Here is the website to release the BCData dataset for our work, BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting (MICCAI 2020).
Please click here to download the BCData dataset for cell detection and counting.
The initial images are in the BCData/images folder, while the corresponding annotations are in the BCData/annotations folder. The dataset is separated into three parts: train, validataion and test. The annotations of the positive and negative tumor cells are in the positive subfolder and negative subfolder, respectively.
Examples:
1) For the image with the path of BCData/images/train/10.png in the train set, the path for its annotations for the positive tumor cells is BCData/annotations/train/positive/10.h5, and the path for its annotations for the negative tumor cells is BCData/annotations/train/negative/10.h5.
2) For the image with the path of BCData/images/validation/10.png in the validation set, the path for its annotations for the positive tumor cells is BCData/annotations/validation/positive/10.h5, and the path for its annotations for the negative tumor cells is BCData/annotations/validation/negative/10.h5.
Example script for loading the annotation (in Python):
import h5py
import numpy as np
gt_path = "Path of the corresponding .h5 file"
gt_file = h5py.File(gt_path)
coordinates = np.asarray(gt_file['coordinates'])