A simple tool for stereological assessment of ... - Wiley Online Library

Journal of Microscopy, Vol. 243, Pt 1 2011, pp. 47–59

doi: 10.1111/j.1365-2818.2010.03481.x

Received 28 June 2010; accepted 13 December 2010

A simple tool for stereological assessment of digital images: the STEPanizer S.A. TSCHANZ, P.H. BURRI & E.R. WEIBEL Institute of Anatomy, University of Bern, Switzerland

Key words. Counting, digital imaging, electron microscopy, light microscopy, morphometry, software, stereology, radiology, test system, tomography. Summary STEPanizer is an easy-to-use computer-based software tool for the stereological assessment of digitally captured images from all kinds of microscopical (LM, TEM, LSM) and macroscopical (radiology, tomography) imaging modalities. The program design focuses on providing the user a defined workflow adapted to most basic stereological tasks. The software is compact, that is user friendly without being bulky. STEPanizer comprises the creation of test systems, the appropriate display of digital images with superimposed test systems, a scaling facility, a counting module and an export function for the transfer of results to spreadsheet programs. Here we describe the major workflow of the tool illustrating the application on two examples from transmission electron microscopy and light microscopy, respectively. Introduction The quantitative assessment of the structure of organ and cell components from tissue sections or tomographic imaging by means of stereology represents a powerful tool in biomedical research. Stereology is based on geometrical and statistical considerations and allows to obtain on sections unbiased estimates of volumes, surfaces, lengths and numbers of the structures of interest. There are many commercial products aimed at performing such tasks, like the NEWCAST System (Visiopharm, Hørsholm, DK, www.visiopharm.com), the STEREO INVESTIGATOR (MBF Bioscience, Williston VT, USA, www.mbfbioscience.com), the STEREOLOGER (Stereology Resource Center, Chester MD, USA, www.disector.com), or the STEREOLOGY TOOLKIT (BIOQUANT Image Analysis Corporation, Nashville TN, USA, www.bioquant.com), to cite a few of them. These instruments are quite sophisticated and flexible; they may integrate a light microscope with

x, y, z (focus) –motor stage and thus allow programmed sampling of viewing fields. However, they are expensive (up to US$ 100 000), and their operation can be complex. Although the acquisition of such instruments is certainly justified where stereology represents an important and major research tool, it may not be when stereological approach is used only occasionally or for the first time. Since many years practical methods for stereology were developed in our institution (Weibel et al., 1966; Weibel, 1979). In early times histological sections were analysed, for example, on a microscope with a projection screen fitted with suitable test systems or, for electron microscopy, photographic films overlaid by test systems printed on transparencies. Counting was performed using our software ‘STEPone’ which recorded the hit events by pressing keys on the numeric keypad of standard PC keyboards (Humbert et al., 1990). After registering counts this software delivered volume and surface densities on printouts. Today images are captured by means of digital cameras and image handling is done entirely within the computer. Unfortunately today’s general purpose image capturing programs are not suited to generate appropriate test systems. We therefore intended to combine the hit recording function of the STEPone program with a test system generator and an image display component to be operated in the computer in connection with the recorded digital images. Here, we present a software tool that greatly facilitates the collection of quantitative stereological data from all kinds of digitally available images. It allows to design the adequate test systems consisting of point lattices, lines and areas, and to directly superimpose these onto the images to be investigated. Although the collection of data is manual and observer guided, the data acquisition can be programmed, executed and exported automatically. The software is available free of charge by accessing it on our site (www.stepanizer.com). Basics of stereological methods

Correspondence to: Stefan A. Tschanz, Institute of Anatomy, University of Bern, Baltzerstrasse 2, 3012 Bern, Switzerland. Tel: +41 31 631 84 78; fax: +41 31 631 38 07; e-mail: [email protected]

C 2011 The Authors C 2011 Royal Microscopical Society Journal of Microscopy

Stereological investigations provide information on volume, surface, length and, with some extended techniques (3D

0D Number, N

–

3D 2D 1D Volume, V Surface area, S Length, L

Note: Basic stereological estimators for three-dimensional material with their respective appearance on images (2D), the suitable test system geometry for its assessment, the description of the hit event that will be recorded and the formula which provides the corresponding density. P(x), I(x), Q(x), Q− (x) indicate the respective counts on structure feature x. Pref , Lref , Aref represent the sum of points, lengths and areas on reference space. atot × t represents the volume of the disector, where t is the distance of the consecutive sections. Modified and extended from Weibel et al. (2007).

Nv(x) = Q − (x)/(Aref × t) Top count, Q− (x) 3D

Number of new appearances

Top of object hits v

V v(x) =P (x)/Pref Sv(X) = 2 × I (x)/L ref L v(x) = 2 × Q (x)/Aref Point count, P(x) Intersection count, I(x) Profile count, Q(x) p hits V l hits S a hits L p hits A l hits B Number of Q 0D 1D 2D

Point, p Line, l Plane, a (section) Volume, v (disector)

Dimension Wanted parameter

Area, A Boundary, B Point, Q

Measurement Dimension

Counting event on the image

Meaning of counting event in the real dimension Probe (test system geometry) Appearance in 2D (on the image)

sample, Disector), on number of structural components (Weibel, 1979; Sterio, 1984; Gundersen et al., 1988a,b; Cruz-Orive & Weibel, 1990; Mayhew, 1991; Howard & Reed, 2005). To obtain accurate and robust results strict stereological rules and principles are to be followed. Special attention must be paid to avoid any bias throughout all steps from tissue preparation and probe sampling to the counting procedure (Gundersen et al., 1999; Howard & Reed, 2005; Schmitz & Hof, 2005; Weibel et al., 2007; Mayhew, 2008; Hsia et al., 2010). Every stereological approach consists of two major steps: (1) probing the organ through an appropriate tissue sampling procedure and (2) estimating quantities of structures by appropriate counting strategies on sections. The quantitative estimation proper is performed through probing the images by means of superimposed test systems with appropriate geometric properties. Counting hits of test points P with the profile area of structures estimates volume density VV , whereas counting the number of intersections I of test lines with structure boundaries provides surface area density SV . Counting transections or profiles Q of very thin string-like structures allows estimating the length density LV of this structure. Surface and length density estimations require isotropic uniform random sections achieved in the preliminary sampling steps. In model-based approaches, LV can be estimated by profile counting even on oriented sections, for example cross-sections of muscle (Mathieu et al., 1983). Evidently, tissue or cell structures can also be counted as single items, but here special measures and precautions are needed to obtain biasfree results. Indeed, there is a strict dimensional relationship between the probe and the parameter to be determined (Table 1). As a rule, the dimension of the probe must at least sum up the parameter dimension to 3 (Weibel, 1979): this means, a volume can be estimated by points, a surface by lines and numbers by volumes. This makes it clear that for number estimates a two-dimensional (2D) sample, for example a single image, does not provide sufficient information. Three-dimensional (volume) samples can be obtained by analysing image pairs from serial sections at a determined distance or by focusing through thick sections in the light microscope according to the Disector principle (Sterio, 1984); however, the present software is not suited to analyse image pairs. Note that all parameters listed in Table 1 are assessed by simple counting of ‘hit’ events and direct profile counts. As a rule of thumb about 100–200 counts only are needed to obtain statistically robust data for every structural parameter per individual (i.e. person, animal, plant, etc.) (Gundersen & Osterby, 1981; Mathieu et al., 1981). In some cases, volume densities of different structures of interest may be very dissimilar. In such cases it is recommended to use double lattice test systems (Weibel, 1979) where two classes of probing points are contained at different densities. This allows the simultaneous probing of two structures at different sampling rates and saves counting work. Special attention has to be paid to the periphery of

Density estimate

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Table 1. Overview of the dimensionality in stereological methods.

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C 2011 The Authors C 2011 Royal Microscopical Society, 243, 47–59 Journal of Microscopy

TOOL FOR STEREOLOGICAL ASSESSMENT

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Fig. 1. Arrangement of the COUNTING Window in the STEPanizer. The relevant region for stereological estimation is quadratic (green AND orange parts). Its side length corresponds to the full height of the screen with part of the image being ignored on the right side (hatched). The green part corresponds to the counting area proper, delimited by the forbidden (continuous) and inclusion (dashed) lines of the red counting frame. Orange coloration defines the guard area. Notice the yellow field highlighting the labelled scale bar. Arrow on top of the vertical red continuous line indicates the vertical axis (if required). During number estimation objects touching the forbidden line are not counted. Elements extending from the green counting quadrat to the orange guard region are counted only when exclusively touching the inclusion line.

images: sometimes structures cut off by the image edge cannot be unambiguously identified. For this reason a counting frame is introduced (Fig. 1) at a certain distance from the image edge proper. This ‘guard area’ improves structure identification. In approaches where counting object profiles are relevant, some profiles may intersect the counting frame. Counting all these profiles leads to an overestimate of the number density as they lie partially outside the counting area. To avoid this socalled edge effect, the left vertical and lower horizontal lines are defined as forbidden lines (continuous line) whereas the right vertical and upper horizontal lines are so-called inclusion lines (dashed line). Only profiles lying completely or partially within the counting area and not touching the forbidden line will be counted. For details on using counting frames, see Gundersen, (1977) and Gundersen et al. (1988a). In special cases, the material under investigation may be strictly planar (blotting film, cell monolayer) so that areas, lengths and numbers can be estimated from single images, but calculation has to be adapted. Numerical data from stereological counting permit the calculation of densities, that is volumes, surface areas, lengths and numbers per unit volume. This means that at least two counts must be made: the hits with the structure of interest x and the hits on the containing space or reference space R. The stereological estimator is a ratio of hits(x)/hits(R). Structural or object density values and their intergroup comparison are not conclusive data, however; they may even be misleading. To avoid this so-called reference trap (Braendgaard & Gundersen, 1986) it is required to calculate absolute values from densities by multiplying these with the corresponding reference volume (e.g. the organ). This step also allows to take into account shrinkage and tissue distortion due to preparation. In many cases volumetry of the reference space, for example of an organ, will be based on the Cavalieri principle, one of the most straightforward, design-based stereological approaches (CruzOrive, 1999). Here again, point counting is the preferred tool to measure the volume. C 2011 The Authors C 2011 Royal Microscopical Society, 243, 47–59 Journal of Microscopy

Aims of the STEPanizer software The goal of this project was to develop a computer-based approach to facilitate the stereological counting process as a whole, starting with the easy creation of adequate test systems, their direct superimposition on the images to be analysed, the simple definition of counting parameters, the counting process itself, and finally the transfer of the primary data to a spread sheet program for the calculations. The requirements of the software were defined as follows: (1) Runs on standard computers with no special hardware requirements irrespective of the operating system (Microsoft Windows , Mac , Linux). (2) Reads .jpeg images. Images are resized proportionally and displayed according to the computer monitor size maintaining the scale. The field of investigation is always a square area. Image sets must be acquired following unbiased sampling rules. (3) Allows the creation of various sets of test system types including points, lines, grids and cycloids covering a wide range of quantification tasks. (4) Creation of double lattice test systems with two different probing densities on the same screen. (5) Test system size, that is number of points, lines etc. is user definable. (6) Images can be scaled with the aid of a scale bar previously imprinted on the images during image capture. (7) Following scaling, the true metric dimension of each test system element is computed. (8) Enables the analysis of series of images as a batch job. (9) Stereological parameters and program settings are continuously saved on local disk. A session can be stopped and restarted any time without data loss. (10) Count recording is done by typing on the numeric keypad (1–9) on standard PC keyboards (nine different parameters assignable). An additional 10th parameter can be recorded by clicking the left mouse button.

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(11) Counting results are readily exported as table files readable in Excel or other data handling software. (12) The program needs no installation and is loaded as a module within any web browser. In reference to our former counting program, called STEPone (Humbert et al., 1990) and the intention to provide an organizer tool for stereological projects, we called the program STEPanizer. Software description The STEPanizer was programmed in Java (Oracle Corporation, Redwood Shores, CA, USA) in compliance with object oriented software design. The software was designed as an APPLET, which is an executable software module that works within a web browser. The applet concept has the advantage, that the software is platform independent and needs no further installation. However, the JAVA plugin for the browser has to be installed, but most modern computers and operating systems normally have this plug-in preinstalled. STEPanizer was tested on Microsoft Windows , Apple Mac and Linux computers using Internet Explorer , Firefox or Safari as browsers, depending on the operating system. Because STEPanizer is freely accessible on the internet, we suggest to examine the software in parallel with the reading of this manuscript. The STEPanizer applet is launched through calling an URL (www.stepanizer.com) in the browser. STEPanizer has to have access to the local disk what is not usual for web-applets. Therefore, particular rights have to be granted to the applet, what is accomplished by a dialog pop-up. During the utilization of STEPanizer, a footprint of the actual state of the program including test system parameters, display size, image number, counting results, etc. is regularly saved on local disk. In case of deliberate or unexpected stop of the program, the previous state is automatically re-established at restart.

but eventually hiding parts of the right image side. Therefore, the quadratic counting area which STEPanizer uses for stereological assessment is always positioned at the left side of the images (Fig. 1).

Test system generation STEPanizer provides a set of test system types. The basic unit of every type is a quadratic test system tile, the size of which is determined by the number of tiles per counting area. Several basic tiles of the same type cover completely the quadratic counting area (see ‘Basics of stereological methods’ and Fig. 1). The actual version provides point, line, grid and cycloid basic tiles (Weibel, 1979; Cruz-Orive & Hunziker, 1986). A point tile consists of a single point; a line tile consists of two staggered lines with test points at their ends, resulting in four test points per tile (Fig. 2). A grid tile consists of two perpendicular lines; their crossing defines a single test point. A cycloid tile consists of one sine-weighted curve between two points. Line, grid and cycloid tiles provide so-called multipurpose test systems (Weibel, 1979) for combined estimation of volume and surface area densities. Point and line test systems allow the creation of two different point densities, forming a double lattice test system, where one coarse point stands in constant relation (1:4, 1:9, 1:16) to all points (see ‘Basics of stereological methods’ and Fig. 3). The counting frame has a dashed, so-called inclusion and continuous, so-called forbidden line (see ‘Basics of stereological methods’ and Fig. 1). The vertical forbidden line has an arrow on top, which can be used as an indicator of the vertical axis for stereological approaches based on vertical axis design (Baddeley et al., 1986). For practical reasons, the length of linear elements is proportional to the side length of a basic tile (Table 2). This is not a requirement based on stereological theories but practical as it helps in surveying calculations and results.

Image display

Counting hits

Microscopical images are always scaled proportionally to the selected monitor size, showing at least the full image height

As mentioned before, data assessment corresponds to counting the number of hits of test system elements (points, lines,

Fig. 2. Basic tiles A–D of test systems used in STEPanizer. Each quadrat corresponds to one basic test system tile with its number of points and lines, respectively. Crossing red lines represent a point. The missing blue circle sector functions as lookup corner to unambiguously recognize the underlying structure. The lines underneath the tiles are only given to illustrate the relation of test lines to the tile side length d (Table 2). C 2011 The Authors C 2011 Royal Microscopical Society, 243, 47–59 Journal of Microscopy


Fig. 3. Test systems with two different point densities (double lattice). The blue circles here indicate the tagged coarse points representing the whole tile area. Untagged points represent the fine points at a higher density. Here the ratio is 1:9 and 1:4, respectively. Notice, the coarse point serves also as fine point when lying on the structure probed by the fine point lattice.

areas) with a structure feature: point hits on profile area for volume, line intersections with profile border for surface area, or area intersections (profiles) with thin string-like structures for length estimation. Count recording is done by hitting one of the keys 1–9 of the numeric keypad where each key is assigned to one of the stereological parameters [e.g. P(a), P(b), I(a), Q(c)] to assess. If (synchronously to the key stroke) the mouse cursor is positioned next to the counting event on the image a smalllabelled circle is traced there. This ‘marking’ of hits is optional. An additional counter is assigned to the clicks of the left mouse button and increments with each click on the image. This 10th parameter can serve as an object counter because at the cursor position every mouse click prints a consecutively numbered small quadratic marker. The progress of counting hits can permanently be surveyed in a table located on the screen next to the image under investigation. Prerequisites for using the software The digital images must be captured following correct stereological sampling rules. They must all be obtained at the same magnification and saved in the jpeg file format at adequate resolution. Picture height should at least match the height of the computer monitor, both defined in pixels. However, the program allows for proportional stretching or shrinking the image to fit the screen. Image series

should be named with an invariable prefix and consecutively numbered without leading zeroes and no whitespaces within the name, for example sampleNr_1.jpg, sampleNr_2.jpg, . . . sampleNr_30.jpg. It is recommended to hierarchically organize the storage of the images according to the number of experimental groups and of animals per group. Images from one animal should be saved in one folder. For calibration purposes it is important to add a calibrated scale bar to at least one image of a batch. This defines the real dimensions of the structures investigated. As STEPanizer may hide some right-sided parts of microscopical images (Fig. 1), this bar should preferably be placed somewhere on the left hand side. If stereological estimation requires the use of a guard area the latter has to be set as explained in the ‘Basics of stereological methods’ section. Once monitor size, properties of the test system and width of the guard area are defined they should not be changed in the course of the whole experiment to obtain consistent results. For counting, STEPanizer uses the keys 1–9 of the numeric keypad located on the right side of standard PC keyboards. If such a keypad is not available (MAC , notebooks) an additional standard PC keyboard or a detached numeric keypad must be connected.

Workflow of the software After starting STEPanizer from http://www.STEPanizer.com and confirming extended read/write rights, the control centre of the application called ‘PARAMETER Window’ is opened. All settings are defined there by means of input fields, drop-down lists and click buttons. The images themselves are displayed later in the ‘COUNTING Window’, where the test system is superimposed on the scaled image and a tabular field on the left side of the image logs the hit counts. A normal work cycle for a new experiment (Fig. 4) starts by choosing the appropriate display size in accordance with the dimensions of the computer monitor. The relation of image size and magnification is determined interactively in the ‘SCALE Window’ by tracking a calibrated scale bar on one image with the mouse cursor. Now structural and parametric names are paired with the corresponding numeric keypad key in the

Table 2. Properties of basic test system tiles.

A: Point tile B: Line tile C: Grid tile D: Cycloid tile

Number of points per tile, ptile

Number of lines per tile

Length of lines per tile, ltile

l/p

Area per tile, atile

1 4 1 2

– 2 2 1

– d 2d d

– d/4 2d d/2

d2 d2 d2 d2

Note: Letters A–D correspond to the labels in Fig. 2. d is the side length of the basic tile. l/p is the test line length per point, where l/p = d/ptile . The total number of points per counting area (Ptot ) is Ptot = ptile × N tile and the total length of lines per counting area (Ltot ) is Ltot = ltile × N tile , where N tile is the number of basic tiles per counting area. N tile corresponds to the ‘Nbr. of tiles’ setting in STEPanizer. If the reference space does not fill the whole counting area (Pref < Ptot ), the line length is Lref = Pref × l/p. C 2011 The Authors C 2011 Royal Microscopical Society, 243, 47–59 Journal of Microscopy

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Fig. 4. Flow chart of initialization and continuous image analysis with the STEPanizer. Names in red correspond to control elements of STEPanizer’s graphical user interface. Rectangular boxes indicate user inputs. On the left side the active windows are shown. This shows only a simplified sequence of available user interactions.



‘SCALE Window’. Thereafter stereological settings of the test system overlay including the type and the number of basic tiles or subsampling properties (e.g. double lattice test system) are set. Colour and thickness of the points and lines can be modified. It is recommended to check display settings and test system by viewing some images (‘CHECK’ button). If this control is satisfactory, the image series should be processed in the ‘Batch mode’. Thereby a full image series from 1 to ‘x’ will be displayed successively in the ‘COUNTING Window’ for stereological examination. When switching to the next image the recorded hits are automatically saved on disk. However, after every image series and/or work session it is recommended to ‘EXPORT’ the result to a spreadsheet on disk. There are two modes to initiate the program: Either a new experiment is started and all parameters of the test system, magnification and display size have to be newly set, or the user continues an image series with already defined parameters. The initial setup for defining all parameters from scratch for a new experiment is illustrated on the flow chart in Fig. 4. Examples of using the STEPanizer Example 1: Quantitative ultrastructural analysis of rat hepatocytes With this example we show how STEPanizer could have been used in a quantitative microscopical study on rat liver that dealt with the impact of drug treatment on hepatocyte organelles (Bolender & Weibel, 1973). Material sampling. Entire livers from five rats were fixed by perfusion in situ and, after removal, their volume estimated by water displacement (Scherle, 1970). Alternatively, volume estimation could have been done by applying the Cavalieri

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principle (Cruz-Orive, 1999) on evenly spaced organ slices (Fig. 5). For each liver, five tissue blocks were sampled in a systematic random manner (Fig. 5) and one section per block was used for electron microscopy. Stereological estimation. The quantitative parameters under investigation were volumes and surface areas of smooth (sER) and rough endoplasmatic reticulum (rER), the volumes of mitochondria and lysosomes. Quantification was performed at two magnification stages. From every electron microscopical section, seven images were recorded at a lower (approx. 370×, stage 1) and seven pictures at a higher magnification (approx. 6700×, stage 2) in a systematic random manner. At the lower magnification, volume density per liver tissue of hepatocytes and of nonhepatocytes as well as of cytoplasm and nuclear volume of hepatocytes was assessed by point counting. At the second stage (higher magnification), volume and surface area densities of sER and rER and volume densities of mitochondria and lysosomes per hepatocyte cytoplasm were determined. For this combined volume and surface area estimation, a multipurpose test system with lines and points was used; with STEPanizer this can be generated on the digital images (Fig. 2B). The ‘PARAMETER Window’ of STEPanizer in Fig. 6 illustrates all settings needed to start the stereological counting procedure. The ‘Info Field’ at the bottom of Fig. 6 summarizes the scaled metric values of the test system. The ‘COUNTING Window’ (Fig. 7) depicts the test system in stage 2 superimposed on an electron microscopical image. The ‘Hit-Table’ on the left of the image logs the hit counts for each parameter. When all images of an animal have been analysed, this window is closed and by hitting the ‘EXPORT’ button on the ‘PARAMETER Window’ an Excel compatible spreadsheet is automatically saved in the images directory.

Fig. 5. Sketch summarizing the steps of random tissue sampling followed by a two-step cascade sampling for stereological estimation. Every step, from tissue preparation to image analysis proper has to obey the principle of systematic random sampling. In case of material anisotropy special precautions are needed to determine the cutting angles to obtain uniform random samples. (Cruz-Orive & Weibel, 1981). At organ level the volume is estimated by water displacement (see text). If slices from initial dicing are spaced at a known and fixed distance, organ volume can be estimated according to the Cavalieri principle. Here, as an example, eight equidistant liver slices are obtained with a random start; the randomly selected slices are cut to obtain sticks and the sticks to obtain blocks. On one section per tissue block seven images are recorded by systematic random sampling for each magnification step and these analysed with the illustrated test systems on the computer monitor. C 2011 The Authors C 2011 Royal Microscopical Society, 243, 47–59 Journal of Microscopy

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Fig. 6. PARAMETER Window. From top to bottom: Definition of the ‘Monitor Resolution’. It is followed by the file handling fields for ‘Image Name’ and ‘Image Path’, including the file system ‘BROWSE’ and the ‘SCALE’ button. Underneath the ‘Test System Properties’ are set, including the ‘CHECK’ button for a test system preview. The ‘Overlay Appearance’ settings define test system colour and line width. The ‘Batch Mode’ settings manage the consecutive working on whole image sets. Finally the standard program control buttons are arranged in a row. The ‘Info Field’ displays important informations on the state of the program and on properties of the test system. On the ‘Caution’ line, we find the buttons for resets and deletion of data.

From these raw data, volume and surface area densities could be calculated. Volume density is the ratio of points on the particular structure to all points in the reference space. Surface densities were computed as Sv(x) = 2I (x)/L ref = 2I (x)/L hep−cytopl = 2I (x)/Phep−cytopl (d /4),

where Sv(x) is the surface area density of the structure x, I(x) the number of intersections of test lines with the boundary of x, Lref = Pref x l/p the total length of all test lines on reference space, in this example hepatocyte cytoplasm (hep-cytopl), Phep−cytopl the number of points on reference space and l/p = d/4 resulting from the line tile property (Table 2; Weibel, 1979). By multiplying subsequently C 2011 The Authors C 2011 Royal Microscopical Society, 243, 47–59 Journal of Microscopy


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Fig. 7. COUNTING Window with a TEM image of hepatocyte cytoplasm. Overlay of a point and line test system (red) consisting of nine two-line tiles and a counting frame (yellow). Points on structures and intersections with structure boundaries are recognized and recorded by typing the corresponding numeric key (1–7). Recorded hits are displayed in the ‘Hit-Table’ on top left. Notice the ‘Info Panel’ showing the actual state of the program. Magnification 50 000×.

organelle densities (volume and surface) with hepatocyte volume density and total liver volume, absolute data for every animal was computed. For interanimal comparison absolute values may be related to body weight. Mean values and standard deviations from all animals of the group were calculated and compared statistically to the other groups. The stereological analysis allowed to show that Phenobarbital induced a doubling of the sER surface area, the organelle being mainly involved in Phenobarbital metabolism (St¨aubli et al., 1969). Within 7 days after stopping the treatment the sER returned to normal values whereas the volume of autophagic vacuoles, where the excess of membranous material was degraded, had increased (Bolender & Weibel, 1973). Example 2: Quantitative assessment of lung parenchymal properties at light microscopical level In this second example, light microscopical images from rat lungs were analysed to determine the volume of septal tissue, the air space volume, the surface area of alveolar septa and the mean linear intercept of alveolar air spaces. The goal of the study was to analyse the effect of mild vitamin A deficiency on newborn rats on postnatal days 4, 10 and 21, C 2011 The Authors C 2011 Royal Microscopical Society, 243, 47–59 Journal of Microscopy

normal pups serving as controls (Frey et al., 2004). Whole lungs of five animals per group were fixed by intratracheal instillation in situ. The lungs were removed and their volume was determined. They were then diced into cubes of 5mm side length according to a systematic uniform random sampling. Cubes were processed for light microscopy and images from paraffin sections captured with a digital chargecoupled device camera on a light microscope. Except for the test system type and the parameter assignment the setting up of STEPanizer and the counting procedures corresponds to the description above in example 1. Fig. 8 illustrates the applied test system within the ‘COUNTING Window’. The study revealed that, among other results, the absolute and body-weight related surface area of alveolar septa was reduced in Vitamin A deficient rats (Frey et al., 2004). In first approximation, the normal lung parenchyma cut at random orientation can be supposed to produce isotropically oriented structures hence a test system of parallel straight lines can be used for surface estimation. This is not appropriate in cases where the structures under investigation could be anisotropic (e.g. surface epithelia, trabecular bone or muscle fibres). Then a suitable approach could be based on the vertical section design (Baddeley et al., 1986) using cycloid

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Fig. 8. COUNTING Window with a light microscopical image of lung parenchyma. Lung parenchyma stained with Fuchsin. Grid overlay in blue, counting frame in black. The test system consists of 4 × 4 = 16 basic grid tiles. Green line encircles one basic tile. d: Side length of a basic tile: these green figures added for clarification are not part of the ‘COUNTING Window’. Crossings of blue lines correspond to counting points, what results in 16 counting points. The reference space for counting is lung parenchyma, therefore two points (upper right) lying on a bronchiole (Pnon−par ) do not belong to the reference space. The total number of reference points is thus 14 and the total length of test lines Lref = 14 × 2 × d as Lref = Pref × l/p and l/p = 2 × d (for grid tile, see Table 2). The ‘Hit−Table’ on top left shows the coupling of a structure parameter with the corresponding numeric keys 1–4 and already counted hits. Magnification 250×.

test line systems that are also provided by the STEPanizer tool. Output and calculation of stereological parameters STEPanizer exports the counting results as a text file in Microsoft Excel file format. However, it can be read by any spreadsheet program allowing the import of tabulator spaced data fields. Fig. 9 presents such an output file showing a result summary from example 2 (lung stereology) of the previous section. It contains the hit counts and their sums over all images investigated. In conjunction with numerical test system information, also available in the file, volume and surface area densities can be calculated taking the formulas on Tables 1 and 2. Discussion STEPanizer was developed to provide a straightforward tool to implement unbiased stereology on digitally acquired images. To many microscopists the use of stereological methods

appears to be too demanding regarding work load, equipment cost and complexity of methodological considerations. Other researchers may rely on sophisticated automated image analysis systems, sometimes not being conscious of possible systematic biases introduced by machines (e.g. threshold) (Sieracki et al., 1989; Litzlbauer et al., 2006). A problem can also be the spatial interpretation of 2D images. Unbiased, design-based stereology represents a powerful approach to obtain correct 3D quantitative data from section images (Ochs, 2006). Often this requires much less effort than expected. The main focus of STEPanizer is to facilitate the use of stereology in research and service-related microscopical investigation. It is designed primarily for the field of microscopy, but medical tomography imaging is certainly an additional area where this tool can be used (Roberts et al., 2000; Duran et al., 2007; Keller et al., 2009). Most commercial stereology packages focus on light microscopy whereas STEPanizer deals with images from any source and is therefore particularly useful in electron microscopy. A crucial part of every structural quantification is an appropriate tissue sampling process, which stands at the C 2011 The Authors C 2011 Royal Microscopical Society, 243, 47–59 Journal of Microscopy


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Fig. 9. Exported result spreadsheet. Excerpt of the exported result file of STEPanizer. For clarification some ranges are coloured: yellow, assignment of the numeric keypad keys to the user defined structures; red, hit count table, per structure (rows) with corresponding sum in the ‘Total’ column and per image (column) with the corresponding ‘SUM’. Notice the corresponding image file name and assessment date at the bottom of each column. The ‘Actual’ column shows counts of the last image only if it was not completed; green, numerical properties of the test system. For clarity some ‘result’ columns and unused rows are blanked out in this figure.

beginning of any experimental consideration. Hence, before using the STEPanizer a conceptual planning of the study including sampling, parameter definition, test system design and use of an appropriate magnification is an evident prerequisite (Weibel, 1979; Howard & Reed, 2005). Several review articles (Gundersen et al., 1988a,b; Cruz-Orive & Weibel, 1990) give a comprehensive overview on stereological approaches. Recent guidelines for quantitative assessment of lung structure are covered in an official document from the American Thoracic Society (ATS) (Brusasco & Dinh-Xuan, 2010; Hsia et al., 2010). Efforts defining such policies exist also in other research fields such as in neuroscience (Saper, 1996) and in nephrology (Madsen, 1999). C 2011 The Authors C 2011 Royal Microscopical Society, 243, 47–59 Journal of Microscopy

To make STEPanizer as easy as possible to use, but also as flexible as needed, its program design is focused on a clear work flow. Only few settings within the user interface are required before actual stereological estimation can be started. Despite this simplicity a large variety of stereological approaches is covered by the software. Selfimposed limitations of the program do not per se impair the stereological process but mainly increase the STEPanizer usability. One of these limitations is the use of a quadratic field of investigation irrespective of the original format of the acquired microscopical images. This has no influence on the sampling process, as every subpart of an unbiased image is itself unbiased. The same holds true for the

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fixed and reproducible positioning of the test system with respect to the image. At the present state the program does not cover approaches based on local or second-order stereology. Although STEPanizer provides only a restricted number of test system types and shapes, it covers a wide field of stereological measurements and avoids the risk of inappropriate configurations. The STEPanizer offers transparent data handling and data security. Each state and setting of the program can be recovered any time. The final data set produced by the program just consists of the number of recorded key strokes, that is the hits of the test system elements with the relevant structures and the scaled properties of the test system. The latter is important if test systems with linear properties (lines, cycloids, grids) are used, for example for estimating surface areas and line lengths in 3D. Evidently, points are also representative of a true area on the image in connection with image magnification. This is used for example in volume estimations by the Cavalieri principle (Cruz-Orive, 1999). However, point counts are mostly used to generate volume ratios where calibration is irrelevant. All the final stereological calculations are done outside of the STEPanizer and remain under the investigator’s control allowing full insight into the computations of stereological data. The STEPanizer should simplify the implementation of a stereological project and not be a black box regarding data handling. In contrast to many full-featured commercial and expensive products STEPanizer has no direct link to the microscope and image-capturing device. STEPanizer is dedicated to counting tasks on images but it is not an image-sampling device. The step from the section to the sampled images has to be managed by the user; this ensures greater flexibility regarding the image source. Furthermore, although automated systems often appear as elegant tools and as powerful feature detectors, they generally need a serious input to homogenize and normalize image quality. This ‘working on the images’ may induce unknown biases into the data collected. Indeed, results from automated approaches tend to be far from being more robust (Gundersen et al., 1981; Litzlbauer et al., 2006) than those obtained by feature detection by eye. Our visual system with its ingenious pattern recognition capabilities detects counting events in an unsurpassed and easy manner. Considering that about 100–200 counts per structure can already provide significant data (Gundersen & Osterby, 1981; Mathieu et al., 1981), the use of expensive and intransparent automated approaches for simple counting tasks may not appear adequate. Generally speaking, counting hit events has many advantages compared to measuring features with respect to efficiency: This was particularly proven for area estimation by point counting versus area tracing with a mouse curser and subsequent output of the ‘true’ area even though the latter method shows a higher precision per single measurement (Gundersen et al., 1981). As presented here,

powerful stereological estimations require nothing else than a standard computer with web access and the free STEPanizer software. To conclude, the STEPanizer allows to easily perform stereological counting tasks on digital images and we hope that it contributes to make stereology more attractive and user-friendly: The straightforward handling of the tool and the knowledge that counting a few hundred points can yield statistically relevant results may help to overcome reservations and fears against the workload of stereology.

Acknowledgements The authors thank Oliver Baum, Beat Haenni and Matthias Ochs for helpful discussions and assistance.

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