The following tokens are available and associated with the Kasthuri2015 cell paper. There are several different versions of the data, depending on the resolution and characteristics required:
Defined on the dataset extent (http://openconnecto.me/ocp/ca/kat11segments/info/)
This data is related to the recently published Cell Paper entitled a Saturated Reconstruction of a Volume of Neocortex. AC3 and AC4 are small reference datasets containing a variety of manually annotated “gold standard” annotations and corresponding machine vision results. These data are found on the server openconnecto.me. Clicking on the channel name will lead to an example slice in your browser.
| Token | Channel | Description | X Extent | Y Extent | Z Extent | Resolution |
|---|---|---|---|---|---|---|
| ac3ac4 | ac3\_neuron\_truth | raw neuron label gold standard data | 5472, 6496 | 8712, 9736 | 1000, 1256 | 1 |
| ac3ac4 | ac3\_synapse\_truth | raw synapse label gold standard data | 5472, 6496 | 8712, 9736 | 1000, 1256 | 1 |
| ac3ac4 | ac3membraneIDSIA | computer vision membranes from isbi 2013 | 5472, 6496 | 8712, 9736 | 1156, 1256 | 1 |
| ac3ac4 | ac4\_neuron\_truth | raw neuron label gold standard data | 4400, 5424 | 5440, 6464 | 1100, 1200 | 1 |
| ac3ac4 | ac4\_synapse\_truth | raw synapse label gold standard data | 4400, 5424 | 5440, 6464 | 1100, 1200 | 1 |
| ac3ac4 | ac4membraneIDSIA | computer vision membranes from isbi 2013 | 4400, 5424 | 5440, 6464 | 1100, 1200 | 1 |
These annotations were produced with a variety of machine vision algorithms (documented elsewhere). These may be used as inputs to algorithms or for comparison purposes (citations to follow).
| Token | Channel | Description | X Extent | Y Extent | Z Extent | Resolution |
|---|---|---|---|---|---|---|
| cv_kasthuri11_membrane_2014 | image | computer vision membrane detection reference for kasthuri volume | 2300, 8300 | 4300, 9300 | 1, 1850 | 1 |
| cv_kasthuri11_vesicle_2014 | annotation | computer vision vesicle detection reference for kasthuri volume | 4400, 5424 | 5440, 6464 | 1100, 1200 | 1 |
| cv_ac3_membrane_2014 | image | computer vision membrane detection reference for ac3 created for i2g paper | 5200, 6700 | 8500, 10000 | 975, 1275 | 1 |
| cv_ac3_vesicle_2014 | annotation | computer vision vesicle detection for i2g paper | 5200, 6700 | 8500, 10000 | 975, 1275 | 1 |
In [1]:
# Example Data Pull @ scale 3 for in memory operations
import numpy as np
import ndio.remote.neurodata as neurodata
import ndio.ramon as ramon
import time
import ndio
start = time.time()
token = 'kasthuri2015_ramon_v4'
channel = 'neurons' #already masked to the 3 cylinders
res = 3
nd = neurodata()
im = nd.get_volume(token, channel, 694, 1794, 1750, 2460, 1004, 1379, resolution=3)
if False:
# save data in matlab format
data = {}
data['im'] = im.cutout
sio.savemat('downloaded_data.mat',data)
In [2]:
import pylab as plt
%matplotlib inline
plt.imshow(im.cutout[:,:,50].T) #need to transpose to be pythonic
plt.show()
In [5]:
# Second example - save full slices to disk
# Example:
# python download-as-png.py kasthuri11 image 3 1100 1110
# (Will download slices 1100-1110 of the kasthuri11 dataset at resolution 3)
import ndio.remote.neurodata as neurodata
import ndio.convert.png as ndpng
import os.path
import sys
nd = neurodata()
token = 'kat11segments'
channel = 'annotation'
resolution = 3
z_start = 1100
z_stop = 1110
dir_out = '~/pngtest/'
img_size = nd.get_image_size(token, resolution=resolution)
img_offset = nd.get_image_offset(token, resolution=resolution)
x_start = img_offset[0]
x_stop = img_size[0]
y_start = img_offset[1]
y_stop = img_size[1]
query = {
"token": token,
"channel": channel,
"x_start": x_start,
"y_start": y_start,
"z_start": z_start,
"x_stop": x_stop,
"y_stop": y_stop,
"z_stop": z_stop,
"resolution": resolution
}
vol = nd.get_cutout(**query)
for z in range(0,vol.shape[2]):
filename = '{}_{}_{}_{}_{}_{}_{}_{}.png'.format(token,channel,x_start,
x_stop,y_start,y_stop,
z_start+z, resolution)
# TODO Confirm that transpose is fixed
ndpng.save_collection(os.path.join(dir_out, filename), vol[:,:,z])
In [ ]: