.: /1-631-632-8361; fax: /1-631-6328490. E-mail address: [email protected] (W.B. Lindquist).
as the method of choice for time dependent studies. While manual measurements of these parameters from digital images have been employed, such a procedure can be very time-consuming, involves subjective decisions, and is prone to fatigue-related bias. In many cases, explant experiments require only a rough manual assessment of neurite outgrowth to indicate overall experimental trends. The algorithm presented here is an attempt to automate this process, with the goal of obtaining more precise measurements that are independent of user-related bias. There have been several attempts to estimate neural growth parameters using computerized algorithms. Algorithms used for individual neuron analysis include skeletonization of the cell body (Matsumoto et al., 1990; Jap Tjoen San et al., 1991; Malgrange et al., 1994; Treubert and Bru¨mmendorf, 1998) and counting of neurite intersections (Ventimiglia et al., 1995; Isaacs et al., 1998; Rønn et al., 2000). While useful for individual cells, these algorithms do not work well with the complex neurite outgrowth seen in explants. Fewer methods for automatic quantification of neurite outgrowth from explants have been proposed. Ford-Holevinski et al. (1986) employ track finding pattern recognition algorithms on segmented images to reconstruct outgrowth. Ro¨sner and Vacun (1997) report use
0165-0270/03/$ - see front matter # 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0165-0270(03)00017-7
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of the OPTIMAS (Media Cybernetics, 1997) general image analysis software toolkit to analyze manually selected subregions of stained explant images. Bilsland describe a semi-automatic neurite density quantification using MCID (M5) image analysis software (Imaging Research, Inc., 1999), which employs manually determined thresholds to segment a stained image into explant, neurite, and background regions, and automatic pixel counting of each pixel type. A crucial step in these methods is the use of a single (global) threshold value to segment the image into neurite candidate and background pixels. This often limits the methods to stained cultures (Bilsland et al., 1999; Ro¨sner and Vacun, 1997). Staining is a non-vital technique, which kills tissue, thus preventing time series studies of the same tissue mass. While the degree of experimenter interaction varies, all the published explant methods require user action for each explant image. In this paper we present a computational method designed for explant outgrowth measurements which requires limited user interaction and is capable of handling unstained microscope images of variable quality. The method is based on the analysis of greyscale variation and avoids using a single threshold value to detect potential neurite pixels. User input is limited to setting some input parameters, which hold for an entire experimental set of images. Application of the method is demonstrated by conducting toxicity assays in order to determine the effects that 56Fe particle radiation have on neurite outgrowth in retinal explants. Our hypothesis is that the capacity of retinal ganglion cells, which are stimulated to regenerate neuronal processes by trophic factors, will be adversely affected by particle radiation in a dose and time dependent manner. The tissue preparation, dosage, and imaging techniques for this are briefly reviewed in Section 2.1. Manual analysis of a subset of the images is discussed in Section 2.2. Algorithms comprising the automated method are discussed in Section 2.3. In Section 3 the automated analysis results are compared against the manual analysis, and against previously published results (Vazquez and Kirk, 2000) on the dose /response relationship. New results for the time/dosage effects of 56Fe particle radiation on retinal ganglion cell outgrowth are also presented in Section 3. Discussion follows in Section 4.
2. Materials and methods 2.1. Chick embryo retinal explant culture and in vitro heavy ion irradiation Retinal tissue pieces were explanted from the eyes of 6-day-old (E6) chick donor embryos (White Leghorn) adapting a technique previously described (Carri and
Ebendal, 1986). The E6 embryos were dissected under sterile conditions in a modified Basal Medium Eagle with Earle’s salt (BME, Gibco Lab.) employing a stereo microscope placed in a laminar flow hood. After careful removal of the distal part of the eye and vitreous body, plugs of neural tissue were taken from an area of 1/2 mm radius surrounding the choroid fissure with a glass capillary attached to a micro-syringe. The pigmentary and mesenchyme-free neuroretinal explants were transferred onto hydrated collagen lattices placed in 35 mm Petri dishes. Collagen lattices (type I, 1.5 mg/ml) were prepared according to the technique described in Elsdale and Bard (1972). Cultures were incubated before treatment for 6/7 h at 37 8C in a 5% CO2 atmosphere. The culture medium consisted of BME, 5% fetal bovine serum, 5% horse serum (Gibco Lab.), 200 mM Lglutamine (Sigma) and 7.4% NaHCO3 (Sigma). Retinal explants were irradiated at room temperature with 56Fe26 ions (1 GeV per nucleon, LET 148 keV/ mm) accelerated at the Brookhaven National Laboratory Alternating Gradient Synchrotron. Details of the dosimetry system and beam characteristics have been published elsewhere (Zeitlin et al., 1998). During irradiation, the Petri dishes were oriented such that the collagen gel was proximal to the beam. The exposures were conducted at 6 /7 h post-explantation, before the onset of regenerative neurites occurred, and with a dose rate of 50 /100 cGy/min at the plateau position of the Bragg curve. After irradiation, the explants were washed with phosphate-buffered saline (PBS), re-cultured in fresh collagen lattices in 35 mm dishes (Nunc) or Permanox slide chambers (Lab-Tek), and incubated for 3 days. The culture media (BME) was supplemented with optic lobe extract from E18 chick embryo tectum at 500 mg/ml of protein concentration. This extract concentration has previously shown to be the optimal concentration for ganglion cell survival and neurite outgrowth in collagen gels (Carri and Ebendal, 1987). The cultured retinal explants were imaged at 24, 48 and 72 h after irradiation. Imaging was done with a Nikon Diaphot inverted microscope using pseudo-darkfield microscopy (phase contrast condenser ring with a non-phase objective) interfaced with a charged-couple device camera. Electronic images from explants were acquired and stored using a laboratory built-based system (Sutherland et al., 1997). The images were stored in uncompressed TIFF greyscale format and were typically 640 /480 pixels in size. 2.2. Manual analysis of explant cultures A subset of the images were manually analyzed. The manual analysis consisted of the following steps: contrast enhancement via IMAGETOOLS v 2.00 (University of Texas Health Science Center, 2000), tracing of the
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explant, and tracing of neurite pixel paths using magnified sections of the images combined with erasing of background pixels (via ADOBE PHOTOSHOP). Two measures of neurite growth were determined, neurite growth area (NGA) and maximum neurite length (MNL(10)) based upon the ten longest neurites. NGA is defined as neurite area (NA) divided by explant area (EA). The straight line distance from the border of the explant to the tip of the longest neurite or neurite bundle was determined at ten locations in each explant; MNL(10) is defined as the average of these ten length measurements.
2.3. Automated analysis of explant cultures From an algorithmic perspective, each digitized image is a greyscale pattern composed of integer intensity values S (i , j) in the range [0, 255], with 0 (respectively, 255) corresponding to minimum (respectively, maximum) light blockage. (As greyscale values are usually stored in TIFF files with 0 (respectively, 255) corresponding to black (respectively, white), a simple linear transformation is used to invert each image before applying our algorithm). Fig. 1(a) shows a typical explant image (after image inversion) to be analyzed. We assume each image contains a full view of a single explant, with possibly partial views of other explants. Analysis of the image consists of the following steps: the image is first contrast enhanced; the explant to be analyzed is detected and the edges of the explant tissue mass established; low intensity background is eliminated; neurites are traced using a ‘ridge’ tracking algorithm incorporating polar function analysis; and finally, NA and length measurements are stored for later synopsis. We present the specific details of these algorithmic steps below.
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2.3.1. Contrast enhancement The raw image is edge-contrast enhanced using a single pass of a standard 3/3 digitized Laplacian high pass filter (Pratt, 1991). As enhanced contrast of high curvature edges can produce intensity values outside the [0, 255] range, values exceeding range limits are truncated. 2.3.2. Explant detection The explant to be analyzed is assumed to be the largest, simply connected, light blocking object in the image. The explant is, therefore, determined to be the largest, four-connected set of pixels whose values lie in the range [Sexp, 255]. Sexp is a user set parameter; for the images analyzed in Section 3, we have found Sexp /254 satisfactory. Physically the explant must contain no holes (apparent holes in the explant may develop under high dosage experiments where cell death occurs). Once the explant has been located, any pixels having intensity value below Sexp that produce holes in the explant are re-identified as explant. The edge of the explant region is smoothed using a small surface feature filters */a series of passes of a majority filter using a 3 /3 window. Thick neurite bundles emerging from the explant region may also be identified as belonging to the explant. A series of passes of a protrusion detection filter employing a 7/7 window is used to eliminate protrusions of neurite width on the explant edge. Details on this filter can be found in Weaver (2003). In less than 5% of (some 1300) images analyzed to date, large scratches, partially viewed explants, or other sizeable dark regions in the image form a four-connected set of pixels in the range [Sexp, 255] that is larger than the explant of interest. To override explant misidentification in such cases, the user can input the coordinates of a single ‘seed’ pixel interior to the desired explant. In some images, two or more explants have outgrowth that overlaps. In such images the neurite tracing
Fig. 1. (a) A typical digitized microscope image from our study. (b) The sharpened image after ‘low background’ thresholding and imposition of a restricted circular region of analysis.
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algorithm may incorrectly assign neurite outgrowth from the second explant as outgrowth from the explant of interest. To avoid this possibility, the user may elect to have a circular region (centered on the center of mass of the explant of interest) established. The user inputs either the radius of the circle, or a point lying on the circumference of the desired circle. Only neurite growth within the circular region will be considered. Fig. 1(b) shows the sharpened image including establishment of a circular region of interest, computed for the image in Fig. 1(a). Such a circular region was imposed in approximately 25% of images analyzed. 2.3.3. Low intensity background removal Pixels with values sufficiently close to zero are assumed to be of no interest. All pixel values of the sharpened image S (i, j) in the range [0, Sbg) are ignored. The threshold Sbg is a user set parameter; in practice we have used Sbg /50. Note that Sbg is not intended to be employed as a global threshold value for determining potential neurite pixels, rather it is used to eliminate areas of no interest from consideration, thus speeding the computational algorithms. 2.3.4. Neurite tracing Neurite tracing occurs in a region V which is exterior to the designated explant; interior to the circular region (if one has been imposed); and excludes any regions declared to be low intensity background. In V , a neurite should be locally represented by a signal consisting of a roughly linear, eight-connected, string of pixels having values higher than the mean pixel value in the local area. This characteristic signal of a ‘ridge’ provides the basis for our neurite tracing algorithm. At the heart of our ridge tracing algorithm is a polar function Gij (u ) definable for any pixel (i, j). Consider a ray of specified length L starting at (i, j), pointing in the direction designated by the polar angle u . Define Gij (u ) as the image intensity averaged over a number of equally spaced points along this ray. If (i , j ) lies along a neurite track, its polar function Gij (u ) for 0 5/u 5/2p should pass the following test. 2.3.4.1. Polar test. 1. /maxu Gij (u)minu Gij (u)Rmin ;/ 2. Gij (u) has an absolute maximum and a separate local maximum at appropriately labeled angles uf and ub which satisfy jjuf/ubj/p jB/Du . The angle uf (respectively, ub) corresponds to the direction away from (respectively, towards) the explant of interest; Rmin and Du are user input parameters. For a pixel not on a neurite track, its polar function should fail this test. Fig. 2 compares plots of the polar functions for a neurite and a non-neurite pixel. As the polar function
is relatively expensive to compute, we require that a pixel pass a prescreening ‘ridge test’ before the polar test is run. 2.3.4.2. Ridge test. S (i, j) /Smed(i , j) where Smed(i, j) is the median value of the local signal intensity, and is computed as described in Weaver (2003). The neurite tracing algorithm must deal with the following topologies: a neurite may branch; due to the extended focal depth of the image, two or more neurites may appear to cross each other; a neurite may leave the focal volume and reappear some distance further. To accommodate these, our algorithm traces neurite segments. A segment may correspond to: an entire neurite, from explant to tip; one branch of a neurite, beginning from a neurite fork; or a neurite segment that appears in the focal volume at a distance from the explant. Tracing a segment involves three stages: determination of a starting pixel, tracing the segment, and verification of the segment as (part of) a neurite from the explant of interest. The search for neurite segment starting candidates in V begins at the explant edge and spirals outward. Since pixel intensity changes along any neurite, we begin a neurite segment trace only on a pixel having a strong signal. Thus each successive pixel (i, j ) on the search spiral, which has not already been assigned to a neurite segment, and which satisfies the ridge and polar tests, is considered a candidate for beginning a neurite segment trace. Let (i0, j0) be such a pixel. Since uf0 and ub0 provide directions in which neurite growth is indicated to exist, pixels in each of these directions are examined for continuation of the neurite segment trace. Without loss of generality, pixels in the direction uf0 are considered first. The neighboring pixel (i1 /i0/cos(uf0), j1 /j0/sin(uf0)), in the direction uf0 is then subjected to the ridge and polar tests. 2.3.4.3. Kink-angle test. If (i1, j1) passes both tests and a kink-angle test, jjub1 /uf0j/pj B/Du, which ensures that the ridge does not bend at too large an angle, then (i1, j1) is considered to be the continuation of the neurite segment in the direction uf0. uf1 provides the search direction to locate the next pixel candidate (i2 /i1/ cos(uf1), j2 /j1/sin(uf1)), along the track. Tracing of the neurite path continues if each successive pixel passes the ridge-, polar- and kink-angle tests. The neurite segment along the starting direction uf0 from (i0, j0) is traced as far as possible. We have implemented the following scheme for tracing across small ‘breaks’ in a neurite signal intensity. Assume tracking proceeds for l pixels from (i0, j0) to (il , jl ), and the pixel (il1 /il /cos(ufl1) jl1 /jl / sin(ufl1)) fails one of the three tests. The pixel (il2 / il /2 cos(ufl1) jl2 /jl /2 sin(ufl1)) is then tested as a
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Fig. 2. (a) The polar function Gij (u ) computed for a neurite (solid curve) and a non-neurite (dashed curve) pixel. The location of the two pixels chosen are indicated in (b). The angle u is measured counterclockwise, relative to the x direction.
possible neurite pixel. If it passes all three tests, the neurite segment is extended through (il1, jl1) to (il2, jl2), the polar function at (il2, jl2) is evaluated and tracking continues from (il2, jl2). If (il2, jl2) fails either test, extension is attempted for up to four more pixels (i.e. (il2, jl2)0/(il5, jl5)). If pixels (i1, j1)0/(i5, j5) all fail, the neurite track is terminated at (il , jl ). Once a segment-terminating pixel (im , jm ) is found, the entire tracing process is then repeated from (i0, j0) in the direction ub0 . When the traces in both directions terminate, a string of pixels: T f(in ; jn ); (in1 ; jn1 ); . . . ; (i0 ; j0 ); (i1 ; j1 ); . . . ; (im ; jm )g
of neurite growth through pixel (i0, j0) has been identified. The neurite segment T is then subjected to the following final acceptance tests: a neurite segment must either extend back to the explant; be identifiable as branched outgrowth from another neurite; or be identifiable as the continuation of an existing segment where the connecting path segment is too weak to be detectable in the tracing step. As we start tracing segments by testing pixels in sequence spiraling outward from the explant, it is sufficient to test segment T against previously accepted explant segments. Details on the acceptance tests can be found in Weaver (2003). If the acceptance tests fail, T is discarded as a segment unrelated to the explant under consideration. Accepted segments are referred to as ‘neurite outgrowth tracks’. All tracks are stored. After completing the spiral search through all pixels in V , the neurite outgrowth has been fully reconstructed. Fig. 3(a) illustrates the results of neurite tracing for the image in Fig. 1(a). Fig. 3(b) shows the results of manual tracing of neurites for the same explant. Qualitatively the results are very similar. Close inspection reveals the following differences. Compared with the automatic tracing, the manual result contains unerased background pixels and traces fainter neurites, often near
neurite tips. There are instances in different connectivity in neurite branches and differences in neurite detail near the explant.
3. Results We first present comparison of manual and automatic determinations. We note that the automated method was not designed to precisely duplicate the manual method outlined in Section 2.2. We are, therefore, interested in quantifying differences between the methods and ultimately in determining whether both methods capture the same trend. We note further that the manual analysis was completed before the automated analysis was designed. Manual measurements of NGA were performed on 120 explants from two experiments (E2 and E5) imaged at 72 h post irradiation. The images were split (79:41) between two expert analyzers. Manual analysis of these images involved approximately 120 man-hours of effort. The automated analysis of all 120 explants was performed on a 500 MHz Pentium III PC running the REDHAT LINUX 6.0 operating system. The software is written in C. The automated analysis took about 7 min per image. The large size of the data set ensures comparison with typical manual analysis results, avoiding ‘best image’ comparison bias. As Vazquez et al. (1994) predicts declining neurite growth as a function of irradiation dosage by 72 h post irradiation, comparison between automated and manual analyses was categorized both by expert and dosage. Separate measurements of EA and NA are involved in computing NGA. Fig. 4 summarizes the difference between the automatic and manual determinations of EA, NA and NGA. No trend with dosage is evident in the difference between automated and manual determinations of EA, NA or NGA for each expert.
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Fig. 3. (a) Automatic and (b) manual reconstruction of the explant image in Fig. 1(b). In (a) each of the ten longest neurites (which contribute to the neurite length parametrization MNL(10)) identified by the algorithm is marked with a letter.
Fig. 4. Comparison of differences between automatic and manual determinations of EA, NA, and NGA categorized by expert and radiation dosage. DEA/EAautomatic/EAmanual. DNA, and DNGA are defined analogously. Circles represent individual measurement differences; for each dose level, the mean and standard error are represented, respectively, by the horizontally offset ‘x’ and vertical error bars.
Mean values for NA, EA, and NGA for each dose/ experiment determined by automatic and manual measurement were tested for equality by paired t-tests. Table 1 summarizes the results of these tests (null hypothesis: equality of mean values between automatic and manual determinations) at the 5% level (two-tailed). Table 1 and Fig. 4 confirm systematic disagreement between manual and automatic determination of EA.
The disagreement results from systematic difference in differentiating the extent of the explant from the dense ‘layer’ of neurite outgrowth surrounding the explant edge. On average there is a factor of 2 in the mean value of the systematic difference in EA between the two manual analyses. Variances for the NA, EA, and NGA measurements were tested (null hypothesis: equality of variance of the automatic and manual determinations) using a test due to Pitman (Snedecor and Cochran, 1989) for correlated, paired measurements. Table 1 also summarizes the results for these tests at the 5% level (two-tailed). The results show that disagreement (as measured by the ttests) between manual and automatic mean area determination correlates very strongly with the occurrence of greater variance in the manual measurements than in the corresponding automatic measurements. Conversely, agreement between the two methods strongly correlates with the variance in the manual measurements being equal to or smaller than that in the automated measurements. We attribute lack of agreement between manual and automated analysis to greater variability in the manual measurements, and systematic differences in estimation of the explant size. Since consistent systematic differences will not affect trend determinations, our main comparison is directed at determining whether the automatic method predicts published dosage response trends. The radiation dosage response at 72 h based upon manual measurements of both NGA and MNL(10) for 78 explants was previously published in Vazquez and Kirk (2000). In Fig. 5 we present NGA and MNL(10) dose response curves at 72 h for a population of 78 explants from two experiments (E2 and E4) obtained using the automated analysis. Also plotted are the results from Vazquez and Kirk
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Table 1 Summary of comparison between automated and manual measurements for EA, NA, and NGA (NGA/NA/EA), categorized by person performing manual analysis and irradiation dosage Dose (cGy)
n
Equality of means (t -test, 5% level)
Equality of variance (Pitman, 5% level)
EA
NA
NGA
EA
NA
NGA
M1 0 10 50 100
33 27 9 10
No No No No
No No No Yes
No No No Yes
s2M /s2A s2M /s2A s2M /s2A s2M /s2A
s2M /s2A s2M /s2A s2M /s2A s2M /s2A
s2M /s2A s2M /s2A s2M /s2A s2M /s2A
M2 5 50 100
16 12 13
No No No
Yes Yes Yes
Yes No Yes
s2M /s2A s2M /s2A s2M /s2A
s2M B/s2A s2M /s2A s2M /s2A
s2M B/s2A s2M /s2A s2M /s2A
s2M (respectively, s2A) denotes variance of manual (respectively, automatic) measurements; n denotes number of images analyzed.
(2000). One-way analysis of variance (ANOVA) indicates rejection at the 5% level of the null hypothesis of no change in growth with dosage for all four (manual/ automatic, NGA/MNL) measured trends. Using a post hoc t -test, Vazquez and Kirk report significant (P B/ 0.05) growth reduction at 10 cGy and very significant (P B/0.01) reduction at 50 and 100 cGy when growth is measured as NGA. For the automatic analysis the post hoc Dunnett’s method was used to test for significant growth reduction. The automatic analysis finds no significant reduction at 10 cGy (the mean growth found at 10 cGy by automated analysis matches the manual mean, but the standard error of the automatic result is larger than for the manual measurements), but measures very significant (P B/0.01) growth reduction at 25, 50 and 100 cGy. For MNL(10) parametrization of growth,
the manual measurements only report significant (t-test, P B/0.01) growth reduction at a dose of 100 cGy. The automatic measurements report significant (Dunnett’s test, P B/0.01) growth reduction at 25, 50 and 100 cGy. To test whether a significant difference exists between the dose-response trends of the automatic and manual results, independent samples t-tests with the Bonferroni correction for multiple comparison were performed at each dose level. A significant difference (P B/0.05) was found only at 50 cGy, for both NGA and MNL. In Fig. 6 we present new results, based upon automated analysis of a single experiment (E4), of the dose response of neurite outgrowth as a function of time (24, 48 and 72 h post irradiation). ANOVA indicates no significant (P B/0.05) growth reduction with dosage at either 24 or 48 h using either NGA or MNL(10)
Fig. 5. Automated analysis of the dose response at 72 h post irradiation by 1 GeV per nucleon Fe ions as parametrized by (a) NGA and (b) MNL(10) for a sample of 78 retinal explants. Plotted is the mean (solid circle) and the standard error determined at each dosage, given as a percentage of the mean growth of the control (0 cGy) group. Entries marked with # and ## indicate significant deviation, P B/0.05 and B/0.01, respectively, from the control group, as measured by Dunnett’s method. Previously published results (Vazquez and Kirk, 2000) utilizing manual determinations of these measures for a data set of 78 explants are marked with an ‘x’. Published results entries marked with * and ** indicate significant deviation, P B/0.05 and B/0.01, respectively, as measured by a t -test. Numbers accompanying each data point indicate sample size governing each measurement.
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Fig. 6. Automated analysis of the time-dependent effects of the dosage of 1 GeV per nucleon Fe ions on (a) NGA and (b) MNL(10). Plotted is the mean and the standard error determined at each dosage level/time value. Seventy-two hours entries marked with ## indicate significant deviation (P B/0.01), as measured by Dunnett’s method, from the control (0 cGy) growth at the same time value. Numbers accompanying each NGA data point indicate sample size governing each measurement.
parametrization of the growth. By 72 h, significant (P B/ 0.01) growth reduction at 25, 50 and 100 cGy is determined by both parametrizations. (The sole exception is for the NGA per 100 cGy measurements where the sample size is very small. However, the larger data set (E2 and E4) reported in Fig. 5 does indicate significant NGA reduction at 100 cGy at 72 h).
4. Discussion Image-by-image comparison of the automated results with manual determinations for the 120 images analyzed by experts M1 and M2 show the following systematic tendencies of manual tracing relative to automated: detection of very faint neurites (especially at neurite tips); thicker neurites; a larger explant; and fatigue related incomplete erasing of background pixels. All four differences contribute to differences in manual versus automatic determinations of neural growth area, while only the first would generally contribute to differences in neurite length determination (note the tendency for the manual measurements of MNL(10) in Fig. 5 to be larger than the automatic measurements). Our results show superior consistency across images analyzed by the automated method. Manual analysis produces agreement with the automated analysis results when variability in manual measurements is reduced. The results shown in Fig. 5 support the conclusion that the automated analysis captures the same dose response trend as has been reported in the literature. As determined from the independent sample t -tests, the automatic and literature trends are similar; the only significant difference between automatic and manual measurements occurs for the 50 cGy dosage group (despite this difference, both methods do report a
significant growth reduction at 50 cGy as compared with the respective control groups). We note the automatic NGA and MNL(10) determinations of dose response are more consistent with each other than are the manual determinations. The automated method of neurite outgrowth quantification presented here removes two restrictions found in methods previously described. It does not require stained cultures, enabling the growth of a single explant to be followed in time, and it utilizes the greyscale information in the image rather than performing a global segmentation into neurite/non-neurite pixels. The algorithm requires relatively few parameters be set by the user. More significantly, these parameters will hold for an entire experiment of images, requiring no image-by-image user interaction. This provides a significant advantage in analysis speed. Finally, automated analysis of a large, time-series, data set indicates that significant growth response to dosage level does not appear until 72 h post irradiation by 1 GeV per nucleon Fe ions. The present study demonstrates that, in the retinal culture system, heavy ion exposure toxically affects retinal cells and inhibits the generation and elongation of neurite outgrowth elicited by optic lobe tissue extracts. These results suggest that iron ion exposure affects retinal ganglion cell functional integrity at low dose.
Acknowledgements This research was supported by the Swartz Foundation, by the US Department of Energy under Contracts Number DE-AC02-98CH10886 and DOE-LDRD 0112, and by NASA Life Sciences Division.
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References Bilsland J, Rigby M, Young L, Harper S. A rapid method for semiquantitative analysis of neurite outgrowth from chick DRG explants using image analysis. J Neurosci Methods 1999;92:75 /85. Carri NG, Ebendal T. Organotypic cultures of neural retina: neurite outgrowth stimulated by brain extracts. Brain Res Dev Brain Res 1983;6:219 /29. Carri NG, Ebendal T. A method for explantation of selected areas of the neural retina. Anat Rec 1986;214:226 /9. Carri NG, Ebendal T. Target-field specificity in the induction of retinal neurite outgrowth. Brain Res Dev Brain Res 1987;31:83 /90. Elsdale T, Bard J. Collagen substrata for studies on cell behavior. J Cell Biol 1972;54:626 /37. Ford-Holevinski T, Dahlberg TA, Agranoff BW. A microcomputerbased image analyzer for quantifying neurite outgrowth. Brain Res 1986;368:339 /46. Goldberg AM. Approaches to the development of in vitro neurotoxicological methods. Prog Clin Biol Res 1987;253:1 /11. Hamdorf G, Shahar A, Cervos-Navarro J, Skoberla A, Schieffler A. Irradiation neurotoxicity assessed in organotypic cultures of rat hippocampus. Neurobehav Toxicol 1992;13:165 /70. Imaging Research Inc., Brock University, Canada, 1999. http:// www.imagingresearch.com/products/MCID.asp. Isaacs KR, Hanbauer I, Jacobowitz D. A method for the rapid analysis of neuronal proportions and neurite morphology in primary cultures. Exp Neurol 1998;149:464 /7. Jap Tjoen San ERA, Schmidt-Michels MH, Spruijt BM, Oestreicher AB, Schotman P, Gispen WH. Quantitation of the growthassociated protein B-50/GAP-43 and neurite outgrowth in PC12 cells. J Neurosci Res 1991;29:149 /54. Malgrange B, Delre´e P, Rigo JM, Baron H, Moonen G. Image analysis of neuritic regeneration by adult rat dorsal root ganglion neurons in culture. J Neurosci Methods 1994;53:111 /22. Matsumoto T, Oshima K, Miyamoto A, Sakurai M, Goto M, Hayashi S. Image analysis of CNS neurotrophic factor effects on neuronal survival and neurite outgrowth. J Neurosci Methods 1990;31:153 / 62. Media Cybernetics, L.P., Silver Spring, MD, 1997. http://www.optimas.com/optimas.htm.
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Pratt WK. Digital Image Processing, second ed. New York: Wiley, 1991:519. Purves D, Lichtman JW. Principles of Neural Development. Massachusetts: Sunderland, 1985. Rønn LCB, Rallets I, Hartz PB, Bech M, Berezin A, Berezin B, Møller A, Bock E. A simple procedure for quantification of neurite outgrowth based on stereological principles. J Neurosci Methods 2000;100:25 /32. Ro¨sner H, Vacun B. Organotypic spinal cord culture in serum-free fibrin gel. J Neurosci Methods 1997;78:93 /103. Snedecor GW, Cochran WG. Statistical Methods, eighth ed. Ames: Iowa State University Press, 1989:192. Sutherland JC, Monteleone DC, Sutherland BM. Computer network for data acquisition, storage, and analysis. J Photochem Photobiol B 1997;40:14 /22. Treubert U, Bru¨mmendorf T. Functional cooperation of b1-integrins and members of the Ig superfamily in neurite outgrowth induction. J Neurosci 1998;18:1795 /805. University of Texas Health Science Center, San Antonio Dental School, 2000. http://www.ddsdx.uthscsa.edu/dig/itdesc.html. Vazquez ME, Kirk E. In vitro neurotoxic effects of 1 GeV/nucleon iron particles assessed in retinal explants. Adv Space Res 2000;25:2041 /9. Vazquez ME, Broglio TM, Worgul BV, Benton E. Neuritogenesis: a model for space radiation effects on the central nervous system. Adv Space Res 1994;14:467 /74. Ventimiglia R, Jones BE, Møller A. A quantitative method for morphometric analysis in neuronal cell culture: unbiased estimation of neuron area and number of branch points. J Neurosci Methods 1995;57:63 /6. Walden TL, Farzaneh NK. Biochemical response of normal tissues to ionizing radiation. In: Gutin PH, Leibel SA, Sheline GE, editors. Radiation Injury to the Nervous System. New York: Raven Press, 1991:17 /36. Weaver C. Automated morphology of neural cells. Ph.D. thesis, State University of New York at Stony Brook, 2003. In preparation. Zeitlin C, Heilbronn L, Miller J. Detailed characterization of the 1087 MeV/nucleon iron-56 beam used for radiobiology at the alternating gradient synchrotron. Radiat Res 1998;149:560 /9.
An algorithm for neurite outgrowth reconstruction Christina M. Weaver a, John D. Pinezich b, W. Brent Lindquist a,*, Marcelo E. Vazquez c a
Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA b Advanced Acoustic Concepts, Hauppauge, NY 11788, USA c Medical Department, Brookhaven National Laboratory, Upton, NY 11973, USA Received 2 October 2002; received in revised form 14 January 2003; accepted 14 January 2003
Abstract We present a numerical method which provides the ability to analyze digitized microscope images of retinal explants and quantify neurite outgrowth. Few parameters are required as input and limited user interaction is necessary to process an entire experiment of images. This eliminates fatigue related errors and user-related bias common to manual analysis. The method does not rely on stained images and handles images of variable quality. The algorithm is used to determine time and dose dependent, in vitro, neurotoxic effects of 1 GeV per nucleon iron particles in retinal explants. No neurotoxic effects are detected until 72 h after exposure; at 72 h, significant reductions of neurite outgrowth occurred at doses higher than 10 cGy. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Neurite outgrowth quantification; Explant; Image analysis; Retinal ganglion; High-LET; Neurotoxicity
1. Introduction The characterization of the central nervous system (CNS) as relatively radiation tolerant to low-linear energy transfer (LET) radiation exposure is one which has arisen from cell survival and/or morphological studies (Walden and Farzaneh, 1991). However, little is known regarding its sensitivity to high-LET particulate radiation. One method for assaying the effects of toxicity agents on the CNS exploits the quantitative assessment of the ability of isolated retinal explants to develop neurites (Carri and Ebendal, 1983; Hamdorf et al., 1992; Goldberg, 1987; Vazquez et al., 1994). Neurite outgrowth, considered one of the main components of synaptogenesis, has been well characterized both in vivo and in vitro, and can be considered an indicator of functional integrity (Purves and Lichtman, 1985). Neurite outgrowth is quantified typically by neurite length, neurite density, and the area occupied by the neurite outgrowth. Due to its noninvasive nature, neurite outgrowth assays utilize optical imaging of cultured tissues
* Corresponding author. Tel.: /1-631-632-8361; fax: /1-631-6328490. E-mail address: [email protected] (W.B. Lindquist).
as the method of choice for time dependent studies. While manual measurements of these parameters from digital images have been employed, such a procedure can be very time-consuming, involves subjective decisions, and is prone to fatigue-related bias. In many cases, explant experiments require only a rough manual assessment of neurite outgrowth to indicate overall experimental trends. The algorithm presented here is an attempt to automate this process, with the goal of obtaining more precise measurements that are independent of user-related bias. There have been several attempts to estimate neural growth parameters using computerized algorithms. Algorithms used for individual neuron analysis include skeletonization of the cell body (Matsumoto et al., 1990; Jap Tjoen San et al., 1991; Malgrange et al., 1994; Treubert and Bru¨mmendorf, 1998) and counting of neurite intersections (Ventimiglia et al., 1995; Isaacs et al., 1998; Rønn et al., 2000). While useful for individual cells, these algorithms do not work well with the complex neurite outgrowth seen in explants. Fewer methods for automatic quantification of neurite outgrowth from explants have been proposed. Ford-Holevinski et al. (1986) employ track finding pattern recognition algorithms on segmented images to reconstruct outgrowth. Ro¨sner and Vacun (1997) report use
0165-0270/03/$ - see front matter # 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0165-0270(03)00017-7
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of the OPTIMAS (Media Cybernetics, 1997) general image analysis software toolkit to analyze manually selected subregions of stained explant images. Bilsland describe a semi-automatic neurite density quantification using MCID (M5) image analysis software (Imaging Research, Inc., 1999), which employs manually determined thresholds to segment a stained image into explant, neurite, and background regions, and automatic pixel counting of each pixel type. A crucial step in these methods is the use of a single (global) threshold value to segment the image into neurite candidate and background pixels. This often limits the methods to stained cultures (Bilsland et al., 1999; Ro¨sner and Vacun, 1997). Staining is a non-vital technique, which kills tissue, thus preventing time series studies of the same tissue mass. While the degree of experimenter interaction varies, all the published explant methods require user action for each explant image. In this paper we present a computational method designed for explant outgrowth measurements which requires limited user interaction and is capable of handling unstained microscope images of variable quality. The method is based on the analysis of greyscale variation and avoids using a single threshold value to detect potential neurite pixels. User input is limited to setting some input parameters, which hold for an entire experimental set of images. Application of the method is demonstrated by conducting toxicity assays in order to determine the effects that 56Fe particle radiation have on neurite outgrowth in retinal explants. Our hypothesis is that the capacity of retinal ganglion cells, which are stimulated to regenerate neuronal processes by trophic factors, will be adversely affected by particle radiation in a dose and time dependent manner. The tissue preparation, dosage, and imaging techniques for this are briefly reviewed in Section 2.1. Manual analysis of a subset of the images is discussed in Section 2.2. Algorithms comprising the automated method are discussed in Section 2.3. In Section 3 the automated analysis results are compared against the manual analysis, and against previously published results (Vazquez and Kirk, 2000) on the dose /response relationship. New results for the time/dosage effects of 56Fe particle radiation on retinal ganglion cell outgrowth are also presented in Section 3. Discussion follows in Section 4.
2. Materials and methods 2.1. Chick embryo retinal explant culture and in vitro heavy ion irradiation Retinal tissue pieces were explanted from the eyes of 6-day-old (E6) chick donor embryos (White Leghorn) adapting a technique previously described (Carri and
Ebendal, 1986). The E6 embryos were dissected under sterile conditions in a modified Basal Medium Eagle with Earle’s salt (BME, Gibco Lab.) employing a stereo microscope placed in a laminar flow hood. After careful removal of the distal part of the eye and vitreous body, plugs of neural tissue were taken from an area of 1/2 mm radius surrounding the choroid fissure with a glass capillary attached to a micro-syringe. The pigmentary and mesenchyme-free neuroretinal explants were transferred onto hydrated collagen lattices placed in 35 mm Petri dishes. Collagen lattices (type I, 1.5 mg/ml) were prepared according to the technique described in Elsdale and Bard (1972). Cultures were incubated before treatment for 6/7 h at 37 8C in a 5% CO2 atmosphere. The culture medium consisted of BME, 5% fetal bovine serum, 5% horse serum (Gibco Lab.), 200 mM Lglutamine (Sigma) and 7.4% NaHCO3 (Sigma). Retinal explants were irradiated at room temperature with 56Fe26 ions (1 GeV per nucleon, LET 148 keV/ mm) accelerated at the Brookhaven National Laboratory Alternating Gradient Synchrotron. Details of the dosimetry system and beam characteristics have been published elsewhere (Zeitlin et al., 1998). During irradiation, the Petri dishes were oriented such that the collagen gel was proximal to the beam. The exposures were conducted at 6 /7 h post-explantation, before the onset of regenerative neurites occurred, and with a dose rate of 50 /100 cGy/min at the plateau position of the Bragg curve. After irradiation, the explants were washed with phosphate-buffered saline (PBS), re-cultured in fresh collagen lattices in 35 mm dishes (Nunc) or Permanox slide chambers (Lab-Tek), and incubated for 3 days. The culture media (BME) was supplemented with optic lobe extract from E18 chick embryo tectum at 500 mg/ml of protein concentration. This extract concentration has previously shown to be the optimal concentration for ganglion cell survival and neurite outgrowth in collagen gels (Carri and Ebendal, 1987). The cultured retinal explants were imaged at 24, 48 and 72 h after irradiation. Imaging was done with a Nikon Diaphot inverted microscope using pseudo-darkfield microscopy (phase contrast condenser ring with a non-phase objective) interfaced with a charged-couple device camera. Electronic images from explants were acquired and stored using a laboratory built-based system (Sutherland et al., 1997). The images were stored in uncompressed TIFF greyscale format and were typically 640 /480 pixels in size. 2.2. Manual analysis of explant cultures A subset of the images were manually analyzed. The manual analysis consisted of the following steps: contrast enhancement via IMAGETOOLS v 2.00 (University of Texas Health Science Center, 2000), tracing of the
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explant, and tracing of neurite pixel paths using magnified sections of the images combined with erasing of background pixels (via ADOBE PHOTOSHOP). Two measures of neurite growth were determined, neurite growth area (NGA) and maximum neurite length (MNL(10)) based upon the ten longest neurites. NGA is defined as neurite area (NA) divided by explant area (EA). The straight line distance from the border of the explant to the tip of the longest neurite or neurite bundle was determined at ten locations in each explant; MNL(10) is defined as the average of these ten length measurements.
2.3. Automated analysis of explant cultures From an algorithmic perspective, each digitized image is a greyscale pattern composed of integer intensity values S (i , j) in the range [0, 255], with 0 (respectively, 255) corresponding to minimum (respectively, maximum) light blockage. (As greyscale values are usually stored in TIFF files with 0 (respectively, 255) corresponding to black (respectively, white), a simple linear transformation is used to invert each image before applying our algorithm). Fig. 1(a) shows a typical explant image (after image inversion) to be analyzed. We assume each image contains a full view of a single explant, with possibly partial views of other explants. Analysis of the image consists of the following steps: the image is first contrast enhanced; the explant to be analyzed is detected and the edges of the explant tissue mass established; low intensity background is eliminated; neurites are traced using a ‘ridge’ tracking algorithm incorporating polar function analysis; and finally, NA and length measurements are stored for later synopsis. We present the specific details of these algorithmic steps below.
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2.3.1. Contrast enhancement The raw image is edge-contrast enhanced using a single pass of a standard 3/3 digitized Laplacian high pass filter (Pratt, 1991). As enhanced contrast of high curvature edges can produce intensity values outside the [0, 255] range, values exceeding range limits are truncated. 2.3.2. Explant detection The explant to be analyzed is assumed to be the largest, simply connected, light blocking object in the image. The explant is, therefore, determined to be the largest, four-connected set of pixels whose values lie in the range [Sexp, 255]. Sexp is a user set parameter; for the images analyzed in Section 3, we have found Sexp /254 satisfactory. Physically the explant must contain no holes (apparent holes in the explant may develop under high dosage experiments where cell death occurs). Once the explant has been located, any pixels having intensity value below Sexp that produce holes in the explant are re-identified as explant. The edge of the explant region is smoothed using a small surface feature filters */a series of passes of a majority filter using a 3 /3 window. Thick neurite bundles emerging from the explant region may also be identified as belonging to the explant. A series of passes of a protrusion detection filter employing a 7/7 window is used to eliminate protrusions of neurite width on the explant edge. Details on this filter can be found in Weaver (2003). In less than 5% of (some 1300) images analyzed to date, large scratches, partially viewed explants, or other sizeable dark regions in the image form a four-connected set of pixels in the range [Sexp, 255] that is larger than the explant of interest. To override explant misidentification in such cases, the user can input the coordinates of a single ‘seed’ pixel interior to the desired explant. In some images, two or more explants have outgrowth that overlaps. In such images the neurite tracing
Fig. 1. (a) A typical digitized microscope image from our study. (b) The sharpened image after ‘low background’ thresholding and imposition of a restricted circular region of analysis.
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algorithm may incorrectly assign neurite outgrowth from the second explant as outgrowth from the explant of interest. To avoid this possibility, the user may elect to have a circular region (centered on the center of mass of the explant of interest) established. The user inputs either the radius of the circle, or a point lying on the circumference of the desired circle. Only neurite growth within the circular region will be considered. Fig. 1(b) shows the sharpened image including establishment of a circular region of interest, computed for the image in Fig. 1(a). Such a circular region was imposed in approximately 25% of images analyzed. 2.3.3. Low intensity background removal Pixels with values sufficiently close to zero are assumed to be of no interest. All pixel values of the sharpened image S (i, j) in the range [0, Sbg) are ignored. The threshold Sbg is a user set parameter; in practice we have used Sbg /50. Note that Sbg is not intended to be employed as a global threshold value for determining potential neurite pixels, rather it is used to eliminate areas of no interest from consideration, thus speeding the computational algorithms. 2.3.4. Neurite tracing Neurite tracing occurs in a region V which is exterior to the designated explant; interior to the circular region (if one has been imposed); and excludes any regions declared to be low intensity background. In V , a neurite should be locally represented by a signal consisting of a roughly linear, eight-connected, string of pixels having values higher than the mean pixel value in the local area. This characteristic signal of a ‘ridge’ provides the basis for our neurite tracing algorithm. At the heart of our ridge tracing algorithm is a polar function Gij (u ) definable for any pixel (i, j). Consider a ray of specified length L starting at (i, j), pointing in the direction designated by the polar angle u . Define Gij (u ) as the image intensity averaged over a number of equally spaced points along this ray. If (i , j ) lies along a neurite track, its polar function Gij (u ) for 0 5/u 5/2p should pass the following test. 2.3.4.1. Polar test. 1. /maxu Gij (u)minu Gij (u)Rmin ;/ 2. Gij (u) has an absolute maximum and a separate local maximum at appropriately labeled angles uf and ub which satisfy jjuf/ubj/p jB/Du . The angle uf (respectively, ub) corresponds to the direction away from (respectively, towards) the explant of interest; Rmin and Du are user input parameters. For a pixel not on a neurite track, its polar function should fail this test. Fig. 2 compares plots of the polar functions for a neurite and a non-neurite pixel. As the polar function
is relatively expensive to compute, we require that a pixel pass a prescreening ‘ridge test’ before the polar test is run. 2.3.4.2. Ridge test. S (i, j) /Smed(i , j) where Smed(i, j) is the median value of the local signal intensity, and is computed as described in Weaver (2003). The neurite tracing algorithm must deal with the following topologies: a neurite may branch; due to the extended focal depth of the image, two or more neurites may appear to cross each other; a neurite may leave the focal volume and reappear some distance further. To accommodate these, our algorithm traces neurite segments. A segment may correspond to: an entire neurite, from explant to tip; one branch of a neurite, beginning from a neurite fork; or a neurite segment that appears in the focal volume at a distance from the explant. Tracing a segment involves three stages: determination of a starting pixel, tracing the segment, and verification of the segment as (part of) a neurite from the explant of interest. The search for neurite segment starting candidates in V begins at the explant edge and spirals outward. Since pixel intensity changes along any neurite, we begin a neurite segment trace only on a pixel having a strong signal. Thus each successive pixel (i, j ) on the search spiral, which has not already been assigned to a neurite segment, and which satisfies the ridge and polar tests, is considered a candidate for beginning a neurite segment trace. Let (i0, j0) be such a pixel. Since uf0 and ub0 provide directions in which neurite growth is indicated to exist, pixels in each of these directions are examined for continuation of the neurite segment trace. Without loss of generality, pixels in the direction uf0 are considered first. The neighboring pixel (i1 /i0/cos(uf0), j1 /j0/sin(uf0)), in the direction uf0 is then subjected to the ridge and polar tests. 2.3.4.3. Kink-angle test. If (i1, j1) passes both tests and a kink-angle test, jjub1 /uf0j/pj B/Du, which ensures that the ridge does not bend at too large an angle, then (i1, j1) is considered to be the continuation of the neurite segment in the direction uf0. uf1 provides the search direction to locate the next pixel candidate (i2 /i1/ cos(uf1), j2 /j1/sin(uf1)), along the track. Tracing of the neurite path continues if each successive pixel passes the ridge-, polar- and kink-angle tests. The neurite segment along the starting direction uf0 from (i0, j0) is traced as far as possible. We have implemented the following scheme for tracing across small ‘breaks’ in a neurite signal intensity. Assume tracking proceeds for l pixels from (i0, j0) to (il , jl ), and the pixel (il1 /il /cos(ufl1) jl1 /jl / sin(ufl1)) fails one of the three tests. The pixel (il2 / il /2 cos(ufl1) jl2 /jl /2 sin(ufl1)) is then tested as a
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Fig. 2. (a) The polar function Gij (u ) computed for a neurite (solid curve) and a non-neurite (dashed curve) pixel. The location of the two pixels chosen are indicated in (b). The angle u is measured counterclockwise, relative to the x direction.
possible neurite pixel. If it passes all three tests, the neurite segment is extended through (il1, jl1) to (il2, jl2), the polar function at (il2, jl2) is evaluated and tracking continues from (il2, jl2). If (il2, jl2) fails either test, extension is attempted for up to four more pixels (i.e. (il2, jl2)0/(il5, jl5)). If pixels (i1, j1)0/(i5, j5) all fail, the neurite track is terminated at (il , jl ). Once a segment-terminating pixel (im , jm ) is found, the entire tracing process is then repeated from (i0, j0) in the direction ub0 . When the traces in both directions terminate, a string of pixels: T f(in ; jn ); (in1 ; jn1 ); . . . ; (i0 ; j0 ); (i1 ; j1 ); . . . ; (im ; jm )g
of neurite growth through pixel (i0, j0) has been identified. The neurite segment T is then subjected to the following final acceptance tests: a neurite segment must either extend back to the explant; be identifiable as branched outgrowth from another neurite; or be identifiable as the continuation of an existing segment where the connecting path segment is too weak to be detectable in the tracing step. As we start tracing segments by testing pixels in sequence spiraling outward from the explant, it is sufficient to test segment T against previously accepted explant segments. Details on the acceptance tests can be found in Weaver (2003). If the acceptance tests fail, T is discarded as a segment unrelated to the explant under consideration. Accepted segments are referred to as ‘neurite outgrowth tracks’. All tracks are stored. After completing the spiral search through all pixels in V , the neurite outgrowth has been fully reconstructed. Fig. 3(a) illustrates the results of neurite tracing for the image in Fig. 1(a). Fig. 3(b) shows the results of manual tracing of neurites for the same explant. Qualitatively the results are very similar. Close inspection reveals the following differences. Compared with the automatic tracing, the manual result contains unerased background pixels and traces fainter neurites, often near
neurite tips. There are instances in different connectivity in neurite branches and differences in neurite detail near the explant.
3. Results We first present comparison of manual and automatic determinations. We note that the automated method was not designed to precisely duplicate the manual method outlined in Section 2.2. We are, therefore, interested in quantifying differences between the methods and ultimately in determining whether both methods capture the same trend. We note further that the manual analysis was completed before the automated analysis was designed. Manual measurements of NGA were performed on 120 explants from two experiments (E2 and E5) imaged at 72 h post irradiation. The images were split (79:41) between two expert analyzers. Manual analysis of these images involved approximately 120 man-hours of effort. The automated analysis of all 120 explants was performed on a 500 MHz Pentium III PC running the REDHAT LINUX 6.0 operating system. The software is written in C. The automated analysis took about 7 min per image. The large size of the data set ensures comparison with typical manual analysis results, avoiding ‘best image’ comparison bias. As Vazquez et al. (1994) predicts declining neurite growth as a function of irradiation dosage by 72 h post irradiation, comparison between automated and manual analyses was categorized both by expert and dosage. Separate measurements of EA and NA are involved in computing NGA. Fig. 4 summarizes the difference between the automatic and manual determinations of EA, NA and NGA. No trend with dosage is evident in the difference between automated and manual determinations of EA, NA or NGA for each expert.
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Fig. 3. (a) Automatic and (b) manual reconstruction of the explant image in Fig. 1(b). In (a) each of the ten longest neurites (which contribute to the neurite length parametrization MNL(10)) identified by the algorithm is marked with a letter.
Fig. 4. Comparison of differences between automatic and manual determinations of EA, NA, and NGA categorized by expert and radiation dosage. DEA/EAautomatic/EAmanual. DNA, and DNGA are defined analogously. Circles represent individual measurement differences; for each dose level, the mean and standard error are represented, respectively, by the horizontally offset ‘x’ and vertical error bars.
Mean values for NA, EA, and NGA for each dose/ experiment determined by automatic and manual measurement were tested for equality by paired t-tests. Table 1 summarizes the results of these tests (null hypothesis: equality of mean values between automatic and manual determinations) at the 5% level (two-tailed). Table 1 and Fig. 4 confirm systematic disagreement between manual and automatic determination of EA.
The disagreement results from systematic difference in differentiating the extent of the explant from the dense ‘layer’ of neurite outgrowth surrounding the explant edge. On average there is a factor of 2 in the mean value of the systematic difference in EA between the two manual analyses. Variances for the NA, EA, and NGA measurements were tested (null hypothesis: equality of variance of the automatic and manual determinations) using a test due to Pitman (Snedecor and Cochran, 1989) for correlated, paired measurements. Table 1 also summarizes the results for these tests at the 5% level (two-tailed). The results show that disagreement (as measured by the ttests) between manual and automatic mean area determination correlates very strongly with the occurrence of greater variance in the manual measurements than in the corresponding automatic measurements. Conversely, agreement between the two methods strongly correlates with the variance in the manual measurements being equal to or smaller than that in the automated measurements. We attribute lack of agreement between manual and automated analysis to greater variability in the manual measurements, and systematic differences in estimation of the explant size. Since consistent systematic differences will not affect trend determinations, our main comparison is directed at determining whether the automatic method predicts published dosage response trends. The radiation dosage response at 72 h based upon manual measurements of both NGA and MNL(10) for 78 explants was previously published in Vazquez and Kirk (2000). In Fig. 5 we present NGA and MNL(10) dose response curves at 72 h for a population of 78 explants from two experiments (E2 and E4) obtained using the automated analysis. Also plotted are the results from Vazquez and Kirk
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Table 1 Summary of comparison between automated and manual measurements for EA, NA, and NGA (NGA/NA/EA), categorized by person performing manual analysis and irradiation dosage Dose (cGy)
n
Equality of means (t -test, 5% level)
Equality of variance (Pitman, 5% level)
EA
NA
NGA
EA
NA
NGA
M1 0 10 50 100
33 27 9 10
No No No No
No No No Yes
No No No Yes
s2M /s2A s2M /s2A s2M /s2A s2M /s2A
s2M /s2A s2M /s2A s2M /s2A s2M /s2A
s2M /s2A s2M /s2A s2M /s2A s2M /s2A
M2 5 50 100
16 12 13
No No No
Yes Yes Yes
Yes No Yes
s2M /s2A s2M /s2A s2M /s2A
s2M B/s2A s2M /s2A s2M /s2A
s2M B/s2A s2M /s2A s2M /s2A
s2M (respectively, s2A) denotes variance of manual (respectively, automatic) measurements; n denotes number of images analyzed.
(2000). One-way analysis of variance (ANOVA) indicates rejection at the 5% level of the null hypothesis of no change in growth with dosage for all four (manual/ automatic, NGA/MNL) measured trends. Using a post hoc t -test, Vazquez and Kirk report significant (P B/ 0.05) growth reduction at 10 cGy and very significant (P B/0.01) reduction at 50 and 100 cGy when growth is measured as NGA. For the automatic analysis the post hoc Dunnett’s method was used to test for significant growth reduction. The automatic analysis finds no significant reduction at 10 cGy (the mean growth found at 10 cGy by automated analysis matches the manual mean, but the standard error of the automatic result is larger than for the manual measurements), but measures very significant (P B/0.01) growth reduction at 25, 50 and 100 cGy. For MNL(10) parametrization of growth,
the manual measurements only report significant (t-test, P B/0.01) growth reduction at a dose of 100 cGy. The automatic measurements report significant (Dunnett’s test, P B/0.01) growth reduction at 25, 50 and 100 cGy. To test whether a significant difference exists between the dose-response trends of the automatic and manual results, independent samples t-tests with the Bonferroni correction for multiple comparison were performed at each dose level. A significant difference (P B/0.05) was found only at 50 cGy, for both NGA and MNL. In Fig. 6 we present new results, based upon automated analysis of a single experiment (E4), of the dose response of neurite outgrowth as a function of time (24, 48 and 72 h post irradiation). ANOVA indicates no significant (P B/0.05) growth reduction with dosage at either 24 or 48 h using either NGA or MNL(10)
Fig. 5. Automated analysis of the dose response at 72 h post irradiation by 1 GeV per nucleon Fe ions as parametrized by (a) NGA and (b) MNL(10) for a sample of 78 retinal explants. Plotted is the mean (solid circle) and the standard error determined at each dosage, given as a percentage of the mean growth of the control (0 cGy) group. Entries marked with # and ## indicate significant deviation, P B/0.05 and B/0.01, respectively, from the control group, as measured by Dunnett’s method. Previously published results (Vazquez and Kirk, 2000) utilizing manual determinations of these measures for a data set of 78 explants are marked with an ‘x’. Published results entries marked with * and ** indicate significant deviation, P B/0.05 and B/0.01, respectively, as measured by a t -test. Numbers accompanying each data point indicate sample size governing each measurement.
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Fig. 6. Automated analysis of the time-dependent effects of the dosage of 1 GeV per nucleon Fe ions on (a) NGA and (b) MNL(10). Plotted is the mean and the standard error determined at each dosage level/time value. Seventy-two hours entries marked with ## indicate significant deviation (P B/0.01), as measured by Dunnett’s method, from the control (0 cGy) growth at the same time value. Numbers accompanying each NGA data point indicate sample size governing each measurement.
parametrization of the growth. By 72 h, significant (P B/ 0.01) growth reduction at 25, 50 and 100 cGy is determined by both parametrizations. (The sole exception is for the NGA per 100 cGy measurements where the sample size is very small. However, the larger data set (E2 and E4) reported in Fig. 5 does indicate significant NGA reduction at 100 cGy at 72 h).
4. Discussion Image-by-image comparison of the automated results with manual determinations for the 120 images analyzed by experts M1 and M2 show the following systematic tendencies of manual tracing relative to automated: detection of very faint neurites (especially at neurite tips); thicker neurites; a larger explant; and fatigue related incomplete erasing of background pixels. All four differences contribute to differences in manual versus automatic determinations of neural growth area, while only the first would generally contribute to differences in neurite length determination (note the tendency for the manual measurements of MNL(10) in Fig. 5 to be larger than the automatic measurements). Our results show superior consistency across images analyzed by the automated method. Manual analysis produces agreement with the automated analysis results when variability in manual measurements is reduced. The results shown in Fig. 5 support the conclusion that the automated analysis captures the same dose response trend as has been reported in the literature. As determined from the independent sample t -tests, the automatic and literature trends are similar; the only significant difference between automatic and manual measurements occurs for the 50 cGy dosage group (despite this difference, both methods do report a
significant growth reduction at 50 cGy as compared with the respective control groups). We note the automatic NGA and MNL(10) determinations of dose response are more consistent with each other than are the manual determinations. The automated method of neurite outgrowth quantification presented here removes two restrictions found in methods previously described. It does not require stained cultures, enabling the growth of a single explant to be followed in time, and it utilizes the greyscale information in the image rather than performing a global segmentation into neurite/non-neurite pixels. The algorithm requires relatively few parameters be set by the user. More significantly, these parameters will hold for an entire experiment of images, requiring no image-by-image user interaction. This provides a significant advantage in analysis speed. Finally, automated analysis of a large, time-series, data set indicates that significant growth response to dosage level does not appear until 72 h post irradiation by 1 GeV per nucleon Fe ions. The present study demonstrates that, in the retinal culture system, heavy ion exposure toxically affects retinal cells and inhibits the generation and elongation of neurite outgrowth elicited by optic lobe tissue extracts. These results suggest that iron ion exposure affects retinal ganglion cell functional integrity at low dose.
Acknowledgements This research was supported by the Swartz Foundation, by the US Department of Energy under Contracts Number DE-AC02-98CH10886 and DOE-LDRD 0112, and by NASA Life Sciences Division.
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