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image
imagewidth (px)
62
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label
imagewidth (px)
62
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crop_name
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288 values
axis
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3 values
slice
int32
0
3k
jrc_mus-liver-zon-1/recon-1/crop347
y
333
jrc_mus-liver-zon-2/recon-1/crop357
z
1,098
jrc_cos7-1b/recon-1/crop249
x
214
jrc_hela-2/recon-1/crop28
y
149
jrc_hela-2/recon-1/crop57
x
12
jrc_mus-liver-zon-1/recon-1/crop347
z
312
jrc_cos7-1a/recon-1/crop254
x
299
jrc_mus-liver-zon-1/recon-1/crop280
z
64
jrc_mus-liver-zon-1/recon-1/crop275
z
57
jrc_cos7-1a/recon-1/crop243
x
32
jrc_hela-3/recon-1/crop100
x
40
jrc_mus-liver-zon-1/recon-1/crop289
x
619
jrc_mus-liver-zon-1/recon-1/crop279
x
146
jrc_mus-kidney/recon-1/crop156
x
201
jrc_mus-liver-zon-1/recon-1/crop349
x
778
jrc_mus-liver-zon-1/recon-1/crop347
x
191
jrc_mus-liver-zon-2/recon-1/crop340
y
38
jrc_mus-liver/recon-1/crop171
x
14
jrc_cos7-1a/recon-1/crop243
x
33
jrc_mus-liver-zon-2/recon-1/crop358
z
1,113
jrc_mus-liver-zon-2/recon-1/crop353
z
250
jrc_mus-liver-zon-1/recon-1/crop266
z
152
jrc_cos7-1b/recon-1/crop249
x
374
jrc_cos7-1b/recon-1/crop259
y
384
jrc_fly-vnc-1/recon-1/crop173
z
95
jrc_mus-liver-zon-1/recon-1/crop273
x
2
jrc_mus-liver-zon-1/recon-1/crop282
x
1,777
jrc_ut21-1413-003/recon-1/crop222
z
102
jrc_sum159-4/recon-1/crop213
z
165
jrc_cos7-1a/recon-1/crop257
x
744
jrc_mus-liver/recon-1/crop416
x
192
jrc_mus-liver-zon-1/recon-1/crop282
y
524
jrc_cos7-1a/recon-1/crop292
y
183
jrc_hela-3/recon-1/crop61
z
82
jrc_jurkat-1/recon-1/crop36
z
181
jrc_hela-2/recon-1/crop58
y
139
jrc_cos7-1b/recon-1/crop238
x
150
jrc_cos7-1b/recon-1/crop245
y
307
jrc_mus-liver-zon-1/recon-1/crop349
z
969
jrc_cos7-1a/recon-1/crop239
z
53
jrc_macrophage-2/recon-1/crop88
y
76
jrc_hela-2/recon-1/crop59
y
106
jrc_mus-liver-zon-2/recon-1/crop355
y
136
jrc_cos7-1a/recon-1/crop256
z
142
jrc_mus-liver-zon-1/recon-1/crop337
y
900
jrc_hela-3/recon-1/crop85
z
175
jrc_mus-liver-zon-2/recon-1/crop340
z
3
jrc_mus-liver-zon-1/recon-1/crop289
z
159
jrc_mus-liver-zon-2/recon-1/crop353
x
270
jrc_ut21-1413-003/recon-1/crop190
y
49
jrc_ctl-id8-1/recon-1/crop117
x
127
jrc_mus-liver-zon-2/recon-1/crop357
z
1,122
jrc_sum159-1/recon-1/crop84
x
147
jrc_macrophage-2/recon-1/crop32
y
174
jrc_fly-mb-1a/recon-1/crop122
y
168
jrc_mus-liver-zon-2/recon-1/crop342
x
6
jrc_fly-vnc-1/recon-1/crop79
z
1
jrc_cos7-1b/recon-1/crop240
y
351
jrc_ut21-1413-003/recon-1/crop225
y
142
jrc_mus-kidney/recon-1/crop231
z
205
jrc_ut21-1413-003/recon-1/crop222
x
62
jrc_ut21-1413-003/recon-1/crop199
y
118
jrc_mus-liver-zon-1/recon-1/crop269
x
187
jrc_cos7-1a/recon-1/crop252
y
15
jrc_macrophage-2/recon-1/crop39
y
62
jrc_cos7-1a/recon-1/crop234
z
304
jrc_mus-liver-zon-1/recon-1/crop298
z
145
jrc_cos7-1b/recon-1/crop259
z
388
jrc_mus-liver/recon-1/crop417
y
84
jrc_fly-vnc-1/recon-1/crop79
x
201
jrc_sum159-4/recon-1/crop211
x
525
jrc_zf-cardiac-1/recon-1/crop381
z
310
jrc_macrophage-2/recon-1/crop110
z
98
jrc_mus-heart-1/recon-1/crop423
x
167
jrc_zf-cardiac-1/recon-1/crop380
z
205
jrc_sum159-4/recon-1/crop208
z
84
jrc_mus-liver-zon-1/recon-1/crop282
x
2,188
jrc_cos7-1b/recon-1/crop238
y
600
jrc_zf-cardiac-1/recon-1/crop379
y
456
jrc_cos7-1b/recon-1/crop238
x
492
jrc_mus-kidney/recon-1/crop184
z
304
jrc_mus-liver-zon-1/recon-1/crop337
x
839
jrc_sum159-1/recon-1/crop81
z
34
jrc_mus-kidney-3/recon-1/crop472
y
147
jrc_macrophage-2/recon-1/crop89
x
99
jrc_hela-3/recon-1/crop101
x
169
jrc_jurkat-1/recon-1/crop36
y
47
jrc_mus-kidney/recon-1/crop231
z
36
jrc_cos7-1b/recon-1/crop259
y
75
jrc_mus-heart-1/recon-1/crop452
x
328
jrc_jurkat-1/recon-1/crop38
z
113
jrc_mus-kidney/recon-1/crop148
y
73
jrc_hela-2/recon-1/crop94
y
40
jrc_hela-2/recon-1/crop8
x
36
jrc_mus-liver-zon-2/recon-1/crop358
y
298
jrc_sum159-4/recon-1/crop189
x
43
jrc_sum159-4/recon-1/crop217
z
154
jrc_mus-kidney/recon-1/crop221
x
121
jrc_mus-liver-zon-2/recon-1/crop358
y
1,483
jrc_mus-kidney-3/recon-1/crop472
z
197
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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