Pixel classification in multi-channel images

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Pixel classification in multi-channel images#

APOC also accepts lists of images as input for training and prediction. This can be used for example for semantic segmentation in multi-channel images showing nuclei, membranes and cytoplasm in between.

from skimage.data import cells3d
from skimage.io import imsave, imread
import napari
import numpy as np
import matplotlib.pyplot as plt
from pyclesperanto_prototype import imshow

image = cells3d()

image_ch1 = image[30, 0]
imshow(image_ch1)
../_images/7ec8e0d876b58ce19d48b9dd08489d1b06248bb0c63debf260a3d3fe297607b6.png 
image_ch2 = image[30, 1]
imshow(image_ch2)
../_images/93ef221d87d0e2f6ad74bddf4f477d0c50e93d0ff2b71d57d86dcac811822655.png 
filename = '../../data/cells_annotation.tif'
            
annotation = imread(filename)

fix, axs = plt.subplots(2,2, figsize=(10,10))

imshow(image_ch1, plot=axs[0,0], colormap="Greens_r")
imshow(image_ch2, plot=axs[0,1], colormap="Purples_r")
imshow(annotation, labels=True, plot=axs[1,0])
imshow(image_ch1, continue_drawing=True, plot=axs[1,1], colormap="Greens_r", alpha=0.5)
imshow(image_ch2, continue_drawing=True, plot=axs[1,1], colormap="Purples_r", alpha=0.5)
imshow(annotation, labels=True, plot=axs[1,1], alpha=0.5)
../_images/2a3a79a5abede0b40362db0e096093c1a9ca915f7db56798db995e647c38c988.png

Training#

from apoc import PixelClassifier

# define features
features = "sobel_of_gaussian_blur=2 laplace_box_of_gaussian_blur=2 gaussian_blur=2 sobel_of_gaussian_blur=4 laplace_box_of_gaussian_blur=4 gaussian_blur=4"

# this is where the model will be saved
cl_filename = 'test.cl'

clf = PixelClassifier(opencl_filename=cl_filename)
clf.train(features=features, ground_truth=annotation, image=[image_ch1, image_ch2])

Prediction#

result = clf.predict(image=[image_ch1, image_ch2])
imshow(result, labels=True)
../_images/689c857017b386314a1bbb59460fd511fd8db980b72036549f18ad1c77c81c68.png
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