Quantifying wound healing/cell migration in 3D

Timo Friedrich and Jan Kaslin showed me some 3D fluorescent labelled cells invading a wound that they would like to quantify. Initially, quantification along a single axis is acceptable. Let's load a sample dataset:



In [1]:

    
%matplotlib inline
import os
import numpy as np
from matplotlib import pyplot as plt

datadir = '/Users/nuneziglesiasj/Data/zebrafish-lesion'
os.chdir(datadir)



In [2]:

    
from gala import imio
im = imio.read_image_stack('sci-quant.tif')
im.shape, im.dtype, im.min(), im.max()









    



WARNING:root: progressbar package not installed. Progress cannot be shown. See http://pypi.python.org/simple/progressbar or type "sudo easy_install progressbar" to fix.






    



pylibtiff not available: http://www.lfd.uci.edu/~gohlke/pythonlibs/#pylibtiff






    Out[2]:





((512, 512, 1872), dtype('int64'), 0, 255)

That took way too long. Additionally, the volume is the wrong shape! I'm going to save the image as an hdf5 file and comment out the above lines.



In [4]:

    
im8 = np.ascontiguousarray(im.astype(np.uint8).transpose((2, 0, 1)).reshape(104, 18, 512, 512))









    Out[4]:





<matplotlib.image.AxesImage at 0x102c4af90>



In [5]:

    
plt.imshow(im8[0, 7, ...])









    Out[5]:





<matplotlib.image.AxesImage at 0x102c7cd90>



In [7]:

    
imio.write_h5_stack(im8, 'sci-quant.lzf.h5', compression='lzf')

The compression has worked really well (3x size reduction)! We start over with the right file format.