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Hacking the Human Body 2022 Dataset

35251
Tagbiology
Tasksemantic segmentation
Release YearMade in 2022
Licenseunknown

Introduction #

Addison Howard, Almost Sohier, Cecilia Lindskoget al.

Hacking the Human Body 2022 Dataset is a part of HuBMAP + HPA - Hacking the Human Body competition, where you need to identify and segment functional tissue units (FTUs) across five human organs - prostate, spleen, lung, kidney, and largeintestine. It helps to accelerate the world’s understanding of the relationships between cell and tissue organization. With a better idea of the relationship of cells, researchers will have more insight into the function of cells that impact human health.

This competition uses data from two different consortia, the Human Protein Atlas (HPA) and Human BioMolecular Atlas Program (HuBMAP). The training dataset consists of data from public HPA data, the public test set is a combination of private HPA data and HuBMAP data, and the private test set contains only HuBMAP data. Adapting models to function properly when presented with data that was prepared using a different protocol will be one of the core challenges of this competition. While this is expected to make the problem more difficult, developing models that generalize is a key goal of this endeavor. This competition uses a hidden test.

Dataset includes metadata for the train/test set. Only the first few rows of the test set are available for download:

  • id - The image ID.
  • organ - The organ that the biopsy sample was taken from.
  • data_source - Whether the image was provided by HuBMAP or HPA.
  • img_height - The height of the image in pixels.
  • img_width - The width of the image in pixels.
  • pixel_size - The height/width of a single pixel from this image in micrometers. All HPA images have a pixel size of 0.4 µm. For HuBMAP imagery the pixel size is 0.5 µm for kidney, 0.2290 µm for large intestine, 0.7562 µm for lung, 0.4945 µm for spleen, and 6.263 µm for prostate.
  • tissue_thickness - The thickness of the biopsy sample in micrometers. All HPA images have a thickness of 4 µm. The HuBMAP samples have tissue slice thicknesses 10 µm for kidney, 8 µm for large intestine, 4 µm for spleen, 5 µm for lung, and 5 µm for prostate.
  • rle - The target column. A run length encoded copy of the annotations. Provided for the training set only.
  • age - The patient’s age in years. Provided for the training set only.
  • sex - The sex of the patient. Provided for the training set only.
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Dataset LinkHomepageDataset LinkAlternative Kaggle

Summary #

Hacking the Human Body 2022 is a dataset for a semantic segmentation task. It is used in the biological research. Possible applications of the dataset could be in the medical industry.

The dataset consists of 352 images with 351 labeled objects belonging to 5 different classes including kidney, prostate, largeintestine, and other: spleen and lung.

Images in the Hacking the Human Body 2022 dataset have pixel-level semantic segmentation annotations. There is 1 unlabeled image (i.e. without annotations). There are 2 splits in the dataset: train (351 images) and test (1 images). Also, the dataset includes age, sex, tissue_thickness for train split, organ and tissue_thickness for test split. The dataset was released in 2022.

Here are the visualized examples for the classes:

Explore #

Hacking the Human Body 2022 dataset has 352 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
OpenSample annotation mask from Hacking the Human Body 2022Sample image from Hacking the Human Body 2022
👀
Have a look at 352 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 5 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-5 of 5
Class
Images
Objects
Count on image
average
Area on image
average
kidney
mask
99
99
1
3.02%
prostate
mask
93
93
1
15.4%
largeintestine
mask
58
58
1
19.34%
spleen
mask
53
53
1
10.1%
lung
mask
48
48
1
2.08%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-5 of 5
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
kidney
mask
99
3.02%
7.95%
0.31%
199px
6.63%
2725px
92.89%
1436px
48.56%
198px
6.6%
2796px
93.2%
prostate
mask
93
15.4%
42.93%
0.99%
487px
16.23%
2583px
96.38%
1948px
65.72%
424px
14.13%
2703px
96.52%
largeintestine
mask
58
19.34%
34.83%
2.11%
558px
18.6%
2817px
93.9%
2321px
77.38%
814px
27.13%
2799px
93.3%
spleen
mask
53
10.1%
34.33%
0.81%
502px
16.73%
2792px
93.07%
1816px
60.9%
397px
13.23%
2660px
90.68%
lung
mask
48
2.08%
8.89%
0.07%
92px
3.07%
2489px
82.97%
1177px
39.29%
93px
3.1%
2201px
73.37%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 351 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 351
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
prostate
mask
435.png
3000 x 3000
2316px
77.2%
2400px
80%
19.62%
2
kidney
mask
29238.png
3000 x 3000
245px
8.17%
265px
8.83%
0.55%
3
kidney
mask
26174.png
3000 x 3000
199px
6.63%
338px
11.27%
0.56%
4
prostate
mask
17143.png
3000 x 3000
2557px
85.23%
1837px
61.23%
17.53%
5
largeintestine
mask
31406.png
3000 x 3000
2608px
86.93%
2450px
81.67%
24.66%
6
lung
mask
23252.png
3000 x 3000
1542px
51.4%
1652px
55.07%
4.21%
7
lung
mask
4412.png
3000 x 3000
663px
22.1%
1216px
40.53%
2.59%
8
spleen
mask
8894.png
3000 x 3000
2395px
79.83%
2417px
80.57%
25.92%
9
kidney
mask
31709.png
3000 x 3000
2620px
87.33%
1427px
47.57%
5.92%
10
prostate
mask
4802.png
3000 x 3000
1178px
39.27%
1393px
46.43%
5.25%

License #

License is unknown for the Hacking the Human Body 2022 dataset.

Source

Citation #

If you make use of the Hacking the Human Body 2022 data, please cite the following reference:

@misc{hubmap-organ-segmentation,
  author = {Addison Howard, AlmostSohier, Cecilia Lindskog, Emma Lundberg, Katy Borner, Leah Godwin, Shriya, Sohier Dane, Trang Le, Yashvardhan Jain},
  title = {HuBMAP + HPA - Hacking the Human Body},
  publisher = {Kaggle},
  year = {2022},
  url = {https://kaggle.com/competitions/hubmap-organ-segmentation}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-hacking-the-human-body-dataset,
  title = { Visualization Tools for Hacking the Human Body 2022 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/hacking-the-human-body-2022 } },
  url = { https://datasetninja.com/hacking-the-human-body-2022 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Please visit dataset homepage to download the data.

. . .

Disclaimer #

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