Dataset Viewer
Auto-converted to Parquet Duplicate
image
image
label
image
crop_name
string
axis
string
slice
int32
jrc_hela-2/recon-1/crop28
z
352
jrc_mus-liver-zon-2/recon-1/crop366
y
88
jrc_jurkat-1/recon-1/crop38
z
133
jrc_mus-kidney/recon-1/crop231
x
305
jrc_mus-liver/recon-1/crop133
x
279
jrc_hela-2/recon-1/crop58
x
111
jrc_mus-liver-zon-2/recon-1/crop358
y
1,038
jrc_hela-2/recon-1/crop6
z
270
jrc_hela-2/recon-1/crop54
z
237
jrc_mus-liver-zon-1/recon-1/crop337
x
832
jrc_mus-liver-zon-1/recon-1/crop279
x
292
jrc_mus-liver-zon-1/recon-1/crop322
x
324
jrc_hela-3/recon-1/crop111
x
908
jrc_cos7-1a/recon-1/crop252
x
5
jrc_mus-kidney-3/recon-1/crop472
z
183
jrc_mus-liver-zon-2/recon-1/crop357
z
33
jrc_mus-liver-zon-2/recon-1/crop357
y
229
jrc_mus-liver-zon-1/recon-1/crop270
x
54
jrc_mus-liver-zon-1/recon-1/crop349
z
549
jrc_mus-liver/recon-1/crop417
z
248
jrc_ut21-1413-003/recon-1/crop220
z
258
jrc_mus-liver-zon-2/recon-1/crop368
y
167
jrc_sum159-4/recon-1/crop186
z
73
jrc_mus-liver-zon-1/recon-1/crop345
z
127
jrc_mus-liver-zon-2/recon-1/crop357
x
243
jrc_mus-liver-zon-1/recon-1/crop386
x
5
jrc_hela-3/recon-1/crop100
z
516
jrc_jurkat-1/recon-1/crop67
x
398
jrc_mus-liver-zon-1/recon-1/crop320
z
56
jrc_zf-cardiac-1/recon-1/crop381
y
195
jrc_cos7-1a/recon-1/crop256
y
231
jrc_hela-3/recon-1/crop111
y
473
jrc_mus-liver-zon-1/recon-1/crop277
y
268
jrc_hela-2/recon-1/crop94
z
167
jrc_mus-kidney/recon-1/crop158
x
198
jrc_mus-liver-zon-1/recon-1/crop337
y
655
jrc_hela-2/recon-1/crop155
z
702
jrc_ut21-1413-003/recon-1/crop195
z
44
jrc_cos7-1a/recon-1/crop252
z
175
jrc_macrophage-2/recon-1/crop90
y
90
jrc_sum159-4/recon-1/crop211
y
702
jrc_hela-2/recon-1/crop13
x
208
jrc_macrophage-2/recon-1/crop77
z
233
jrc_macrophage-2/recon-1/crop39
x
103
jrc_mus-liver/recon-1/crop139
x
30
jrc_hela-3/recon-1/crop111
x
224
jrc_mus-liver-zon-1/recon-1/crop336
y
672
jrc_mus-liver/recon-1/crop125
z
14
jrc_sum159-1/recon-1/crop26
x
135
jrc_ut21-1413-003/recon-1/crop192
x
147
jrc_hela-3/recon-1/crop51
y
342
jrc_mus-liver-zon-1/recon-1/crop289
z
658
jrc_cos7-1b/recon-1/crop255
z
378
jrc_mus-liver-zon-1/recon-1/crop337
y
311
jrc_sum159-1/recon-1/crop83
y
118
jrc_mus-nacc-1/recon-1/crop115
x
598
jrc_mus-liver-zon-1/recon-1/crop282
x
721
jrc_jurkat-1/recon-1/crop91
x
286
jrc_sum159-4/recon-1/crop201
z
76
jrc_sum159-1/recon-1/crop26
z
59
jrc_sum159-4/recon-1/crop208
x
338
jrc_macrophage-2/recon-1/crop110
x
853
jrc_ut21-1413-003/recon-1/crop225
z
216
jrc_hela-2/recon-1/crop58
z
34
jrc_cos7-1b/recon-1/crop258
y
125
jrc_mus-liver/recon-1/crop135
x
40
jrc_hela-2/recon-1/crop28
x
53
jrc_jurkat-1/recon-1/crop43
y
177
jrc_mus-kidney/recon-1/crop230
y
112
jrc_mus-liver/recon-1/crop175
z
201
jrc_macrophage-2/recon-1/crop76
z
210
jrc_cos7-1b/recon-1/crop238
y
83
jrc_mus-liver-zon-2/recon-1/crop333
y
75
jrc_hela-3/recon-1/crop60
x
244
jrc_sum159-1/recon-1/crop83
x
196
jrc_mus-kidney/recon-1/crop158
z
143
jrc_ut21-1413-003/recon-1/crop214
z
190
jrc_mus-liver/recon-1/crop124
z
17
jrc_hela-2/recon-1/crop4
z
3
jrc_fly-mb-1a/recon-1/crop123
x
136
jrc_sum159-4/recon-1/crop216
x
106
jrc_ctl-id8-1/recon-1/crop117
x
229
jrc_mus-liver-zon-1/recon-1/crop313
y
24
jrc_mus-liver-zon-1/recon-1/crop351
z
349
jrc_hela-2/recon-1/crop6
z
436
jrc_mus-liver-zon-1/recon-1/crop266
x
85
jrc_mus-liver/recon-1/crop135
z
92
jrc_macrophage-2/recon-1/crop89
z
320
jrc_macrophage-2/recon-1/crop40
y
210
jrc_sum159-1/recon-1/crop80
y
275
jrc_hela-2/recon-1/crop13
x
161
jrc_mus-kidney/recon-1/crop163
y
158
jrc_mus-liver/recon-1/crop139
y
38
jrc_sum159-4/recon-1/crop186
y
377
jrc_mus-liver-zon-1/recon-1/crop276
y
148
jrc_sum159-4/recon-1/crop210
z
383
jrc_jurkat-1/recon-1/crop93
z
79
jrc_mus-liver/recon-1/crop145
y
503
jrc_mus-liver/recon-1/crop135
x
12
jrc_cos7-1b/recon-1/crop249
y
111
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), together with their corresponding labeled segmentation masks. The slices were prepared and pushed to the HF datasets Hub with this script.

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=400x400 at 0xFFFA15823BF0>,
'label': <PIL.PngImagePlugin.PngImageFile image mode=L size=400x400 at 0xFFFA15AE91F0>,
'crop_name': 'jrc_hela-2/recon-1/crop28',
'axis': 'z',
'slice': 352
}

where:

  • image contains the actual 2D slice encoded as a PIL.Image object.
  • label contains the labeled segmentation masks 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

Downloads last month
121