Automated image analysis in autoradiography

Experimental Cell Research 68 (197 1) 388-394

AUTOMATED

1MAGE ANALYSIS

IN AUTORADIOGRAPHY

W. PRENSKY Tufts Unioersity

School

of Mecticin:,,Department

qf Physiology,

Boston,

Mass.

02111,

USA

SUMMARY Light microscope autoradiographs can be analysed with the aid of the Quantimet, a commercially available image scanning instrument. Standardization of the performance of the instrument was obtained by performing on-line data reduction and display with a small digital computer. The number of silver grains over individual cells can be rapidly and routinely determined, and excellent agreement can be achieved between visually determined and machine generated grain counts.

The usefulness of instrumentation to aid in the acquisition of quantitative data from autoradiographs was recognized relatively early [1, 21, and a number of instruments to generate grain count data were described [3-61. Although none of these instruments appear to have been ineffective, they have found neither wide acceptance nor wide application in biological research. It appears that their intrinsic shortcomings were due either to slow speed of operation because of the requirement of relatively elaborate microscope adjustments for the analysis of each cell, or to the limited range of sample material which could be successfully examined with their aid. To be generally useful, an automated system for counting grains should perform much faster than the unaided human eye, and its capabilities should approach the human eye in the types of autoradiographs which can be analysed with its aid. In this communication a system for automated grain counting is described which is largely based on commercially available components, is rapid in operation, can handle a wide variety of grain

densities, and is flexible enough to be useful in the analysis of other quantitative histological problems as well. MATERIALS Instrumentation

AND METHODS

and programming

Our system is based on the Quantimet (QTM), the operating principles of which have been described by others [7, 81. It is an “image analysing computer” which accepts information from a television camera connected to a light microscope (fig. I). Primary discrimination between different features of an image depends on differences in their optical density. The threshold level at which discrimination takes place can be set by observing the television monitor, which provides a display of the image and an overlay indicating the features which are being detected and evaluated. Successive scans can be programmed to evaluate the number, size distribution, area, and projection of the detected features. Projection, as defined by the hardware of the QTM, consists of the number of scan lines which are intersected by a detected feature. Fig. 2 illustrates the appearance of the monitor screen when three successive projection scans are performed. The numerical values generated by the QTM are represented by an overlay of white dots next to the silver grains and each of the scans is set to evaluate features of different width. The first scan (P,) intersects the three grain clusters four times. The second scan (PI) discriminates against features whose width is less than two grains, and only two intersects are recorded for the same cluster. In the third scan the cluster is rejected entirely. The sum of

Automated image analysis in autoradiography

F/g. 1. Modified QTM system for analysing biological samples. consisting of: Scanner, Quantimet, Model R, with Data Multiplexing Device, Metals Research Ltd., Cambridge, UK; Data processor, PDP-8/L, Digital Equipment Corporation, Maynard, Mass.; Optics, Zeiss photomicroscope with additional beam splitter in reflecting system, 60-watt tungsten illuminator, photochanger, and TV adapter, Carl Zeiss, New York. Interfaces and QTM cycle control circuit constructed by Mr Brock Dew, Watertown, Mass., USA. the scans estimates the size of the cluster, and the factors necessary for calculating the grain count of a cell are therefore given by the equation CC a (X” x1 x2 x3) b (1) where CC is the estimated grain count, and the values of x,, through xQare the raw data generated by successive QTM scanseach set to detect clusters of increasing width. The value of a depends on magnification, and both n and b can be determined by multiple regression analysis of the data from cells for which both visual and QTM observations are available. The instrument controls which achieve the discrimination shown in fig. 2 are set by observing the monitor screen, and need adjustment only when microscope magnification is altered. The optical density of underlying cells can vary due to differences in cell mass, flatness, and staining, and the threshold sensitivity of the instrument must be periodically adjusted by the operator on the basis of the subjective appearance of the monitor screen. It was therefore necessary that this element of human judgement be subject to objective evaluation. This was provided for by entering the raw data from the QTM into a small digital computer (fig. I) which was programmed to calculate the grain count and present it on a digital display. When necessary, the QTM estimate can be compared to a manually obtained estimate of the grain count. The two simultaneously available feedbacks, the appearance of the monitor screen and the display of the calculated grain count, as shown below, provide the necessary elements for obtaining reproducible threshold settings in terms of the visually determined grain count.

The autoradiographs used in this study were prepared from cells of L 1210 leukemia which had been grown

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intraperitoneally in CD2F 1 mice and labeled by one local injection of tritiated thymidine (5 /Xi/per mouse, 6.7 Ci/mM). Freshly collected cells were suspended in 0.15 M NaCl and diluted to 150 000 cells/ml. Slides were prepared with the aid of a Shandon cytofuge (Shandon Scientific Co., London), and two drops of the suspension gave a flat, wellspread cellular preparation. This step was critical since other methods did not yield consistently reproducible cell densities or equally flat preparations. Optimum density greatly speeded up the task of sampling the cell population, and the absence of crowding resulted in a uniform degree of flattening of the cells. The silver grains over a flattened cell are mostly in a 0.5 /tm layer of emulsion above the cell, simplifying the problem of focusing the microscope. The cells were post-fixed in I : 3 acetic acid-alcohol and stained with the Feulgen reaction. Autoradiographs were prepared with NTB liquid emulsion (Eastman Kodak, Rochester), using standard procedures. To reduce the optical density of heavily stained cells, some observations were made with light filtered through a cuvette containing a solution of the dye used in preparing the Feulgen stain.

QTM Display. Successive Scans

‘\

\

\

P ‘12 \ t

\

\

Scan #

1 Mini’mum Chord Setting

Fig. 2. Display of projection values on QTM monitor screen. Each average sized silver grain is intersected by two scan lines. When grains are clumped into aggregates the size of the aggregate can be determined by adding the results of different scans, each scan being based on different minimum chord size settings are preset on the basis of observed aggregates and the threshold detection level varied as necessary. Exptl Cdl Res 68

390

W. Prensky

Fig. 3. Abscissa: threshold detection level setting; ordinate: relative Quantimet determination. Effect of incremental changes in the detection threshold on the magnitude of the measurements generated by the QTM. A feature distinguished by its contrast is either detected or not detected depending on its “grey” level. The grey level above which detection starts is determined by the threshold control. (A) Evaluation of the number of silver grains over a cell. The cell had 46 grains, and the QTM values given are relative to the visually determined grain count. The projection values are based on text eq. (I); (B) measurement of a field containing two Giemsa stained cells. The area and projection values were expressed relative to the middle of their plateau region

RESULTS AND DISCUSSION Instrument settings and capabilities Each silver grain is about 0.3 pm in diameter, and up to 130 could be found over a cell having a diameter of lo-15 pm. The size of the grains is close to the limit of resolution of the light microscope, requiring the use of oil immersion optics. Silver grains in an emulsion are much more opaque to light than the underlying biological material in an autoradiograph. However, because of their small size they do not seem to have a natural boundary as far as the scanning system of the QTM is concerned. Fig. 3.4 illustrates the numerical evaluation of a labeled cell as a function of the threshold sensitivity setting of the QTM. The grain count was estimated from “area” and “projection”, two parameters supplied by the QTM. The absence of a plateau in the slopes of either of the curves indicates that even minor changes in optical density of underlying cells would distort the accuracy of the calculated grain count, and valid estimates can thereExptl Cell Res 68

fore only be obtained by adjusting the threshold sensitivity on the basis of the appearance of the overlay on the television monitor. That this is mainly due to the small size of the silver grains is illustrated by similar data obtained from evaluating the area of a Giemsa stained cell (fig. 3B). There is a plateau in the magnitude of the measurements produced by the QTM when the threshold level is approximately correct. It is less difficult therefore to obtain reproducible data for calculating the size or number of objects larger than silver grains. Grain count calculation from QTM duta The QTM provides a choice of a number of parameters (area, projection, particle count), and the choice of projection was made on the basis of repeated linear regression analyses of the different parameters. A sample of 87 cells was used whose visually determined counts ranged from 5 to 122 grains/cell. The cells were arbitrarily divided into three classes based on their labeling intensity, and a separate set of coefficients was calculated

Automated image analjlsis ill autoradiography Table 1. Goodness of fit analysis of Quantimet data Comparison of expected versus observed grain numbers Chi-Square goodness of fit test Grains/cells

No. of cells

Value

Pb. c

All cells 5520 21-40 41-122

87 I9 25 43

60.50 4.22 4.69 39.63

0.90 0.99 0.99 0.50

i Determined by visual observation. Probability of obtaining a greater value than the one recorded. ’ A p value smaller than 0.05 would indicate that observed deviations from the visual count are greater than could be accounted by chance alone.

for each class. Calculated grain counts, based on the different parameters were then compared with the visual counts. Table 1 presents a chi-square goodness of fit analysis of the data based on eq. (1). The calculated grain counts were extremely close to the visually determined counts when there were fewer than 40 grains/cell. When cells had more than 40 grains it was more difficult to calculate a close estimate of the visual grain count, as shown by the smaller p value (0.50) associated with this class of cells. However, since thep value is considerably larger than 0.05, it is clear that statistically significant estimates of the grain count can be obtained even for the more densely labeled cells. When the number of silver grains per cell is low, or when the grains are uniformly distributed over the cell, accurate estimates of the grain count can be obtained in a number of different ways. Therefore, in selecting the equation for use in estimating the grain count one has to consider primarily those features of the QTM which would tend to improve the accuracy of the estimated grain count

391

under the marginal conditions encountered when examining heavily labeled cells with extensive clumping of silver grains. Silver grains, on the average, have the same area, and the area determined by the QTM can be divided by a constant to obtain an estimate of the grain count. However, at higher grain densities, closer estimates of the count were obtained from QTM determinations of projection rather than determinations of area or particle number. A combination of parameters was also used, but resulted in trivial improvements, if any, in the estimate of the grain count. The main advantage of using projection for the xi variables given in eq (1) is that they depend on threshold and minimum chord size settings whose accuracy can be easily checked by visual observation of the monitor screen, greatly simplifying the operation of the instrument. Routine applications Once it was demonstrated that the raw data from the QTM can be converted to an acceptable estimate of the grain count, two other problems had to be solved. One was to insure a uniform and reproducible interpretation of the clues provided by the monitor screen. As the grain density increased, interpretation of the display on the monitor screen became increasingly subjective, and lack of a means of standardizing the settings of the instrument seemed to be the primary barrier to the successful utilization of the instrument in our application. The second problem had to do with the relatively large amount of raw data which had to be recorded for later reduction into the desired estimates of the grain count. The QTM system operated at a much higher speed than the data recording facilities available for it so that overall operating speed was significantly reduced by the amount of time necessary for recording the Exptl Cell Res 68

392

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W. Prenskl

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100

120

Fig. 4. Abscissa: no. of grains-visually determined counts; ordinate: no. of grains-calculated (QTM) counts. O, slides analysed simultaneously. l , slides scored manually prior to analysis with QTM. Comparison of visual grain counts with counts calculated by the PDP-8. (A) Individual cell comparisons; (B) Comparison of population averages.

data. This problem was further aggravated when we found it desirable to record data from redundant scans, which increased the number of items recorded from each cell. We found that both of the above problems could either be solved or minimized by performing an on-line reduction of the raw QTM data with the aid of a small digital computer. We interfaced the QTM to a PDP-8, which in turn was attached to a digital meter, so that values obtained by the PDP-8 could be displayed on the meter under program control (fig. 1). Thus, after a cell is manually selected and the instrument settings are checked by the operator, the QTM scans the image, the raw data which it generates are read into the PDP-8, a grain count is obtained by eq (I), and the result is displayed on the meter and also stored in the computer memory for later use. The automated part of the operation lasts about 0.5 set, so that overall speed of operation is a function of the time necessary to perform the required manual steps. Programming of the system was greatly facilitated by the development of appropriate assembly language subroutines for addition to Exorl Cell Res 68

FOCAL. FOCAL provides a simple means of programming arithmetic statements, teletypewriter input and output, and several other necessary control functions. The functions added by us regulate data input from the QTM, display values on the digital meter, and facilitate storage of the acquired grain count data in the form of a histogram within the memory of the computer. We compared visual and estimated counts under conditions designed to test the performance of the operator. Periodic visual counts were taken, and later compared with machine determined estimates. Fig. 4A shows the concordance obtained for individual cells. The calculated regression line is close to an ideal line which should have a .I’ intercept of zero and a slope of 1. The scatter about the regression line is random, and the QTM data can therefore be used for determining mean grain counts of cell populations. Fig. 4B shows a comparison of mean grain counts for a series of slides. The data shown include 5 means obtained visually while operating the QTM (c), and 7 means in which the manual observations were obtained by a different observer (0). The last set of

Automated image analysis in autoradiography data is most indicative of the performance of the QTM, since our purpose in using grain counts is to estimate the radioactivity of the labeled cells within a population. DISCUSSION Visual grain counting is a tedious operation when a great amount of it is to be done. We found that our system could be operated with minimum operator fatigue, and efficiency is not quickly impaired as when grain counting is done manually. Since data acquisition and analysis is also more rapid, large blocks of data can be expeditiously processed with the aid of the QTM. The system described above makes use of the ability of the human observer to perform a rapid and reproducible qualitative evaluation of the image seen on the television monitor. To eliminate this step would require the construction of a more complex computer based system, in which the scanner produces an optical density reading for each of the points within the area of interest. Such systems have been built for the computer analysis of chromosomes [9-131, and for the analysis of cell mass by microspectrophotometry [14]. In all cases both the computational methods and the equipment specifications are more complex than in our system. Furthermore, the time necessary to analyze a single cell is longer than is warranted by the application we are interested in. Simultaneous availability of both the monitor display and the calculated grain count which is based on that display greatly simplified the problem of personnel training, system standardization, and managerial supervision of the data gathering process. Both the objective and subjective results of the evaluation procedure are visible to several observers, and the differences in judgement between them can therefore be easily resolved.

393

For training and standardization purposes the operator sets the instrument according to his judgement and permits a reading to be taken. A visual grain count is then made and is compared to the computer estimate. Since the monitor screen provides a display which changes with variation of the instrument controls, it is relatively easy to learn the appearance of the display which is associated with an accurate grain count determination. Once this is learned, operation of the system is very rapid. The digital computer was found to be essential for the provision of efficient and flexible means of data acquisition, interpretation, and display. The ease of programming in FOCAL made it possible not only to perform the minimum required tasks but also to add routines to our operating program for on-line data reduction and analysis, which greatly extended the usefulness of the system. Since the FOCAL programs are easily modifiable, the system can be used for other applications as well. Recently the QTM has been used for measuring the histological changes in lung epithelium brought about by atmospheric pollution [15]. The report noted the difficulty of ascertaining the correspondence between visual and instrument counts. The latter problem is therefore not unique to our application, it is merely more severe because of the small size of the silver grains. We found that on-line data reduction aided not only in insuring the acquisition of reproducible data, but also in the evolution of the methodology for doing so, and should therefore be useful in other applications of image processing of histological preparations as well. The author thanks Dr Walter L. Hughes, Tufts University School of Medicine, Boston, Mass., for aid and encouragement in the development of the grain counting system. He is indebted to Mr David Low for aid with computer applications, to Mr Exptl Cell Res 68

394 W. Prensky Brock Dew for designing the interfaces, and to Mr Robert Giebitz for operating the QTM. This study was supported by grant T-495 from the American Cancer Society, grant no. 6699 from the National Science Foundation to Wolf Prensky, and by a National Institutes of Health grant to Dr Walter L. Hughes, grant no. CA 10735. Mice used in the study were obtained from the Mammalian Genetics and Animal Production Section, NCI, Bethesda, Md. REFERENCES I. Rogers, A W, Techniques of autoradiography. Elsevier, Amsterdam (I 967). 2. Dudley, R A & Pelt, S R, Nature 172 (1953) 992. 3. Gullberg, J E, Lab invest 8 (1959) 94. 4. Tolles, W E, Lab invest 8 (1959) 99. 5. Rogers, A W, Exptl cell res 24 (1961) 228.

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6. Altman, J, J histochem cytochem I I (1963) 741. 7. Cole, M, Microscopic I5 (1966) 148. 8. Fisher, C, Proceedings of the particle size analysis conference (Society for analytical chemistry, London) p. 1200 (I 967). 9. Ledley, R S, Science I46 ( 1964) 2 16. IO. Rutovitz, D, Brit med bull 24 (1968) 260. II. Mendelsohn, M L et al., .4nn NY acad sci 157 ( 1969) 376. 12. Neurath, P W, Brand, D H & Schrinor, E D, Ann NY acad sci I57 (1969) 324. 13. Ruddle, F, Smith i $, Ledley, R S & Belson, M, Ann NY acad sci 157 (1969) 400. 14. Stein, P G, Lipkin, L E & Shapiro, M M, Science 166 (1969) 328. 15. Mawdesley-Thomas, L E & Healey, P, Science 163 (1969) 1200. Received November 24, 1970 Revised version received April 20, 1971