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289 values
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3 values
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2.98k
jrc_mus-heart-1/recon-1/crop423
y
221
jrc_cos7-1a/recon-1/crop237
z
178
jrc_mus-liver/recon-1/crop125
y
156
jrc_mus-liver-zon-1/recon-1/crop269
z
39
jrc_mus-liver-zon-2/recon-1/crop354
z
179
jrc_jurkat-1/recon-1/crop47
x
253
jrc_mus-liver-zon-1/recon-1/crop273
x
167
jrc_jurkat-1/recon-1/crop112
z
239
jrc_mus-liver/recon-1/crop172
y
228
jrc_mus-liver-zon-1/recon-1/crop349
z
881
jrc_hela-2/recon-1/crop6
x
111
jrc_hela-3/recon-1/crop27
z
152
jrc_sum159-4/recon-1/crop211
z
712
jrc_jurkat-1/recon-1/crop70
y
76
jrc_mus-liver/recon-1/crop175
x
210
jrc_mus-liver/recon-1/crop136
x
26
jrc_ut21-1413-003/recon-1/crop226
y
112
jrc_hela-3/recon-1/crop65
z
181
jrc_ctl-id8-1/recon-1/crop118
z
171
jrc_sum159-1/recon-1/crop98
z
126
jrc_mus-liver-zon-1/recon-1/crop411
x
125
jrc_mus-kidney/recon-1/crop129
y
126
jrc_mus-liver-zon-2/recon-1/crop354
y
276
jrc_mus-liver/recon-1/crop132
x
116
jrc_cos7-1b/recon-1/crop238
x
858
jrc_mus-liver/recon-1/crop175
z
351
jrc_jurkat-1/recon-1/crop182
z
7
jrc_mus-liver-zon-2/recon-1/crop358
x
1,455
jrc_mus-liver-zon-2/recon-1/crop367
x
938
jrc_fly-vnc-1/recon-1/crop79
x
221
jrc_mus-liver-zon-2/recon-1/crop362
y
390
jrc_ut21-1413-003/recon-1/crop190
y
188
jrc_mus-heart-1/recon-1/crop423
z
364
jrc_mus-kidney/recon-1/crop179
x
471
jrc_mus-liver-zon-1/recon-1/crop346
y
187
jrc_sum159-4/recon-1/crop203
y
98
jrc_mus-liver-zon-1/recon-1/crop337
y
153
jrc_mus-heart-1/recon-1/crop452
x
326
jrc_hela-2/recon-1/crop7
x
76
jrc_mus-liver-zon-1/recon-1/crop313
z
94
jrc_mus-liver/recon-1/crop177
y
67
jrc_mus-liver-zon-1/recon-1/crop282
x
2,662
jrc_mus-liver-zon-2/recon-1/crop358
y
1,069
jrc_hela-2/recon-1/crop9
x
72
jrc_hela-2/recon-1/crop57
z
76
jrc_mus-liver-zon-1/recon-1/crop345
y
146
jrc_ut21-1413-003/recon-1/crop227
y
131
jrc_hela-2/recon-1/crop54
y
187
jrc_cos7-1a/recon-1/crop237
y
357
jrc_zf-cardiac-1/recon-1/crop379
y
96
jrc_cos7-1a/recon-1/crop256
z
168
jrc_jurkat-1/recon-1/crop67
x
191
jrc_mus-liver/recon-1/crop137
y
70
jrc_macrophage-2/recon-1/crop32
z
12
jrc_hela-3/recon-1/crop87
x
166
jrc_ut21-1413-003/recon-1/crop226
x
25
jrc_cos7-1a/recon-1/crop247
x
230
jrc_sum159-4/recon-1/crop211
y
363
jrc_hela-2/recon-1/crop113
x
196
jrc_mus-heart-1/recon-1/crop452
y
105
jrc_ut21-1413-003/recon-1/crop198
z
186
jrc_ut21-1413-003/recon-1/crop225
x
21
jrc_cos7-1b/recon-1/crop238
y
471
jrc_hela-3/recon-1/crop85
y
34
jrc_hela-2/recon-1/crop57
z
47
jrc_hela-3/recon-1/crop50
x
203
jrc_macrophage-2/recon-1/crop73
x
133
jrc_fly-vnc-1/recon-1/crop78
y
79
jrc_cos7-1b/recon-1/crop240
x
339
jrc_mus-liver-zon-1/recon-1/crop337
x
555
jrc_mus-liver-zon-1/recon-1/crop289
y
742
jrc_mus-heart-1/recon-1/crop452
z
151
jrc_jurkat-1/recon-1/crop180
y
160
jrc_sum159-1/recon-1/crop22
x
113
jrc_mus-liver/recon-1/crop417
z
109
jrc_mus-liver-zon-2/recon-1/crop353
z
167
jrc_jurkat-1/recon-1/crop38
y
28
jrc_ctl-id8-1/recon-1/crop118
z
31
jrc_sum159-4/recon-1/crop202
y
7
jrc_mus-liver-zon-1/recon-1/crop337
y
4
jrc_cos7-1a/recon-1/crop237
y
91
jrc_hela-2/recon-1/crop13
x
59
jrc_mus-liver-zon-1/recon-1/crop275
y
171
jrc_mus-liver-zon-1/recon-1/crop351
z
364
jrc_sum159-4/recon-1/crop206
y
179
jrc_mus-liver-zon-1/recon-1/crop282
y
571
jrc_mus-liver-zon-1/recon-1/crop324
y
444
jrc_ctl-id8-1/recon-1/crop116
x
88
jrc_sum159-4/recon-1/crop211
x
84
jrc_hela-3/recon-1/crop63
z
159
jrc_mus-liver-zon-1/recon-1/crop273
y
26
jrc_hela-2/recon-1/crop57
z
53
jrc_macrophage-2/recon-1/crop49
y
242
jrc_mus-liver/recon-1/crop157
y
75
jrc_mus-liver/recon-1/crop125
x
150
jrc_mus-liver-zon-1/recon-1/crop289
x
200
jrc_hela-3/recon-1/crop111
z
333
jrc_mus-liver-zon-1/recon-1/crop282
z
282
jrc_mus-kidney-glomerulus-2/recon-1/crop421
y
54
jrc_cos7-1a/recon-1/crop292
y
272
End of preview. Expand in Data Studio

CellMap 2D

This dataset contains all 2D slices from the EM volumes used in the CellMap segmentation challenge. The dataset contains all x, y, z slices obtained from a total of 289 3D EM volume crops (the crops come from 22 different samples). The slices are in their native resolution (no resizing).

You can load the dataset as follows (non-streaming mode):

ds = load_dataset("eminorhan/cellmap-2d", split='train')

and then inspect the first data row:

>>> print(ds[0])
>>> {
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=300x300 at 0xFFF93926C850>,
'crop_name': 'jrc_mus-kidney/recon-1/crop129',
'axis': 'z',
'slice': 0
}

where:

  • image contains the actual 2D slice encoded as a PIL.Image object.
  • crop_name is an identifier string indicating the sample and crop names the slice comes from.
  • axis indicates the axis along which the slice was taken (x, y, or z).
  • slice is the slice index along the axis.

Please note that the dataset rows are pre-shuffled to make the shards roughly uniform in size.

License: The data originally come from HHMI Janelia's OpenOrganelle data portal released under the CC-BY-4.0 license.

Citation: If you use these data, please cite the following papers:

@article{heinrich2021whole,
  title={Whole-cell organelle segmentation in volume electron microscopy},
  author={Heinrich, Larissa and Bennett, Davis and Ackerman, David and Park, Woohyun and Bogovic, John and Eckstein, Nils and Petruncio, Alyson and Clements, Jody and Pang, Song and Xu, C Shan and others},
  journal={Nature},
  volume={599},
  number={7883},
  pages={141--146},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

Paper link

@misc{CellMap2024,
  title={CellMap 2024 Segmentation Challenge},
  author={{CellMap Project Team} and Ackerman, David and Ahrens, Misha B. and Aso, Yoshinori and Avetissian, Emma and Bennett, Davis and others},
  year={2024},
  publisher={Janelia Research Campus},
  doi={10.25378/janelia.c.7456966},
}

Paper link

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