- Top of page
- Results and discussion
- Materials and methods
Current research in cell biology frequently uses light microscopy to study intracellular organelles. To segment and count organelles, most investigators have used a global thresholding method, which relies on homogeneous background intensity values within a cell. Because this is not always the case, we developed WatershedCounting3D, a program that uses a modified watershed algorithm to more accurately identify intracellular structures from confocal image data, even in the presence of an inhomogeneous background. We give examples of segmenting and counting endoplasmic reticulum exit sites and the Golgi apparatus.
Investigations into the mechanisms that regulate the function, morphology and biogenesis of many intracellular organelles often rely on data from 3D confocal microscopy (1). For cell biology in particular, validated markers are used to identify the organelles which they label. Once the image data are acquired, a step known as segmentation is performed to identify regions within each image that correspond to a labeled organelle or structure of interest. Identified organelles are then counted or analyzed further, see for example Jokitalo et al. (2). Because image data are actually stored as an array of intensity values, each of which represents the light intensity measured at one pixel (2D) or voxel (3D) within the image, segmentation can be carried out mathematically using a computer program that imports the image file and subsequently processes the raw intensity data.
Thresholding is one method that is widely used to analyze confocal image data. In practice, this involves the creation of a binary mask from image data by testing whether the value of each voxel is greater than a certain value, which is known as the threshold value (3). A binary mask is simply an assignment of either a value of True or False to each voxel within the image data, depending on whether the intensity value is greater (True) or less (False) than the threshold value. Voxels within the binary mask that have True values can then be grouped into regions of adjacent voxels, whose values are also True. Each region is then either counted as individual objects or broken down further into subobjects. The simplest and most commonly used form of thresholding is known as global thresholding, which applies the same threshold value to all voxels within the image data set (3). This works well if each object being segmented contains voxels with intensity values greater than the global threshold and is completely surrounded by voxels with intensity values less than the threshold value.
However, when the density of objects is non-uniform, the intensity of background labeling often becomes inhomogeneous. An example is shown in Figure 1A where BSC-1 cells have been labeled for endoplasmic reticulum exit sites (ERES). These structures collect newly assembled cargo molecules for onward transport to the Golgi apparatus. In mammalian cells, there are several hundred such sites, dispersed throughout the cell (4,5). They are identified by the aggregation of coat protein II (COPII) on the endoplasmic reticulum (ER) membrane (6). Figure 1A shows an intensity profile through three such sites, two of which are close together. The background intensity between these two close objects is higher than the background between ERES that are spaced further apart. This inhomogeneity means that there is no global threshold value that will yield three objects. In the example shown, thresholding at the level indicated by either lines A or C yields two objects, while thresholding at the level of line B yields only one.
Figure 1. Comparison between global thresholding and watershed segmentation. A) BSC-1 cells were fixed, permeabilized and labeled with antibodies to the COPII subunit Sec31p, followed by secondary antibodies tagged with Alexa 488. The image at top left shows discrete ERES, three of which are cut by the red line in the adjacent image, the corresponding 1D intensity profile plot being drawn below. Three possible global threshold values (A–C) are illustrated with blue lines drawn on the intensity profile to indicate the 1D segmented regions that would result. The corresponding 2D segmentation results are shown on the right. Note that there is no global threshold value which segments this 1D profile into three objects. B) The raw image and intensity profile data from A are shown with local maxima labeled with a red asterisk. A hypothetical watershed segmentation applied to this intensity profile and seeded at local minima of the inverted data (i.e. local maxima of the raw data) segments this profile into three regions (i–iii). Application of the WatershedCounting3D algorithm to the corresponding raw 3D confocal data shows, in the image on the right, that three segmented regions are obtained.
In the field of image analysis, other thresholding methods have been devised to address the problem posed by such an inhomogeneous background (3,7). Some of these methods sample a subregion of the image to determine a threshold value that will be applied to voxels within that region. Such methods work well if variations in the background level are smooth. However, this is rarely the case in biology because the clustering of objects themselves often introduces variations in background levels. Variations in background staining can also be caused by the presence of marker molecules outside of the structure being segmented. This is partially solved by multiple component analysis and its related methods, which decompose an image using a set of basis functions. However, the choice of which basis functions to use will ultimately determine the number of individual structures identified (8). Thus, these methods fail to reproducibly count the number of organelles within a cell under different conditions.
A method developed by Vincent and Soille, called segmentation by morphological watersheds, offers the means to address these segmentation issues (9). This method makes an analogy between raw image data and a topological terrain map of the earth, where intensity values in the image data represent vertical elevation. Voxels are then grouped into regions that are analogous to water catch basins or watershed regions. Thus, each watershed region contains one local minimum along with all the points from which water would flow toward that minimum. If water would flow toward more than one minimum from a particular location, such as a ridgeline, then that point can be defined as a watershed line (or border) between two regions. The minimum of a watershed region is often referred to as the seed point of the region since it is the first identified point within a region.
Fluorescence images from confocal microscopy are also analogous to a terrain map, but the structures of interest are the peaks, not the catch basins. To identify the peaks using a watershed algorithm, the intensity data need to be inverted before analysis, so that the local maxima become the local minima. For visualization purposes, the original intensity data are then shown with seed points defined at local maxima, which is equivalent to local minima of the inverted data. The robustness of this algorithm stems from the fact that each object is first identified by the presence of a seed point, without having to use a global threshold. A watershed algorithm separates the ERES that could not be segmented by the global thresholding method (Figure 1A,B).
Unfortunately, the basic watershed algorithm is not directly applicable to segmenting organelles from 3D confocal image data. One problem is that confocal image data have better resolution in the x-y direction compared with the z direction. Further work into watershed segmentation has addressed the anisotropic nature of confocal data, and these methods have been used to segment and classify nuclei in confocal data (10,11). However, the lack of a robust method for identifying individual organelles or a method for removing noise without applying a global threshold has prevented the widespread use of a watershed algorithm for segmenting intracellular organelles. Here, we have extended the original watershed algorithm and optimized it for segmenting and counting organelles from 3D confocal image data. As an illustration of this new method, it was applied to the 3D confocal data that contain the subregion shown in Figure 1. The corresponding subregion after application of this algorithm is shown in the right of Figure 1B with segmented objects drawn in blue. The image shows that this method can identify the three ERES that could not be discriminated by global thresholding applied in Figure 1A.