Hacking the Human Body 2022

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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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.

Sample annotation mask from Hacking the Human Body 2022

Sample image from Hacking the Human Body 2022

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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.

Rows 1-5 of 5

Class ㅤ	Images ㅤ	Objects ㅤ	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.

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.

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.

Rows 1-10 of 351

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
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 = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Please visit dataset homepage to download the data.

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