Volume slicing

Prerequisites

Before starting this lesson, you should be familiar with:

• Basic properties of images and pixels

• Spatial calibration

Learning Objectives

After completing this lesson, learners should be able to:

• Create slice views of volumetric image data

• Master different ways of dealing with anisotropic voxels

Motivation

Volumetric data is intrinsically difficult to visualise, especially on a 2D computer monitor. Various volume rendering techniques, including VR, exist, however for quantitative image inspection, where it is critical to access the gray value of individual voxels, extracting one 2-D slice of a volume is the method of choice. Thus, such volume slicing is implemented in almost all image analysis software packages. A conceptual challenge is that microscopes often produce anisotropic data, where the voxel spacing along the z-axis is typically larger than in the x and y axes. Thus, creating a slice, e.g., in the zy plane needs to be done with special care.

Concept map

Figure

Activities

Create slice views of a 3D image

• Open a 3D image, e.g.,
  • xyz_16bit_calibrated__dna_metaphase.tif
• Create slice views of the 3D volume in XY, XZ, and YZ planes
• Appreciate and deal with anisotropic voxels sizes
• Optionally also create slice views at non-orthogonal planes

ImageJ GUI

• Open a 3D image
• Use Image > Properties to check for anisotropic voxel sizes
• Use Image > Stacks > Orthogonal views to view the data in XY, XZ and YZ planes
• Understand how the anisotropy is dealt with
• Use Image > Stacks > Reslice to resample the data, exploring the below options for dealing with anisotropy
  • Output spacing
  • [ ] avoid interpolation, if this is checked, Output spacing is ignored
• Use the line tool to draw a line ROI and again use Image > Stacks > Reslice to resample along this line
• Slice the data at arbitrary angles using Plugins > BigDataViewer > Open Current Image]
  • Shift-X/Y/Z: slice along x,y,z.
  • Left button drag: slice along arbitray plane
  • I: toggle interpolation

ImageJ Macro

// open volumetric image
open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_16bit_calibrated__dna_metaphase.tif");

// explore the dimensions
width = getWidth();
height = getHeight();
depth = nSlices();
print("Original Image Dimensions: Width = " + width + ", Height = " + height + ", Depth = " + depth);

// reslice YZ with interpolation
run("Reslice [/]...", "output=0.300 start=Left flip rotate");
rename("YZ view");
width = getWidth();
height = getHeight();
depth = nSlices();
print("Image Dimensions YZ view: Width = " + width + ", Height = " + height + ", Depth = " + depth);

// reslice XZ with interpolation
selectImage("xyz_16bit_calibrated__dna_metaphase.tif");
run("Reslice [/]...", "output=0.300 start=Top");
rename("XZ view");
width = getWidth();
height = getHeight();
depth = nSlices();
print("Image Dimensions XZ view: Width = " + width + ", Height = " + height + ", Depth = " + depth);

// reslice YZ without interpolation
selectImage("xyz_16bit_calibrated__dna_metaphase.tif");
run("Reslice [/]...", "output=0.300 start=Left flip rotate avoid");
rename("YZ view");
width = getWidth();
height = getHeight();
depth = nSlices();
print("Image Dimensions YZ view WITHOUT interpolation: Width = " + width + ", Height = " + height + ", Depth = " + depth);

// reslice XZ with interpolation
selectImage("xyz_16bit_calibrated__dna_metaphase.tif");
run("Reslice [/]...", "output=0.300 start=Top avoid");
rename("XZ view");
width = getWidth();
height = getHeight();
depth = nSlices();
print("Image Dimensions XZ view WITHOUT interpolation: Width = " + width + ", Height = " + height + ", Depth = " + depth);

run("Tile")

Python Napari

# !First start Napari and drag and drop the image (https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_16bit_calibrated__dna_metaphase.tif) in the Napari viewer.

# Access the 3D array in the image layer
image = viewer.layers[0].data # assuming the image is in the first layer
shape = image.shape
print("Image shape:", shape)

# Compare the non-scaled image to the scaled image. You can also use the 3D view to better appreciate the dimensions.
voxel_dims = (0.300, 0.1377745, 0.1377745) # original voxel dimensions in µm, in order ZYX
scale_factor = 1 / voxel_dims[0]
scaled_voxel_dims = [voxel_dims[0] * scale_factor, voxel_dims[1] * scale_factor, voxel_dims[2] * scale_factor] # rescaling the voxel dimensions to maintain proportions but display with a z-step of 1.
print("New voxel dimensions scaled relative to original", scaled_voxel_dims)
viewer.add_image(d, name="Scaled to physical world dimensions", scale=scaled_voxel_dims) # Note that the scaled image layer contains the exact same data as the non-scaled image, but is only visualized differently to show the correct proportions.

# Create 2D slices (YX, ZX, and ZY)
mid_z_slice_ind = int(shape[0] / 2)
mid_y_slice_ind = int(shape[1] / 2)
mid_x_slice_ind = int(shape[2] / 2)

viewer.add_image(d[mid_z_slice_ind], name=f"z={mid_z_slice_ind}")
viewer.add_image(d[mid_z_slice_ind], name=f"scaled: z={mid_z_slice_ind}", scale = scaled_voxel_dims[1:])
viewer.add_image(d[:, mid_y_slice_ind, :], name=f"y={mid_y_slice_ind}")
viewer.add_image(d[:, mid_y_slice_ind, :], name=f"scaled: y={mid_y_slice_ind}", scale = [scaled_voxel_dims[0], scaled_voxel_dims[2]])
viewer.add_image(d[:, :, mid_x_slice_ind], name=f"x={mid_x_slice_ind}")
viewer.add_image(d[:, :, mid_x_slice_ind], name=f"scaled: x={mid_x_slice_ind}", scale = scaled_voxel_dims[:2])

# View in grid mode side by side
viewer.grid.stride = 1
viewer.grid.shape = (-1, 2)
viewer.grid.enabled=True

Assessment

Fill in the blanks

1. A set of 2D ____ placed on top of each other form a 3D ____.
2. An ____ voxel size can cause the image to appear ____ when viewing it at an angle.
3. Rendering anisotropic voxels can be done in various ways, such as ____, ____, or ____.

Solution

1. 2D slices placed on top of each other from a 3D stack.
2. An anisotropic voxel size can cause the image to appear deformed when viewing at a certain angle.
3. One can render anisotropic voxels, by (i) isotropic resampling, (ii) just showing them as a square anyway, (iii) showing them as a rectangle.

True or False

1. Isotropic image data has voxels of equal XYZ dimensions.
2. Slicing is the process of sectioning the data, that has more than two dimensions, along defined axes and dimensions.
3. Reslicing is a term used to indicate repeated slicing.

Solution

1. True
2. True
3. False - Typically, the term reslicing refers to resampling volumetric data from a different direction, such that the resulting image stack is a rotated version of the original stack.

Follow-up material

Recommended follow-up modules:

• Projections of 3D data

• Volume viewer

Learn more:

• ImageJ: Z-functions