"""
Base functions to extract information from a single cell
These functions are automatically read, so only add new functions with
the same arguments as the existing ones.
np.where is used to cover for cases where z>1
TODO: Implement membrane functions when needed
"""
# Alan: why aren't we using skimage's regionprops?
import numpy as np
from scipy import ndimage
from sklearn.cluster import KMeans
def area(cell_mask):
# find the area of a cell mask
return np.sum(cell_mask, dtype=int)
def eccentricity(cell_mask):
min_ax, maj_ax = min_maj_approximation(cell_mask)
return np.sqrt(maj_ax**2 - min_ax**2) / maj_ax
def mean(cell_mask, trap_image):
"""
Finds the mean of the pixels in the cell.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
trap_image: 2d array
"""
return np.mean(trap_image[np.where(cell_mask)], dtype=float)
def median(cell_mask, trap_image):
"""
Finds the median of the pixels in the cell.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
trap_image: 2d array
"""
return np.median(trap_image[np.where(cell_mask)])
def max2p5pc(cell_mask, trap_image):
"""
Finds the mean of the brightest 2.5% of pixels in the cell.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
trap_image: 2d array
"""
# number of pixels in mask
npixels = cell_mask.sum()
top_pixels = int(np.ceil(npixels * 0.025))
# sort pixels in cell
sorted_vals = np.sort(trap_image[np.where(cell_mask)], axis=None)
# find highest 2.5%
top_vals = sorted_vals[-top_pixels:]
# find mean of these highest pixels
max2p5pc = np.mean(top_vals, dtype=float)
return max2p5pc
def max5px(cell_mask, trap_image):
"""
Finds the mean of the five brightest pixels in the cell.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
trap_image: 2d array
"""
# sort pixels in cell
sorted_vals = np.sort(trap_image[np.where(cell_mask)], axis=None)
top_vals = sorted_vals[-5:]
# find mean of five brightest pixels
max5px = np.mean(top_vals, dtype=float)
return max5px
def max5px_med(cell_mask, trap_image):
"""
Finds the mean of the five brightest pixels in the cell divided by the median pixel value.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
trap_image: 2d array
"""
# sort pixels in cell
sorted_vals = np.sort(trap_image[np.where(cell_mask)], axis=None)
top_vals = sorted_vals[-5:]
# find mean of five brightest pixels
max5px = np.mean(top_vals, dtype=float)
# find the median
med = np.median(sorted_vals)
return max5px / med
def max2p5pc_med(cell_mask, trap_image):
"""
Finds the mean of the brightest 2.5% of pixels in the cell
divided by the median pixel value.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
trap_image: 2d array
"""
# number of pixels in mask
npixels = cell_mask.sum()
top_pixels = int(np.ceil(npixels * 0.025))
# sort pixels in cell
sorted_vals = np.sort(trap_image[np.where(cell_mask)], axis=None)
# find highest 2.5%
top_vals = sorted_vals[-top_pixels:]
# find mean of these highest pixels
max2p5pc = np.mean(top_vals, dtype=float)
med = np.median(sorted_vals)
return max2p5pc / med
def std(cell_mask, trap_image):
"""
Finds the standard deviation of the values of the pixels in the cell.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
trap_image: 2d array
"""
return np.std(trap_image[np.where(cell_mask)], dtype=float)
def k2_top_median(cell_mask, trap_image):
"""
Finds the medians of the major and minor clusters after clustering the pixels in the cell into two clusters.
These medians might be useful if the cell has a large vacuole.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
trap_image: 2d array
Returns
-------
medians: tuple of floats
The median of the major cluster and the median of the minor cluster.
"""
if np.any(cell_mask):
X = trap_image[np.where(cell_mask)].reshape(-1, 1)
# cluster pixels in cell into two clusters
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
high_clust_id = kmeans.cluster_centers_.argmax()
low_clust_id = kmeans.cluster_centers_.argmin()
# find the median of pixels in the largest cluster
major_cluster = X[kmeans.predict(X) == high_clust_id]
major_median = np.median(major_cluster, axis=None)
# find the median of pixels in the smallest cluster
minor_cluster = X[kmeans.predict(X) == low_clust_id]
minor_median = np.median(minor_cluster, axis=None)
return major_median, minor_median
else:
return np.nan, np.nan
def volume(cell_mask):
"""
Estimates the volume of the cell assuming it is an ellipsoid with the mask providing a cross-section through the median plane of the ellipsoid.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
"""
min_ax, maj_ax = min_maj_approximation(cell_mask)
return (4 * np.pi * min_ax**2 * maj_ax) / 3
def conical_volume(cell_mask):
padded = np.pad(cell_mask, 1, mode="constant", constant_values=0)
nearest_neighbor = (
ndimage.morphology.distance_transform_edt(padded == 1) * padded
)
return 4 * (nearest_neighbor.sum())
def spherical_volume(cell_mask):
'''
Estimates the volume of the cell assuming it is a sphere with the mask providing a cross-section through the median plane of the sphere.
Parameters
----------
cell_mask: 2d array
Segmentation mask for the cell
'''
area = cell_mask.sum()
r = np.sqrt(area / np.pi)
return (4 * np.pi * r**3) / 3
def min_maj_approximation(cell_mask):
"""
Finds the lengths of the minor and major axes of an ellipse from a cell mask.
Parameters
----------
cell_mask: 3d array
Segmentation masks for cells
"""
padded = np.pad(cell_mask, 1, mode="constant", constant_values=0)
nn = ndimage.morphology.distance_transform_edt(padded == 1) * padded
dn = ndimage.morphology.distance_transform_edt(nn - nn.max()) * padded
cone_top = ndimage.morphology.distance_transform_edt(dn == 0) * padded
min_ax = np.round(nn.max())
maj_ax = np.round(dn.max() + cone_top.sum() / 2)
return min_ax, maj_ax