BCCD - Dataset Ninja

Summary #

BCCD: Blood Cell Count and Detection is a dataset for an object detection task. Possible applications of the dataset could be in the medical industry and biomedical research.

The dataset consists of 364 images with 4888 labeled objects belonging to 3 different classes including WBC, RBC, and platelets.

Images in the BCCD dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (205 images), val (87 images), and test (72 images). The dataset was released in 2018.

Explore #

BCCD dataset has 364 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.

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Have a look at 364 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 3 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 1-3 of 3

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
WBC➔ rectangle	358	372	1.04	12.08%
RBC➔ rectangle	349	4155	11.91	38.3%
Platelets➔ Unknown	201	361	1.8	1.08%

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 1-3 of 3

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
RBC rectangle	4155	3.49%	7.79%	0%	1px	0.21%	167px	34.79%	101px	21.1%	1px	0.16%	169px	26.41%
WBC rectangle	372	11.64%	27.81%	0.29%	28px	5.83%	287px	59.79%	176px	36.76%	32px	5%	339px	52.97%
Platelets Unknown	361	0.6%	6.4%	0.16%	24px	5%	135px	28.12%	41px	8.58%	20px	3.12%	168px	26.25%

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 4888 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 4888

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➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	108px	22.5%	114px	17.81%	4.01%
2➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	108px	22.5%	114px	17.81%	4.01%
3➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	129px	26.88%	105px	16.41%	4.41%
4➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	129px	26.88%	104px	16.25%	4.37%
5➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	129px	26.88%	105px	16.41%	4.41%
6➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	129px	26.88%	105px	16.41%	4.41%
7➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	128px	26.67%	105px	16.41%	4.38%
8➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	108px	22.5%	100px	15.62%	3.52%
9➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	108px	22.5%	100px	15.62%	3.52%
10➔	RBC rectangle	BloodImage_00005.jpeg	480 x 640	108px	22.5%	99px	15.47%	3.48%

License #

BCCD: Blood Cell Count and Detection is under MIT license.

Source

Citation #

If you make use of the BCCD data, please cite the following reference:

@dataset{BCCD,
	author={shenggan and Nicolas Chen and cosmicad and akshaylamba},
	title={BCCD: Blood Cell Count and Detection},
	year={2018},
	url={https://github.com/Shenggan/BCCD_Dataset}
}

Source

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

@misc{ visualization-tools-for-bccd-dataset,
  title = { Visualization Tools for BCCD Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/bccd } },
  url = { https://datasetninja.com/bccd },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Dataset BCCD can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='BCCD', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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Disclaimer #

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