MICROSCOPY RESEARCH AND TECHNIQUE 21:347-354 (1992)
Computer Assisted Data Collection for Stereology: Rationale and Description of Point Counting Stereology (PCS) Software N. DEAN PENTCHEFF AND ROBERT P. BOLENDER Department o f Integrative Biology, University of California, Berkeley, California 94720 (N.D.P.1; Department of Biological Structure, University of Washington, School of Medicine, Seattle, Washington 98195 (R.P.B.)
KEY WORDS
Quantitative morphology, Morphometry, Light microscopy, Electron microscopy, PCS System 111, MS-DOS, UNIX
ABSTRACT The paper describes microcomputer software for point counting stereology. Stereology includes a collection of statistical methods that quantify the images of light and transmission electron microscopy. The methods use test grids placed over images to collect raw data, which includes counts of points, intersections, transections, and profiles. In turn, the counts are included in stereological equations that give estimates of compartmental volumes, surfaces, lengths, or numbers. These parameters describe the composition of a structure in three-dimensional space. The PCS (point counting stereology) System Software I11 serves as a data collection, storage, and management tool. Users set up point counting protocols without programming, enter data by pressing predefined function (MS-DOS) or alphabetic keys (UNIX), store data in files, select files for analysis, and calculate results as stereological densities. The latest version of the PCS software includes a new user interface and is designed as a research “front end” that can feed data either into the calculation tools of a stereology tutorial (Bolender, 1992, this issue) or into the analysis routines of quantitative morphology databases (Bolender and Bluhm, 1992). o 1992 Wiley-Liss, Inc. COMPUTER ASSISTED DATA COLLECTION The central aim in data collection for stereology is to count or measure features of interest on images of twodimensional sections. There are two generally useful methods of doing this. The first is manual or semimanual point counting. In this method, raw data are collected by overlaying images with test grids or rulers (DeHoff and Rhines, 1968; Weibel, 1979). The investigator records the raw data based on direct observation of the test areas. In the other method, the images are submitted to automatic analysis by a computer system. The computer, through the use of specialized hardware and software, collects data and tallies the results. These methods have been reviewed by Bolender (1986) and Jarvis (this issue). Automatic Image Analysis Systems Automatic image analysis systems offer the appealing possibility of replacing repetitive (and expensive) manual counting with automatic measurement. With currently available image analysis hardware and software, a n individual image can be measured much faster than with manual methods. Each image can also be sampled much more densely than is common in manual systems, giving a higher precision for measurements on each image. Rather than the subjective assessment of microscopic features that is inherent in manual approaches, the computer program uses a set of objective (or at least reproducible) standards to decide what to measure. These standards, of course, must be developed by the investigator for each new study. This
0 1992 WILEY-LISS, INC.
objectivity can also permit the use of workers with less biological training, yielding a potential savings in labor costs. As one might expect, automatic image analysis systems have some drawbacks. The most obvious is the large initial expense, both in buying the basic hardware and software and in developing the specialized analysis algorithms for each study. High speed image analysis hardware is expensive ($8,000-$12,000 or higher), and the programming personnel needed to adapt the software for each new study are highly skilled, and therefore add to the cost. Compared with manual systems, it also takes much longer to set up and debug a measurement protocol. The image-sampling algorithms must be designed, programmed, debugged, and then validated against manual measurements. Another problem lies in the images required by automated systems. Because such systems typically rely on relatively simple grey-scale or texture analysis to identify features of interest, the image quality must be highly reproducible. An automatic image analysis system is inherently less able to compensate for variable image quality than is a n experienced investigator.
Received December 15, 1990; accepted in revised form January 14, 1991. Address reprint requests to N. Dean Pentcheff, Department of Biology, University of South Carolina, Columbia, SC 29208.
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Perhaps the most important drawback is that the high precision with which automated systems can sample images is unnecessary. The precision of final stereological estimates can be improved much more by sampling more animals (and blocks) than by sampling more of each micrograph (Gundersen and Osterby, 1981; Matheiu et al., 1981).
Computer Assisted Point Counting Traditionally, stereological measurements have been done manually by overlaying images with test grids. An investigator records counts manually, based on direct observation. The key advantage this method has over automated systems is the use of a trained investigator to identify and count the features of interest. The human visual system is remarkably good at recognizing complex patterns and a t dealing with images of variable quality (e.g., Marr, 1982). Of course, the advantage of using a trained observer has its accompanying problem: the measurements must be made by observers who have received appropriate training. For automated systems, achieving accurate recognition of features in biological images is very d i f f i c u l t especially for transmission electron microscopy. Since this is simple for computer assisted systems (the investigator does the recognition), setting up a working measurement protocol is much quicker. In the data collection phase, point counting stereology is becoming increasingly efficient a s a result of recent advances in sampling (Gundersen et al., 1988). For example, the volume density of a biological compartment can be estimated with a few hundred points (Cruz-Orive and Weibel, 1990) a task that can be completed in a few minutes. Using point counting software, the time spent collecting data from images with test grids therefore accounts for only a minor part of a n experimental protocol. Domains of Automatic Image Analyzers and Point Counting Methods The respective qualities of automatic image analysis systems and manual point counting methods suggest appropriate applications for each. In situations where large numbers of similar specimens need to be evaluated, automatic systems may have a n advantage. Their typically high initial cost can be offset by long-term savings in personnel time and consistency of results. Pathology laboratories and clinical screening applications represent such situations (see, for example, Jarvis, 1992, this issue). In research applications involving standard light and electron microscopy, point counting methods remain the method of choice. Since the goal of researchers is to examine new phenomena, i t is unlikely that they will be processing large numbers of similar specimens. Research laboratories typically examine different tissues with different techniques through time. Given the high initial investment in setting up a n image processing protocol, it is likely that point counting methods will remain more economical-at least for the near future.
Aspects of Point Counting That Can Be Computer Assisted How can manual point counting stereology be enhanced using computers? At least three areas can be identified: data collection, data management, and data analysis. Data Collection. Using a microcomputer with a specialized counting program offers several advantages over paper and pencil or mechanical counters. The programmable and interactive aspects of a computer program enable i t to teach or prompt the user, rather than passively accepting information. With a n appropriate point counting program, the counting protocol (including test grid specifications, features to be counted, measurements to be made, etc.) can be quickly specified for each investigation. Since the protocol defines the input data, the chances of omission or error are decreased. The physical accumulation of data is also eased with a computer. By using the computer as a counter, the program can allow immediate correction of errors. Running totals and statistical summaries can be made available on line, rather than after hand calculation. Data Analysis. By collecting data with a microcomputer, the numbers are stored in a form that can be used directly for data analysis. Preliminary analyses can provide immediate information on the progress of a n investigation and allow a researcher to identify problems arising from the counting protocols. Data Management. Once data have been collected, they need to be put in a form that allows for quick access and archiving. Data in the form of computer disk files fulfill both requirements. Multiple copies of individual data files can be produced and stored in separate locations to protect against data loss. Data files stored on a hard disk can be recalled a t any time for further analysis. POINT COUNTING STEREOLOGY (PCS) SOFTWARE, OLD AND NEW PCS-I In the early 198Os, Bolender and coworkers developed the PCS-I (Point Counting for Stereology I) program for biological stereology (Bolender et al., 1982). For each micrograph, the user visually scanned a n overlaid test grid and recorded the point intersection, transection, or profile counts by pressing user-defined counter keys on a Tektronix 4051 or 4052 microcomputer. Fundamentally, this method of data entry has been preserved throughout the PCS series of programs. In the data collection phase, PCS-I accumulated and totaled the counts for each compartment, computed calibration constants, provided graphical summaries of the cumulative errors in each compartment, and stored the raw data on tape cartridges. In the data analysis phase, the program evaluated stereological equations for volume, surface, and length densities and included provisions for grouping and randomizing the data from individual micrographs. PCS-I provided a welcome automation of steps which had previously been manually (and laboriously) performed. Unfortunately, PCS-I was not a “user-friendly” pro-
PCS SYSTEM SOFTWARE
gram. Calibration procedures and data entry were somewhat puzzling and unforgiving of mistakes. Moreover, the method of identifying files often created confusion, and changing the counting protocols and analyses required reprogramming. There were other problems. PCS-I was implemented on the Tektronix 4051 and 4052 microcomputers which, though advanced for their time, had several disadvantages. They had a single, built-in programming language, Tektronix BASIC, making structured programming difficult. The language and the tape cartridge storage format were incompatible with most other machines. Also, the graphical display was based on storage tube technology. This meant that changing any part of the screen required rewriting the entire display, making it impossible to write interactive screen routines. PCS System I In early 1983, the original PCS-I software was rewritten into a new group of programs called PCS System I (Pentcheff and Bolender, 1985). We set four design goals: 1) users should be able to set up counting protocols without reprogramming, 2) the analysis modules should have the capability of using data from one or more files, even when data were collected with different test grids or at different magnifications, 3) the density module should display the stereological equations it was evaluating, and 4) a n analysis module should be added that provided cell counts and average cell data. Following modern principles of structured programming (Kernighan and Plaugher, 1978), we split the program functions into modules. Data collection, statistics, and stereological analyses were embodied in separate programs. The programs, however, still ran on obsolete equipment (Tektronix 4050 series microcomputers) and were still written in a primitive, nonportable language (Tektronix BASIC). Attempts to maintain a strictly structured program broke down in the ongoing attempt to add new features to the developing programs. The ultimate result was a set of programs with thousands of lines of poorly commented, convoluted code. In a well-intentioned but misguided effort to limit coding to the most portable aspects of Tektronix BASIC, the graphical error analysis routine of PCS-I was relegated to a set of prewritten statistical programs from Tektronix. This essentially eliminated the useful on-line statistical error analysis.
PCS System I1 By the time PCS System I was complete, we were already planning the next version, PCS System I1 (Pentcheff and Bolender, 1987). Beyond the original goals of PCS System I, we added the following: 1) the program code and the data file format should be portable to a wide variety of computers, and 2) the interactive data collection facilities should be improved, since the target machines now used rewritable screens (standard computer displays). For development, we chose the MS-DOS (PC-DOS) operating system on a n IBM PC microcomputer using
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the C programming language (Pentcheff, 1987). The code could be compiled and run on both UNIX-based microcomputers and IBM PC compatible microcomputers, rather than PCS System 1’s single-system limitation. Since then, the code has also been ported to the Macintosh operating system by Malcolm Pradhan. In contrast to the machine-specific binary format of PCS System I, the data files used by PCS System I1 consisted of ASCII characters. This conferred two important benefits. First, data manipulation and analysis programs already available under the MS-DOS and UNIX operating systems could be used with the data files. Second, ASCII characters could be sent over domestic and international computer networks without any special processing. Given the growing importance of communication and data exchange via computer networks, this was a n important concern for scientific data storage. For PCS System 11, the Counting Module (for data collection) was thoroughly redesigned for ease of use and speed. The on-line cumulative statistical error display was restored. To preserve the portability of the code, the statistical display was implemented as a numerical rather than a graphical display. PCS System I1 had problems. The two analysis modules (Density Module and Average Cell (B-Bar) Module) were transliterated directly from the Tektronix BASIC version of PCS System I into the C language of PCS System I1 without redesign. Therefore, they did not receive the benefits of a n improved user interface and their code was difficult to debug and maintain. PCS System 111 PCS System I1 was essentially a n interim program, bridging the time between PCS System I and the development of a fully redesigned system. By the time PCS System I11 was being designed, the importance of linking data from multiple scientific studies was beginning to be emphasized (Morowitz and Smith, 1987). The orientation of PCS System I11 has therefore changed from that of its predecessors. Rather than being a self-contained system, PCS System I11 is now seen as a research “front end” (see Table 1) that can feed new primary data (stereological densities) into either the calculation tools of a stereology tutorial (Bolender, 1992, this issue) or into the analysis routines of quantitative morphology databases (Bolender and Bluhm, 1992). The design goals for PCS System I11 included collection and storage of raw data and indexing information, on-line statistics, preliminary data analysis, portability of data to other computer programs, and a new user interface. Design and Operation of PCS System I11 Data Collection (Counting Module). The Counting Module has changed little since the redesign that accompanied PCS System 11. There are three functional areas within the program: the Indexing Screen, the heading of the Counting Screen, and the body of the Counting Screen. In addition, a Help Screen listing the functions and keypresses of the program can be displayed at any time.
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N.D. PENTCHEFF AND R.P. BOLENDER TABLE 1 . Components of a three-part computer assisted system for quantitutiue morphology'
PCS System 111 Count Module B-Bar Module Density Module (Other analyses)
Stereology tutorial Tutorial modules on sampling, data collection, stereol6gicil - analysis, and data interpretation 'l'opics Introduction to quantitative morphology Symbols and terms Data types Sampling Information hierarchies Data interpretation Statistics
Quantitative morphology databases Intemated database nf . .ouantitative m&phological data from multiple sereological studies Topics Cell data Orgadtissue data Standardization of data Structural patterns Histological generalization ~
'Data flow through the programs from left to right. PCS System 111 functions a s the data collection and preliminary analysis front end. The stereology tutorial performs other analyses as well as its educational and training functions. The databases allow a specific study's analysis to integrate the study's new data (collected with PCS System 111) with the results of other studies in the literature.
PCS SYSTEM I11 POINT COUNTING MODULE RELEASE 1.00 N. DEAN PENTCHEFF AND ROBERT P. BOLENDER DEPARTMENT OF BIOLOGICAL STRUCTURE UNIVERSITY OF WASHINGTON (press the key marked 'Home' for help)
.~ ~
Length of calibration spaces (L) in cms Number of calibration spaces (N) in length L Calibration standard (C) in spaces/cm Magnification (M) (M=L*C/N)
10. 10. 100000. 100000.000000
Fig. 1. The indexing screen of the Counting Module of PCS System 111. The sample data shown here have been recalled from a data file saved with PCS System I. The magnification is automatically calculated based on the calibration information.
The Indexing Screen (Fig. 1) records identifying information about the data set. The final magnification of the micrographs is calculated based on the calibration information entered by the user (see Bolender and Pentcheff, 1985). The heading of the Counting Screen (Fig. 2) is used to describe the specific data being collected from a given set of images (e.g., micrographs). The user can insert up to 30 data entry columns, each of which is used to accumulate counts from a specific compartment. The heading area records the abbreviated name of each compartment (including its reference compartment) as well as the characteristics of the test grid used to count it. Information about the type of count (point, intersection, transection, or number), the grid con-
stants, and the magnification is stored by the counting module in files and used by the analysis modules to calculate stereological densities. The body of the Counting Screen (Fig. 2) is used to accumulate the counts. Each line corresponds to the data of a single image (micrograph).All data in a given data file come from a set of images taken at the same magnification, and each file may include up to 145 lines of data. Counts are accumulated by pressing the function keys of PC computers (terminals on UNIX minicomputers use alphabetic keys). Each time a key is pressed, the counter in that key's column for the current row is incremented by one. To correct errors or add data collected by other means, numbers may be typed directly into the data entry fields of the Counting Screen.
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PCS SYSTEM SOFTWARE
I 36 2. 2. I
omim EXC F7 105 14 29 4 40 18
The lower half of the screen contains sample data collected from five micrographs. Additional lines are inserted as needed. If necessary, the lower half of the screen will automatically scroll vertically and the data columns will automatically scroll horizontally to display the active location.
Fig. 2. The counting screen of the Counting Module of PCS System 111. The top half of the screen contains information describing the compartment being counted and the characteristics of the grid used to count it. The line labelled SUM can be used to display the sum of counts, number of counts, average count, standard deviation of counts, or percent coefficient of variation of counts for each column.
Options -
Analyze I
I
Directory: .\*.pcs Choose a data file or files to read: h0612c.pcs pancla.qcs panc2a.pcs panc2e.pcs data.pcs h0612d.pcs panclb.pcs panc2b.pcs testl.pcs h0612a.pcs h0614a.pcs panclc.pcs pa-nc2c.pcs test2.pcs h0612b.pcs h0614b.pcs pancld.pcs panc2d.pcs Select a file by pressing 'Ins' or ' + I . Unselect by pressing 'Dell or '-I. Press 'Return' to select file and quit. Press 'Esc' to quit without selecting file. Fig. 3. The Density Module can read one or more data files at a time, selected from a menu of the available files. The "NEW M A S K option allows the user to change the current directory or the file types that are included in the menu display.
While the counts accumulate, summary statistics of all lines in the file are calculated and displayed. The row labelled SUM can be switched to display any of the following statistics for each column: sum of counts, number of rows (empty fields are treated a s missing, not zeros), average counts, standard deviation
of counts, and percent coefficient of variation of counts. All statistics are updated whenever a datum changes. Once all the data for a series of micrographs are entered, they are saved in a data file in ASCII format. Data files can be read by the analysis modules (de-
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TABLE 2. Density Module command menus’ File Info on density Read PCS file Files read Print Choose output destination Print file summary Print constructed compartment summary Print analysis Print prior analysis Print next analysis Print all subsequent analyses Stop printing analyses Save analysis Enter raw data DOS escape Quit Options Combine compartments Combine point (P) compartments Combine intersection (I) compartments Combine transection (T) compartments Combine number (N) compartments Create “OTHER compartment Delete constructed compartment Equation format Select output units Change all units Change volume units Change surface units Change length units Analyze Volume density Surface density Length density Density summary table Other ratios Surface reference Leneth reference ‘The commands in boldface constitute the pulldown menu at the top of the screen. The other menus appear as submenus beneath them.
scribed below) or recalled by the Counting Module for reviewing, editing, or adding new data.
Data Management PCS System I11 data files are standard operating system files. Since they contain ASCII characters, any word processor or editor can be used to examine or modify the contents of a file. To export PCS System I11 data files, the user selects the Converter Module. At present, it rearranges the data into a format appropriate for use by a spreadsheet program such as Lotus 1-2-3 (Cambridge, MA) or Microsoft Excel (Redmond, WA). In turn, these commercial programs have links to many other software packages. Analysis Modules Stereological Densities. The Density Module performs standard stereological calculations of volume, surface, and length densities. It has two modes of operation: interactive and noninteractive. The interactive mode is useful for training and preliminary data analysis. Through a system of pulldown menus, the user chooses to include or delete data files from the
analysis and then selects an analysis routine. Each analysis ends with a display of equations and the stereological result. Once an analysis protocol has been designed and tested interactively, the program can be run in noninteractive mode through a script of commands. This mode generates results automatically. The following discussion addresses some aspects of the interactive mode of the Density Module. Table 2 shows the menu commands available to the user. The Density Module allows the user to select one or more data files, previously collected with the Counting Module. Data files are selected from a menu of the files available on disk, illustrated in Figure 3. The magnification information stored in each file is used to scale the incoming data (all internal calculations are done in absolute units using meters). The algorithms used to do the stereological analyses are able to combine data files even when data were collected with different test grids and magnifications. Once one or more data files have been chosen, a stereological analysis can be selected. In the following example, we show how to make a surface density estimate. The user first selects the surface density analysis and then chooses a surface area compartment to estimate (Fig. 4a) and one or more reference compartments to serve as the reference volume (Fig. 4b). After these variables are selected, the program displays the equations and the results (Fig. 5). This “building block” type of analysis is easy to learn and understand and encourages the user t o explore several interpretations of the data. The most commonly used stereological densities are those whose reference is a volume. These include the familiar numerical, length, surface, and volume densities. However, densities related to other references are also useful (Bolender, 1979; Bolender and Bluhm, 1992). The “Other ratios” option of the Density Module (see Table 2) allows surface areas or lengths to be used as reference compartments rather than the usual volume reference. For example, this option allows the user to relate a selected compartment to the surface area of a nuclear or basement membrane compartment or to the length of a vessel. To simplify repetitive analyses, the “Combine compartments” option allows users to create new compartments that represent sums of original compartments. This functions just as though the summed compartments were individually selected while setting up an analysis. The “Equation format” option allows users to choose whether analyses display the names of the new compartments they have created or the full list of compartments that were summed together. A related option, “create OTHER compartment,” creates a volume compartment consisting of all the test points that were not counted. By selecting this option, a compartment that covers large areas of the test grids need not be counted directly during data collection with the Counting Module. Instead, its size can be calculated indirectly as the difference between the total test grid area and the area covered by all other compartments. For example, this option could be used to “count” the large air spaces of the alveolar portions of the lung.
File Options 9 a l y z e 1
OF INTEREST
Choose one or more surface area compartments:
Select a compartment by pressing 'Ins' or Unselect by pressing 'Del' or Press 'Return' to select compartment and quit. Press 'Esc' to quit without selecting compartment. ' + I .
I - ' .
File
Options SELECT REFERENCE COMPARTMENTS Choose one or more volume compartments: nEXC
miEXC
isPAN
zgEXC
Select a compartment by pressing 'Ins' or Unselect by pressing 'Del' or Press 'Return' to select Compartment and quit. Press 'Esc' to quit without selecting compartment. ' + I .
I - ' .
Fig. 4. To perform an analysis of surface density in the Density Module, the user selects (A) the compartment of interest; and (B) the reference compartment. Note that only compartments whose data are intersection counts are displayed as choices for the compartment of interest, and only point count compartments are offered for the ref-
SV(i/ref)
4 =
* B(i)
PI * A(ref)
-
erence compartment. In this sample analysis, the zgEXC (zymogen granules within exocrine cells) surface area has been selected as the compartment of interest. All the EXC (exocrine cell) organelle compartments have been selected to serve as the reference volume.
4
* PI *
PI
* 2 * P(ref) * dA2 *
I(i)
* d
-
k
2 * I(i) P(ref) * k * d
I
Result
I
SV(i/ref)
=
6641.4 cm"2/cmA3
Press Return to continue Fig. 5. Presentation of the estimated surface density includes a display of the equations used to perform the calculation as well as the compartments included in the estimate. The units of the result can be selected by the user. In the equations, PI stands for the mathematical constant 71.
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The results of an analysis can be output either to a printer or to a text file. The user can choose to print the analysis that he or she just performed or to instruct the printing of analyses that are about to be performed (see Table 2). These printouts are identical to the analysis displayed on the screen (e.g., Fig. 5). The “Save analysis” command is used to save just the numerical results of analyses in a format suitable for other data analysis programs.
CONCLUDING COMMENTS Biological stereology is experiencing an explosive period of growth, both in theory and application. As biologists, we would like to benefit from this exciting new technology, but for many beginners stereology continues to be a challenging technique to learn and use. Although the PCS software can ease the burdens of data collection and preliminary analyses, it has never addressed the more difficult problem of technology transfer nor has it directly supported the design and analysis of new experiments. To meet these challenges, we have improved the PCS software and added two new features-tutorials and databases. The stereology tutorial introduces the beginner t o the general topic of quantitative morphology and includes calculation toolkits for many of the new stereological methods (Bolender, 1992, this issue). It is intended to serve as a vehicle for technology transfer. A biologist with little or no experience in stereology can now try out one of the new methods by following the simple step-by-step instructions of a worked example. Interactive workscreens and simulations encourage the user to explore the basic concepts and assumptions of the methods and to develop an in-depth understanding of their application in experimental biology. Such an understanding, we feel, is an important first step in making the decision to use these methods in the laboratory. The quantitative morphology databases, which include published data, offer an entirely new way of interacting with the literature when designing new experiments and interpreting their results. We are just beginning to discover how to use these databases, but it already seems apparent that they represent a promising new research tool (Bolender and Bluhm, 1992). We are currently using them to look for solutions to the problems of standardizing results, reducing experimen-
tal biases, recovering data, and searching for biological patterns and generalizations. REFERENCES Bolender, R.P. (1979)Surface area ratios. I. A stereological method for estimating average cell changes in membrane surface areas. Anat. Rec., 194511-522. Bolender, R.P. (1986) Computer programs for biological stereology. In: Advanced Techniques in Biological Electron Microscopy 111. J.K. Koehler, ed. Springer, Heidelberg, pp. 167-200. Bolender, R.P. (1992)Quantitative morphology for biologists and computer scientists: I. Computer-aided tutorial for biological stereology (Version 1.0). Microsc. Res. Tech., 21:338-346. Bolender, R.P., and Bluhm, J.M. (1992) Database literature review: A new tool for experimental biology. In: Advances in Mathematics in Computers and Medicine, Pergamon Press (in press). Bolender, R.P., Pederson, E.A., and Larsen, M.P. (1982) PCS-I-a point counting stereology program for cell biology. Comp. Prog. Biomed., 15:175-186. Bolender, R.P., and Pentcheff, N.D. (1985) Computer Programs for Biological Stereology: PCS System I. Washington Research Foundation, Seattle. Cruz-Orive, L.M., and Weibel, E.R. (1990) Recent stereological methods for cell biology: A brief survey. Am. J . Physiol., 258 (Lung Cell. Mol. Phvsiol. 2kL148-Ll56. DeHoff, FrT., and Rhines, F.N. (1968) Quantitative Microscopy. McGraw-Hill, New York. Gundersen, H.J.G., and Osterby, R. (1981) Optimizing sampling effciency of stereological studies in biology: Or ‘Do more less well!’ J. Microsc., 121:65-73. Gundersen, H.J.G., Bendtsen, T.F., Korbo, L., Marcussen, N., M@ller, A., Nielsen, K., Nyengaard, J.R., Pakkenberg, B., SZrensen, F.B., Vesterby, A,, and West, M.J. (1988) Some new, simple and efficient stereological methods and their use in pathological research and diagnosis. APMIS, 96:379-394. Jarvis, L.R. (1992) The microcomputer and image analysis in diagnostic pathology. Microsc. Res. Tech., 21:292-299. Kernighan, B.W., and Plaugher, P.J. (1978) The elements of programming style, 2nd edition. Addison-Wesley, New York. Marr, D. (1982) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman, San Francisco. Mathieu, O., Cruz-Orive, L.M., Hoppeler, H., and Weibel, E.R. (1981) Measuring error and sampling variation in stereology: Comparison of the efficiency of various methods for planar image analysis. J . Microsc., 121:75-88. Morowitz, H.J., and Smith, T. (1987) Report of the Matrix of Biological Knowledge WorkshoD. Santa Fe Institute. Santa Fe. NM. Pentcheff, R.D.(1987)buidelines for developing data’collection and analysis systems for stereology: A case study and proposed standards. Acta Stereol., 6257-269. Pentcheff, N.D., and Bolender, R.P. (1985) PCS System I: Point counting stereology programs for cell biology. Comput. Methods Programs Biomed., 20:173-187. Pentcheff, N.D., and Bolender, R.P. (1987) PCS System 11: Modular programs and flexible databases to computerize stereology. Acta Stereol., 6/III:587-590. Weibel, E.R. (1979) Stereological Methods, Vol. 1. Practical Methods for Biological Morphometry. Academic Press, London. I
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Computer Assisted Data Collection for Stereology: Rationale and Description of Point Counting Stereology (PCS) Software N. DEAN PENTCHEFF AND ROBERT P. BOLENDER Department o f Integrative Biology, University of California, Berkeley, California 94720 (N.D.P.1; Department of Biological Structure, University of Washington, School of Medicine, Seattle, Washington 98195 (R.P.B.)
KEY WORDS
Quantitative morphology, Morphometry, Light microscopy, Electron microscopy, PCS System 111, MS-DOS, UNIX
ABSTRACT The paper describes microcomputer software for point counting stereology. Stereology includes a collection of statistical methods that quantify the images of light and transmission electron microscopy. The methods use test grids placed over images to collect raw data, which includes counts of points, intersections, transections, and profiles. In turn, the counts are included in stereological equations that give estimates of compartmental volumes, surfaces, lengths, or numbers. These parameters describe the composition of a structure in three-dimensional space. The PCS (point counting stereology) System Software I11 serves as a data collection, storage, and management tool. Users set up point counting protocols without programming, enter data by pressing predefined function (MS-DOS) or alphabetic keys (UNIX), store data in files, select files for analysis, and calculate results as stereological densities. The latest version of the PCS software includes a new user interface and is designed as a research “front end” that can feed data either into the calculation tools of a stereology tutorial (Bolender, 1992, this issue) or into the analysis routines of quantitative morphology databases (Bolender and Bluhm, 1992). o 1992 Wiley-Liss, Inc. COMPUTER ASSISTED DATA COLLECTION The central aim in data collection for stereology is to count or measure features of interest on images of twodimensional sections. There are two generally useful methods of doing this. The first is manual or semimanual point counting. In this method, raw data are collected by overlaying images with test grids or rulers (DeHoff and Rhines, 1968; Weibel, 1979). The investigator records the raw data based on direct observation of the test areas. In the other method, the images are submitted to automatic analysis by a computer system. The computer, through the use of specialized hardware and software, collects data and tallies the results. These methods have been reviewed by Bolender (1986) and Jarvis (this issue). Automatic Image Analysis Systems Automatic image analysis systems offer the appealing possibility of replacing repetitive (and expensive) manual counting with automatic measurement. With currently available image analysis hardware and software, a n individual image can be measured much faster than with manual methods. Each image can also be sampled much more densely than is common in manual systems, giving a higher precision for measurements on each image. Rather than the subjective assessment of microscopic features that is inherent in manual approaches, the computer program uses a set of objective (or at least reproducible) standards to decide what to measure. These standards, of course, must be developed by the investigator for each new study. This
0 1992 WILEY-LISS, INC.
objectivity can also permit the use of workers with less biological training, yielding a potential savings in labor costs. As one might expect, automatic image analysis systems have some drawbacks. The most obvious is the large initial expense, both in buying the basic hardware and software and in developing the specialized analysis algorithms for each study. High speed image analysis hardware is expensive ($8,000-$12,000 or higher), and the programming personnel needed to adapt the software for each new study are highly skilled, and therefore add to the cost. Compared with manual systems, it also takes much longer to set up and debug a measurement protocol. The image-sampling algorithms must be designed, programmed, debugged, and then validated against manual measurements. Another problem lies in the images required by automated systems. Because such systems typically rely on relatively simple grey-scale or texture analysis to identify features of interest, the image quality must be highly reproducible. An automatic image analysis system is inherently less able to compensate for variable image quality than is a n experienced investigator.
Received December 15, 1990; accepted in revised form January 14, 1991. Address reprint requests to N. Dean Pentcheff, Department of Biology, University of South Carolina, Columbia, SC 29208.
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Perhaps the most important drawback is that the high precision with which automated systems can sample images is unnecessary. The precision of final stereological estimates can be improved much more by sampling more animals (and blocks) than by sampling more of each micrograph (Gundersen and Osterby, 1981; Matheiu et al., 1981).
Computer Assisted Point Counting Traditionally, stereological measurements have been done manually by overlaying images with test grids. An investigator records counts manually, based on direct observation. The key advantage this method has over automated systems is the use of a trained investigator to identify and count the features of interest. The human visual system is remarkably good at recognizing complex patterns and a t dealing with images of variable quality (e.g., Marr, 1982). Of course, the advantage of using a trained observer has its accompanying problem: the measurements must be made by observers who have received appropriate training. For automated systems, achieving accurate recognition of features in biological images is very d i f f i c u l t especially for transmission electron microscopy. Since this is simple for computer assisted systems (the investigator does the recognition), setting up a working measurement protocol is much quicker. In the data collection phase, point counting stereology is becoming increasingly efficient a s a result of recent advances in sampling (Gundersen et al., 1988). For example, the volume density of a biological compartment can be estimated with a few hundred points (Cruz-Orive and Weibel, 1990) a task that can be completed in a few minutes. Using point counting software, the time spent collecting data from images with test grids therefore accounts for only a minor part of a n experimental protocol. Domains of Automatic Image Analyzers and Point Counting Methods The respective qualities of automatic image analysis systems and manual point counting methods suggest appropriate applications for each. In situations where large numbers of similar specimens need to be evaluated, automatic systems may have a n advantage. Their typically high initial cost can be offset by long-term savings in personnel time and consistency of results. Pathology laboratories and clinical screening applications represent such situations (see, for example, Jarvis, 1992, this issue). In research applications involving standard light and electron microscopy, point counting methods remain the method of choice. Since the goal of researchers is to examine new phenomena, i t is unlikely that they will be processing large numbers of similar specimens. Research laboratories typically examine different tissues with different techniques through time. Given the high initial investment in setting up a n image processing protocol, it is likely that point counting methods will remain more economical-at least for the near future.
Aspects of Point Counting That Can Be Computer Assisted How can manual point counting stereology be enhanced using computers? At least three areas can be identified: data collection, data management, and data analysis. Data Collection. Using a microcomputer with a specialized counting program offers several advantages over paper and pencil or mechanical counters. The programmable and interactive aspects of a computer program enable i t to teach or prompt the user, rather than passively accepting information. With a n appropriate point counting program, the counting protocol (including test grid specifications, features to be counted, measurements to be made, etc.) can be quickly specified for each investigation. Since the protocol defines the input data, the chances of omission or error are decreased. The physical accumulation of data is also eased with a computer. By using the computer as a counter, the program can allow immediate correction of errors. Running totals and statistical summaries can be made available on line, rather than after hand calculation. Data Analysis. By collecting data with a microcomputer, the numbers are stored in a form that can be used directly for data analysis. Preliminary analyses can provide immediate information on the progress of a n investigation and allow a researcher to identify problems arising from the counting protocols. Data Management. Once data have been collected, they need to be put in a form that allows for quick access and archiving. Data in the form of computer disk files fulfill both requirements. Multiple copies of individual data files can be produced and stored in separate locations to protect against data loss. Data files stored on a hard disk can be recalled a t any time for further analysis. POINT COUNTING STEREOLOGY (PCS) SOFTWARE, OLD AND NEW PCS-I In the early 198Os, Bolender and coworkers developed the PCS-I (Point Counting for Stereology I) program for biological stereology (Bolender et al., 1982). For each micrograph, the user visually scanned a n overlaid test grid and recorded the point intersection, transection, or profile counts by pressing user-defined counter keys on a Tektronix 4051 or 4052 microcomputer. Fundamentally, this method of data entry has been preserved throughout the PCS series of programs. In the data collection phase, PCS-I accumulated and totaled the counts for each compartment, computed calibration constants, provided graphical summaries of the cumulative errors in each compartment, and stored the raw data on tape cartridges. In the data analysis phase, the program evaluated stereological equations for volume, surface, and length densities and included provisions for grouping and randomizing the data from individual micrographs. PCS-I provided a welcome automation of steps which had previously been manually (and laboriously) performed. Unfortunately, PCS-I was not a “user-friendly” pro-
PCS SYSTEM SOFTWARE
gram. Calibration procedures and data entry were somewhat puzzling and unforgiving of mistakes. Moreover, the method of identifying files often created confusion, and changing the counting protocols and analyses required reprogramming. There were other problems. PCS-I was implemented on the Tektronix 4051 and 4052 microcomputers which, though advanced for their time, had several disadvantages. They had a single, built-in programming language, Tektronix BASIC, making structured programming difficult. The language and the tape cartridge storage format were incompatible with most other machines. Also, the graphical display was based on storage tube technology. This meant that changing any part of the screen required rewriting the entire display, making it impossible to write interactive screen routines. PCS System I In early 1983, the original PCS-I software was rewritten into a new group of programs called PCS System I (Pentcheff and Bolender, 1985). We set four design goals: 1) users should be able to set up counting protocols without reprogramming, 2) the analysis modules should have the capability of using data from one or more files, even when data were collected with different test grids or at different magnifications, 3) the density module should display the stereological equations it was evaluating, and 4) a n analysis module should be added that provided cell counts and average cell data. Following modern principles of structured programming (Kernighan and Plaugher, 1978), we split the program functions into modules. Data collection, statistics, and stereological analyses were embodied in separate programs. The programs, however, still ran on obsolete equipment (Tektronix 4050 series microcomputers) and were still written in a primitive, nonportable language (Tektronix BASIC). Attempts to maintain a strictly structured program broke down in the ongoing attempt to add new features to the developing programs. The ultimate result was a set of programs with thousands of lines of poorly commented, convoluted code. In a well-intentioned but misguided effort to limit coding to the most portable aspects of Tektronix BASIC, the graphical error analysis routine of PCS-I was relegated to a set of prewritten statistical programs from Tektronix. This essentially eliminated the useful on-line statistical error analysis.
PCS System I1 By the time PCS System I was complete, we were already planning the next version, PCS System I1 (Pentcheff and Bolender, 1987). Beyond the original goals of PCS System I, we added the following: 1) the program code and the data file format should be portable to a wide variety of computers, and 2) the interactive data collection facilities should be improved, since the target machines now used rewritable screens (standard computer displays). For development, we chose the MS-DOS (PC-DOS) operating system on a n IBM PC microcomputer using
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the C programming language (Pentcheff, 1987). The code could be compiled and run on both UNIX-based microcomputers and IBM PC compatible microcomputers, rather than PCS System 1’s single-system limitation. Since then, the code has also been ported to the Macintosh operating system by Malcolm Pradhan. In contrast to the machine-specific binary format of PCS System I, the data files used by PCS System I1 consisted of ASCII characters. This conferred two important benefits. First, data manipulation and analysis programs already available under the MS-DOS and UNIX operating systems could be used with the data files. Second, ASCII characters could be sent over domestic and international computer networks without any special processing. Given the growing importance of communication and data exchange via computer networks, this was a n important concern for scientific data storage. For PCS System 11, the Counting Module (for data collection) was thoroughly redesigned for ease of use and speed. The on-line cumulative statistical error display was restored. To preserve the portability of the code, the statistical display was implemented as a numerical rather than a graphical display. PCS System I1 had problems. The two analysis modules (Density Module and Average Cell (B-Bar) Module) were transliterated directly from the Tektronix BASIC version of PCS System I into the C language of PCS System I1 without redesign. Therefore, they did not receive the benefits of a n improved user interface and their code was difficult to debug and maintain. PCS System 111 PCS System I1 was essentially a n interim program, bridging the time between PCS System I and the development of a fully redesigned system. By the time PCS System I11 was being designed, the importance of linking data from multiple scientific studies was beginning to be emphasized (Morowitz and Smith, 1987). The orientation of PCS System I11 has therefore changed from that of its predecessors. Rather than being a self-contained system, PCS System I11 is now seen as a research “front end” (see Table 1) that can feed new primary data (stereological densities) into either the calculation tools of a stereology tutorial (Bolender, 1992, this issue) or into the analysis routines of quantitative morphology databases (Bolender and Bluhm, 1992). The design goals for PCS System I11 included collection and storage of raw data and indexing information, on-line statistics, preliminary data analysis, portability of data to other computer programs, and a new user interface. Design and Operation of PCS System I11 Data Collection (Counting Module). The Counting Module has changed little since the redesign that accompanied PCS System 11. There are three functional areas within the program: the Indexing Screen, the heading of the Counting Screen, and the body of the Counting Screen. In addition, a Help Screen listing the functions and keypresses of the program can be displayed at any time.
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N.D. PENTCHEFF AND R.P. BOLENDER TABLE 1 . Components of a three-part computer assisted system for quantitutiue morphology'
PCS System 111 Count Module B-Bar Module Density Module (Other analyses)
Stereology tutorial Tutorial modules on sampling, data collection, stereol6gicil - analysis, and data interpretation 'l'opics Introduction to quantitative morphology Symbols and terms Data types Sampling Information hierarchies Data interpretation Statistics
Quantitative morphology databases Intemated database nf . .ouantitative m&phological data from multiple sereological studies Topics Cell data Orgadtissue data Standardization of data Structural patterns Histological generalization ~
'Data flow through the programs from left to right. PCS System 111 functions a s the data collection and preliminary analysis front end. The stereology tutorial performs other analyses as well as its educational and training functions. The databases allow a specific study's analysis to integrate the study's new data (collected with PCS System 111) with the results of other studies in the literature.
PCS SYSTEM I11 POINT COUNTING MODULE RELEASE 1.00 N. DEAN PENTCHEFF AND ROBERT P. BOLENDER DEPARTMENT OF BIOLOGICAL STRUCTURE UNIVERSITY OF WASHINGTON (press the key marked 'Home' for help)
.~ ~
Length of calibration spaces (L) in cms Number of calibration spaces (N) in length L Calibration standard (C) in spaces/cm Magnification (M) (M=L*C/N)
10. 10. 100000. 100000.000000
Fig. 1. The indexing screen of the Counting Module of PCS System 111. The sample data shown here have been recalled from a data file saved with PCS System I. The magnification is automatically calculated based on the calibration information.
The Indexing Screen (Fig. 1) records identifying information about the data set. The final magnification of the micrographs is calculated based on the calibration information entered by the user (see Bolender and Pentcheff, 1985). The heading of the Counting Screen (Fig. 2) is used to describe the specific data being collected from a given set of images (e.g., micrographs). The user can insert up to 30 data entry columns, each of which is used to accumulate counts from a specific compartment. The heading area records the abbreviated name of each compartment (including its reference compartment) as well as the characteristics of the test grid used to count it. Information about the type of count (point, intersection, transection, or number), the grid con-
stants, and the magnification is stored by the counting module in files and used by the analysis modules to calculate stereological densities. The body of the Counting Screen (Fig. 2) is used to accumulate the counts. Each line corresponds to the data of a single image (micrograph).All data in a given data file come from a set of images taken at the same magnification, and each file may include up to 145 lines of data. Counts are accumulated by pressing the function keys of PC computers (terminals on UNIX minicomputers use alphabetic keys). Each time a key is pressed, the counter in that key's column for the current row is incremented by one. To correct errors or add data collected by other means, numbers may be typed directly into the data entry fields of the Counting Screen.
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PCS SYSTEM SOFTWARE
I 36 2. 2. I
omim EXC F7 105 14 29 4 40 18
The lower half of the screen contains sample data collected from five micrographs. Additional lines are inserted as needed. If necessary, the lower half of the screen will automatically scroll vertically and the data columns will automatically scroll horizontally to display the active location.
Fig. 2. The counting screen of the Counting Module of PCS System 111. The top half of the screen contains information describing the compartment being counted and the characteristics of the grid used to count it. The line labelled SUM can be used to display the sum of counts, number of counts, average count, standard deviation of counts, or percent coefficient of variation of counts for each column.
Options -
Analyze I
I
Directory: .\*.pcs Choose a data file or files to read: h0612c.pcs pancla.qcs panc2a.pcs panc2e.pcs data.pcs h0612d.pcs panclb.pcs panc2b.pcs testl.pcs h0612a.pcs h0614a.pcs panclc.pcs pa-nc2c.pcs test2.pcs h0612b.pcs h0614b.pcs pancld.pcs panc2d.pcs Select a file by pressing 'Ins' or ' + I . Unselect by pressing 'Dell or '-I. Press 'Return' to select file and quit. Press 'Esc' to quit without selecting file. Fig. 3. The Density Module can read one or more data files at a time, selected from a menu of the available files. The "NEW M A S K option allows the user to change the current directory or the file types that are included in the menu display.
While the counts accumulate, summary statistics of all lines in the file are calculated and displayed. The row labelled SUM can be switched to display any of the following statistics for each column: sum of counts, number of rows (empty fields are treated a s missing, not zeros), average counts, standard deviation
of counts, and percent coefficient of variation of counts. All statistics are updated whenever a datum changes. Once all the data for a series of micrographs are entered, they are saved in a data file in ASCII format. Data files can be read by the analysis modules (de-
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TABLE 2. Density Module command menus’ File Info on density Read PCS file Files read Print Choose output destination Print file summary Print constructed compartment summary Print analysis Print prior analysis Print next analysis Print all subsequent analyses Stop printing analyses Save analysis Enter raw data DOS escape Quit Options Combine compartments Combine point (P) compartments Combine intersection (I) compartments Combine transection (T) compartments Combine number (N) compartments Create “OTHER compartment Delete constructed compartment Equation format Select output units Change all units Change volume units Change surface units Change length units Analyze Volume density Surface density Length density Density summary table Other ratios Surface reference Leneth reference ‘The commands in boldface constitute the pulldown menu at the top of the screen. The other menus appear as submenus beneath them.
scribed below) or recalled by the Counting Module for reviewing, editing, or adding new data.
Data Management PCS System I11 data files are standard operating system files. Since they contain ASCII characters, any word processor or editor can be used to examine or modify the contents of a file. To export PCS System I11 data files, the user selects the Converter Module. At present, it rearranges the data into a format appropriate for use by a spreadsheet program such as Lotus 1-2-3 (Cambridge, MA) or Microsoft Excel (Redmond, WA). In turn, these commercial programs have links to many other software packages. Analysis Modules Stereological Densities. The Density Module performs standard stereological calculations of volume, surface, and length densities. It has two modes of operation: interactive and noninteractive. The interactive mode is useful for training and preliminary data analysis. Through a system of pulldown menus, the user chooses to include or delete data files from the
analysis and then selects an analysis routine. Each analysis ends with a display of equations and the stereological result. Once an analysis protocol has been designed and tested interactively, the program can be run in noninteractive mode through a script of commands. This mode generates results automatically. The following discussion addresses some aspects of the interactive mode of the Density Module. Table 2 shows the menu commands available to the user. The Density Module allows the user to select one or more data files, previously collected with the Counting Module. Data files are selected from a menu of the files available on disk, illustrated in Figure 3. The magnification information stored in each file is used to scale the incoming data (all internal calculations are done in absolute units using meters). The algorithms used to do the stereological analyses are able to combine data files even when data were collected with different test grids and magnifications. Once one or more data files have been chosen, a stereological analysis can be selected. In the following example, we show how to make a surface density estimate. The user first selects the surface density analysis and then chooses a surface area compartment to estimate (Fig. 4a) and one or more reference compartments to serve as the reference volume (Fig. 4b). After these variables are selected, the program displays the equations and the results (Fig. 5). This “building block” type of analysis is easy to learn and understand and encourages the user t o explore several interpretations of the data. The most commonly used stereological densities are those whose reference is a volume. These include the familiar numerical, length, surface, and volume densities. However, densities related to other references are also useful (Bolender, 1979; Bolender and Bluhm, 1992). The “Other ratios” option of the Density Module (see Table 2) allows surface areas or lengths to be used as reference compartments rather than the usual volume reference. For example, this option allows the user to relate a selected compartment to the surface area of a nuclear or basement membrane compartment or to the length of a vessel. To simplify repetitive analyses, the “Combine compartments” option allows users to create new compartments that represent sums of original compartments. This functions just as though the summed compartments were individually selected while setting up an analysis. The “Equation format” option allows users to choose whether analyses display the names of the new compartments they have created or the full list of compartments that were summed together. A related option, “create OTHER compartment,” creates a volume compartment consisting of all the test points that were not counted. By selecting this option, a compartment that covers large areas of the test grids need not be counted directly during data collection with the Counting Module. Instead, its size can be calculated indirectly as the difference between the total test grid area and the area covered by all other compartments. For example, this option could be used to “count” the large air spaces of the alveolar portions of the lung.
File Options 9 a l y z e 1
OF INTEREST
Choose one or more surface area compartments:
Select a compartment by pressing 'Ins' or Unselect by pressing 'Del' or Press 'Return' to select compartment and quit. Press 'Esc' to quit without selecting compartment. ' + I .
I - ' .
File
Options SELECT REFERENCE COMPARTMENTS Choose one or more volume compartments: nEXC
miEXC
isPAN
zgEXC
Select a compartment by pressing 'Ins' or Unselect by pressing 'Del' or Press 'Return' to select Compartment and quit. Press 'Esc' to quit without selecting compartment. ' + I .
I - ' .
Fig. 4. To perform an analysis of surface density in the Density Module, the user selects (A) the compartment of interest; and (B) the reference compartment. Note that only compartments whose data are intersection counts are displayed as choices for the compartment of interest, and only point count compartments are offered for the ref-
SV(i/ref)
4 =
* B(i)
PI * A(ref)
-
erence compartment. In this sample analysis, the zgEXC (zymogen granules within exocrine cells) surface area has been selected as the compartment of interest. All the EXC (exocrine cell) organelle compartments have been selected to serve as the reference volume.
4
* PI *
PI
* 2 * P(ref) * dA2 *
I(i)
* d
-
k
2 * I(i) P(ref) * k * d
I
Result
I
SV(i/ref)
=
6641.4 cm"2/cmA3
Press Return to continue Fig. 5. Presentation of the estimated surface density includes a display of the equations used to perform the calculation as well as the compartments included in the estimate. The units of the result can be selected by the user. In the equations, PI stands for the mathematical constant 71.
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The results of an analysis can be output either to a printer or to a text file. The user can choose to print the analysis that he or she just performed or to instruct the printing of analyses that are about to be performed (see Table 2). These printouts are identical to the analysis displayed on the screen (e.g., Fig. 5). The “Save analysis” command is used to save just the numerical results of analyses in a format suitable for other data analysis programs.
CONCLUDING COMMENTS Biological stereology is experiencing an explosive period of growth, both in theory and application. As biologists, we would like to benefit from this exciting new technology, but for many beginners stereology continues to be a challenging technique to learn and use. Although the PCS software can ease the burdens of data collection and preliminary analyses, it has never addressed the more difficult problem of technology transfer nor has it directly supported the design and analysis of new experiments. To meet these challenges, we have improved the PCS software and added two new features-tutorials and databases. The stereology tutorial introduces the beginner t o the general topic of quantitative morphology and includes calculation toolkits for many of the new stereological methods (Bolender, 1992, this issue). It is intended to serve as a vehicle for technology transfer. A biologist with little or no experience in stereology can now try out one of the new methods by following the simple step-by-step instructions of a worked example. Interactive workscreens and simulations encourage the user to explore the basic concepts and assumptions of the methods and to develop an in-depth understanding of their application in experimental biology. Such an understanding, we feel, is an important first step in making the decision to use these methods in the laboratory. The quantitative morphology databases, which include published data, offer an entirely new way of interacting with the literature when designing new experiments and interpreting their results. We are just beginning to discover how to use these databases, but it already seems apparent that they represent a promising new research tool (Bolender and Bluhm, 1992). We are currently using them to look for solutions to the problems of standardizing results, reducing experimen-
tal biases, recovering data, and searching for biological patterns and generalizations. REFERENCES Bolender, R.P. (1979)Surface area ratios. I. A stereological method for estimating average cell changes in membrane surface areas. Anat. Rec., 194511-522. Bolender, R.P. (1986) Computer programs for biological stereology. In: Advanced Techniques in Biological Electron Microscopy 111. J.K. Koehler, ed. Springer, Heidelberg, pp. 167-200. Bolender, R.P. (1992)Quantitative morphology for biologists and computer scientists: I. Computer-aided tutorial for biological stereology (Version 1.0). Microsc. Res. Tech., 21:338-346. Bolender, R.P., and Bluhm, J.M. (1992) Database literature review: A new tool for experimental biology. In: Advances in Mathematics in Computers and Medicine, Pergamon Press (in press). Bolender, R.P., Pederson, E.A., and Larsen, M.P. (1982) PCS-I-a point counting stereology program for cell biology. Comp. Prog. Biomed., 15:175-186. Bolender, R.P., and Pentcheff, N.D. (1985) Computer Programs for Biological Stereology: PCS System I. Washington Research Foundation, Seattle. Cruz-Orive, L.M., and Weibel, E.R. (1990) Recent stereological methods for cell biology: A brief survey. Am. J . Physiol., 258 (Lung Cell. Mol. Phvsiol. 2kL148-Ll56. DeHoff, FrT., and Rhines, F.N. (1968) Quantitative Microscopy. McGraw-Hill, New York. Gundersen, H.J.G., and Osterby, R. (1981) Optimizing sampling effciency of stereological studies in biology: Or ‘Do more less well!’ J. Microsc., 121:65-73. Gundersen, H.J.G., Bendtsen, T.F., Korbo, L., Marcussen, N., M@ller, A., Nielsen, K., Nyengaard, J.R., Pakkenberg, B., SZrensen, F.B., Vesterby, A,, and West, M.J. (1988) Some new, simple and efficient stereological methods and their use in pathological research and diagnosis. APMIS, 96:379-394. Jarvis, L.R. (1992) The microcomputer and image analysis in diagnostic pathology. Microsc. Res. Tech., 21:292-299. Kernighan, B.W., and Plaugher, P.J. (1978) The elements of programming style, 2nd edition. Addison-Wesley, New York. Marr, D. (1982) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman, San Francisco. Mathieu, O., Cruz-Orive, L.M., Hoppeler, H., and Weibel, E.R. (1981) Measuring error and sampling variation in stereology: Comparison of the efficiency of various methods for planar image analysis. J . Microsc., 121:75-88. Morowitz, H.J., and Smith, T. (1987) Report of the Matrix of Biological Knowledge WorkshoD. Santa Fe Institute. Santa Fe. NM. Pentcheff, R.D.(1987)buidelines for developing data’collection and analysis systems for stereology: A case study and proposed standards. Acta Stereol., 6257-269. Pentcheff, N.D., and Bolender, R.P. (1985) PCS System I: Point counting stereology programs for cell biology. Comput. Methods Programs Biomed., 20:173-187. Pentcheff, N.D., and Bolender, R.P. (1987) PCS System 11: Modular programs and flexible databases to computerize stereology. Acta Stereol., 6/III:587-590. Weibel, E.R. (1979) Stereological Methods, Vol. 1. Practical Methods for Biological Morphometry. Academic Press, London. I
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