Welcome to the GitHub page of DeepTrackAI's Cell Counting dataset. The repository contains the image set BBBC039v1 Caicedo et al., 2018, available from the Broad Bioimage Benchmark Collection and described in Ljosa et al., Nature Methods, 2012.
The data set has a total of 200 fields of view of nuclei captured with fluorescence microscopy using the Hoechst stain. The collection has around 23,000 single nuclei manually annotated to establish a ground truth collection for segmentation evaluation.
This dataset has been used for evaluating the performance of Deep Learning models. More details of the evaluation framework can be found in this bioRxiv preprint by Caicedo et al., 2018.
The Cell Counting dataset contains 200 images. Each image is a 16-bit grayscale picture in TIFF, and the associated label is the manually annotated segmentation map in PNG.
- Dataset Size: 200 images
- Image Size: 520x696 pixels
- Color: Grayscale
- Labels: Segmentation map
To use the Cell Counting dataset in your project:
- Clone this repository to your local machine.
- Import the dataset into your machine learning framework of choice.
- Train or evaluate your models using the dataset.
To clone the repository and access the Cell Counting dataset:
git clone -b cell_counting_dataset github.com/DeepTrackAI/cell_counting_dataset
cd cell_counting_datasetCopyright: CC0. To the extent possible under law, the various contributors of the image sets have waived all copyright and related or neighboring rights to BBBC039v1.
If you find any issues with the dataset or have suggestions for improvements, please open an issue or submit a pull request.