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BCCD Dataset

36431918
Tagmedical
Taskobject detection
Release YearMade in 2018
LicenseMIT
Download12 MB

Summary #

Dataset LinkHomepage

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.

Dataset Poster

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.

OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
OpenSample annotation mask from BCCDSample image from BCCD
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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.

Search
Rows 1-3 of 3
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
WBCβž”
rectangle
358
372
1.04
12.08%
RBCβž”
rectangle
349
4155
11.91
38.3%
Plateletsβž”
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 4888
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
RBC
rectangle
BloodImage_00005.jpeg
480 x 640
108px
22.5%
114px
17.81%
4.01%
2βž”
RBC
rectangle
BloodImage_00005.jpeg
480 x 640
108px
22.5%
114px
17.81%
4.01%
3βž”
RBC
rectangle
BloodImage_00005.jpeg
480 x 640
129px
26.88%
105px
16.41%
4.41%
4βž”
RBC
rectangle
BloodImage_00005.jpeg
480 x 640
129px
26.88%
104px
16.25%
4.37%
5βž”
RBC
rectangle
BloodImage_00005.jpeg
480 x 640
129px
26.88%
105px
16.41%
4.41%
6βž”
RBC
rectangle
BloodImage_00005.jpeg
480 x 640
129px
26.88%
105px
16.41%
4.41%
7βž”
RBC
rectangle
BloodImage_00005.jpeg
480 x 640
128px
26.67%
105px
16.41%
4.38%
8βž”
RBC
rectangle
BloodImage_00005.jpeg
480 x 640
108px
22.5%
100px
15.62%
3.52%
9βž”
RBC
rectangle
BloodImage_00005.jpeg
480 x 640
108px
22.5%
100px
15.62%
3.52%
10βž”
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 = { feb },
  note = { visited on 2024-02-24 },
}

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.

. . .

Disclaimer #

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