With the development of tissue clearing technology, the wide availability of fluorescent probes and fluorescent-tagged antibodies, and advances in confocal imaging and light sheet imaging techniques, 3D microscopy for tissue specimens has become a powerful tool for interrogating biology. In this post, we will discuss how to conduct image processing on the resultant image stacks obtained from using these advanced microscopy techniques, using the freely available, open-sourced, and hugely extensible ImageJ imaging platform (available for download here). We recommend using the Fiji package (Fiji Is Just ImageJ), a packaged version of ImageJ pre-loaded with many useful plugins and tools (available for download here).

in a fixed position, providing a three-dimensional sampling of the tissue being examined. This is commonly known as “optical sectioning” wherein thick tissue sections are employed and utilizing the optical ability of the confocal microscope as an optical “microtome,” structural details of the tissue can be revealed in three-dimensions.

The selection of microscope objective for imaging can have serious impacts on quality of images obtained. For most applications, air objectives work effectively; the highest numerical aperture (NA) available at the magnification required should be selected, as higher NA results in more light entering the objective, which yields higher contrast and signal. For deep tissue imaging (> 2mm), the use of glycerol or water immersion objectives is preferred.

Tissue Clearing

To increase the achievable depth at which confocal microscopes can be utilized to obtain images within tissues, clearing techniques (e.g. Visikol HISTO, CLARITY, 3DISCO) can by employed to render tissue specimens optically transparent. In conjunction with fluorescent labeling techniques, tissue clearing dramatically increases the depths at which confocal microscopy can image into tissue, allowing for large scale reconstructions of tissue histology and morphology on large and thick pieces of tissue (> 1 mm).

Loading and Processing Image Stacks in ImageJ (or Fiji)

Three-dimensional confocal imaging results in a collection of images as planes incrementing along the Z axis, known as an “image stack” (also known as a “z-stack”). Depending on the type of instrument and settings used during acquisition, the image may be a sequence of individual images (typically as .TIF files) where each image is a single z-plane in a single color or as a single file containing all images in the stack (e.g. TIF hyperstack, proprietary formats such as Leica .lif file or Zeiss .czi image file).

(and for time series, time as the last dimension). Select the correct order, and then enter in the number of color channels (c) and steps in the z-sequence (z).

Calibrating Image Stacks

After image stacks are loaded (and converted to hyperstacks if needed), calibration information should be input for the z-stack. This is accomplished by using the Image > Properties menu command. In the dialog, the x, y, and z resolution should be entered; the x and y resolution are defined by the image acquisition parameters, and the z-step (e.g. z resolution) is typically entered into the microscope when acquiring a stack. This step will ensure that distortions or stretching does not occur when processing the image in 3D, and furthermore will allow for direct measurements of the image to be taken.

If the proprietary image format stores multiple image sets in the same file, the Bioformats plugin will show you an additional dialog allowing you to select which image acquisitions you would like to load. Please note that Bioformats will load each checked image set into memory, and if image stacks are each very large, it may consume a lot of memory on your system, which can seriously effect the performance of your computer. Select the image acquisition you would like to load, and click OK.

Measurement of Object Volume from Image Stacks

ImageJ has numerous tools for measuring objects from images, however most of these tools are built to process a single image and return a single result. In order to measure volumes or to repeat measurements across the entire stack, some simple macro programming is required. ImageJ is also very powerful and there are often many ways methods that can be used to accomplish the same goals. In this example, we will demonstrate how to measure the volume of the cytoplasm of a cell, working with the confocal stack sample image that can be opened in ImageJ by going to File > Open Samples > Confocal Series menu option.

Step 1. Split the channels

• The color channels need to be split apart so that we can measure only the cytoplasm channel. This can be accomplished by using the Image > Color > Split Channels menu option. The result is the individual channels in separate windows. Close the red channel since it won’t be needed. Select the green cytoplasm channel.

• selected from the Threshold method dropdown box (outlined in blue in Figure 4), or you may manually threshold by entering the min and max values you would like to include.
• Select the Otsu method from the automatic threshold dropdown box. The Otsu threshold method is very useful for separating foreground objects from background in microscopy. The Otsu method works by grouping all pixels into two classes, bright and dark, and then determining the average pixel value within each class, then assigning the threshold value as the midpoint between the averages of the bright and dark pixels. This is an effective way to threshold foreground objects from the background in most fluorescent images.

• • The macro will loop through each image in the stack, and take the measurement of each slice individually.
  • Finally, the macro will loop through and sum the area measurement measurements, multiply this sum by the depth of each slice, and finally display the result.
  • There are detailed comments as to what each portion of code does below within comments in the macro code (comments start with // )
• Create a new macro by selecting Plugin > New > Macro menu item in ImageJ, and paste the code below into the macro window.
• Run the macro by clicking the “Run” button at the bottom of the macro window

  ImageJ Macro Code (click here to download code file):

  // ImageJ Macro Code
  // Measure Volume of Thresholded Pixels in an Image Stack
  //
  //
  
  macro "Measure Stack" {
      run("Clear Results");   // First, clear the results table
    
      // loop through each slice in the stack. Start at n=1 (the first slice), 
      // keep going while n <= nSlices (nSlices is the total number of slices in the stack)
      // and increment n by one after each loop (n++)
      for (n=1; n<=nSlices; n++) {  
         setSlice(n);  // set the stack's current slice to n
         run("Measure");   // Run the "Measure" function in ImageJ
      }
  
      // Create a variable that we will use to store the area measured in each slice
      totalArea = 0;
      // Loop through each result from 0 (the first result on the table) to nResult (the total number of results on the table)
      for (n=0; n < nResults; n++)
      {
         totalArea += getResult("Area",n);   // Add the area of the current result to the total
      }
      // Get the calibration information from ImageJ and store into width, height, depth, and unit variables. 
      // We will only be using depth and unit
      getVoxelSize(width, height, depth, unit);
      // Calculate the volume by multiplying the sum of area of each slice by the depth
      volume = totalArea*depth;
      // Print the result of the volume calculation to the log
      print(volume + " " + unit + "^3");
  }

Blog Post Author: Dr. Thomas Villani