Volume slicing refactoring

_includes/volume_slicing/volume_slicing_act1_imagej-gui.md

@@ -1,7 +1,7 @@
 - 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
+- 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

_includes/volume_slicing/volume_slicing_act1_imagej-macro.ijm

@@ -1,32 +1,45 @@
-// This macro opens the head image stack and reslices it to view it from different angles (side, front, top and diagonal view)
-// and then selects slices corresponding to Z=60, X=135, and Y=160.
-// Close other open images
-run("Close All");
+// open volumetric image
+open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_16bit_calibrated__dna_metaphase.tif");
 
-// open head image stack
-open("http://imagej.net/images/t1-head.zip");
-rename("Head viewed from the side");
-run("Duplicate...", "duplicate range=60");
-rename("Head Z=60");
+// explore the dimensions
+width = getWidth();
+height = getHeight();
+depth = nSlices();
+print("Original Image Dimensions: Width = " + width + ", Height = " + height + ", Depth = " + depth);
 
-// Reslice the head image stack to view the head from the front and from the top, and select the slices for X=135 and Y=160 in the orginal view.
-selectWindow("Head viewed from the side");
-run("Reslice [/]...", "output=1.500 start=Left rotate"); // view head from the front
-rename("Head viewed from the front");
-run("Duplicate...", "duplicate range=135-135");
-rename("Head X=135");
+// 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);
 
-selectWindow("Head viewed from the side");
-run("Reslice [/]...", "output=1.500 start=Top rotate"); // view head from the top
-rename("Head viewed from the top");
-run("Duplicate...", "duplicate range=160-160");
-rename("Head Y=160");
+// 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);
 
-// Rotate the head stack that is viewed from the side and reslice to obtain diagonal view
-selectWindow("Head viewed from the side");
-run("Duplicate...", "duplicate");
-run("Rotate... ", "angle=45 grid=1 interpolation=Bilinear enlarge stack"); // rotate the stack
-run("Reslice [/]...", "output=1.500 start=Top rotate");
-rename("Head in diagonal view");
+// 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);
 
-run("Tile");
+// 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")

_includes/volume_slicing/volume_slicing_act1_python-napari.py

@@ -0,0 +1,30 @@
+# !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

_modules/volume_slicing.md

@@ -17,13 +17,13 @@ concept_map: >
     V["Volume data"] --> S("Slicing")
     V --- A["Anisotropic"]
     S --> M["2D image"]
-    M -.- A 
+    M -.- A
 
 figure: /figures/volume_slicing.png
 figure_legend: "Volume slicing: Extracting 2-D slices from a 3-D volume, e.g. to be visualised on a computer monitor. Anisotropy: The measured data points have a different spacing in pixel vs. physical space, which poses a practical challenge for the rendering." 
 
 multiactivities:
-  - ["volume_slicing/volume_slicing_act1.md", [["ImageJ GUI", "volume_slicing/volume_slicing_act1_imagej-gui.md", "markdown"], ["ImageJ Macro", "volume_slicing/volume_slicing_act1_imagej-macro.ijm", "java"], ["ImageJ Jython", "volume_slicing/volume_slicing_act1_imagej-jython.py", "python"]]]
+  - ["volume_slicing/volume_slicing_act1.md", [["ImageJ GUI", "volume_slicing/volume_slicing_act1_imagej-gui.md", "markdown"], ["ImageJ Macro", "volume_slicing/volume_slicing_act1_imagej-macro.ijm", "java"], ["Python Napari", "volume_slicing/volume_slicing_act1_python-napari.py", "python"]]]
 
 assessment: >