Measureing intensity on vectors and surfaces

Often, we want to measure the intensity of a signal on a vector or surface. This can be done using the measure_intensity_on_vectors() function. This function takes a vector or surface as input and returns the intensity of the signal on the vector or surface.

from napari_stress import vectors, sample_data, measurements, frame_by_frame
import napari
import numpy as np
import napari_segment_blobs_and_things_with_membranes as nsbatwm
import napari_process_points_and_surfaces as nppas

Segmentation of sample data

We use napari-segment-blobs-and-things-with-membranes to segment the sample data. By using the frame_by_frame option, we can segment each frame individually without having to write for-loops.

droplet = sample_data.get_droplet_4d()[0][0]

droplet_rescaled = frame_by_frame(nsbatwm.rescale)(droplet, scale_x=1, scale_y=1, scale_z=2)
droplet_binary = frame_by_frame(nsbatwm.threshold_otsu)(droplet_rescaled)

Create surface

To create a surface from the rescaled and binarized droplet data, we use napari-process-points-and-surfaces, which provides the marching-cubes algorithm to create a surface from a binary image.

surface = frame_by_frame(nppas.label_to_surface)(droplet_binary, 1)
surface_smooth = frame_by_frame(nppas.smooth_surface)(surface)

viewer = napari.Viewer(ndisplay=3)
viewer.add_image(droplet_rescaled, name="droplet")
viewer.add_surface(surface_smooth, name="droplet")
napari.utils.nbscreenshot(viewer)

Measureing intensity

We now calculate the surface normals of the surface and use them to measure the intensity of the signal on the surface. To be able to measure intensity in a rregion around the surface, we can pass the length_multiplier value to the function, which will create a vector with the length of the surface normal multiplied by the length_multiplier value. The intensity of the signal on the vector is then measured.

Moreover, the center parameter determines whether the vector is centered on the surface or not. If center is set to True, the vector is centered on the surface. If center is set to False, the vector can point outwards or inwards, depending on the sign of the length_multiplier parameter.

normals = vectors.normal_vectors_on_surface(surface_smooth, length_multiplier=5, center=True)
vectors_LDtuple = measurements.intensity._sample_intensity_along_vector(normals, droplet_rescaled)

layer = napari.layers.Layer.create(vectors_LDtuple[0], vectors_LDtuple[1], vectors_LDtuple[2])
vector_layer = viewer.add_layer(layer)

napari.utils.nbscreenshot(viewer)

Projecting vector measurements on surface

Lastly, we can project the vector measurements on the surface. The measurements are stored in a features table that is attached to the last added vectors layer:

viewer.layers[-1].features.head()

	step_0	step_1	step_2	step_3	step_4	intensity_mean	intensity_std	intensity_max	intensity_min
0	163.143768	119.508354	70.187156	36.135386	20.747821	81.944497	59.131438	163.143768	20.747821
1	166.786490	124.618316	71.018596	37.731357	23.359034	84.702758	60.182248	166.786490	23.359034
2	175.819763	138.049967	81.683062	39.697817	16.711681	90.392458	66.414832	175.819763	16.711681
3	172.347860	138.653992	80.542078	39.854559	18.776891	90.035076	64.855847	172.347860	18.776891
4	160.743460	118.354990	68.113843	33.601110	19.586847	80.080050	59.022170	160.743460	19.586847

To colour the surface according to either of these measurements, we can simply do so by the following lines of code:

values_to_visualize = viewer.layers[-1].features['intensity_max'].values
new_surface_tuple = (surface_smooth[0], surface_smooth[1], values_to_visualize)

vector_layer.visible = False
viewer.add_surface(new_surface_tuple, name="droplet", colormap='inferno')
napari.utils.nbscreenshot(viewer)