Breast Mammary Gland Dataset (BMGD): DAPI-Stained Fluorescent Images for Nuclei Segmentation

Accurate segmentation of nuclei images is essential for analyzing cellular responses to perturbation in both in vitro and in vivo experiments. Although traditional methods, including watershed, thresholding, clustering, morphological operations, and active contour models, have long been used in segmenting nuclei in digital images, these methods are labor-intensive and time-consuming. Therefore, current research has shifted to deep learning techniques for improved nuclei segmentation. However, training deep learning models requires high-quality annotated ground truth datasets, which are often scarce and not available for public use. In this study, we introduce the Breast Mammary Gland Dataset (BMGD), an annotated collection of DAPI-stained nuclei images of mammary organoids. The dataset contains 819 image patches with more than 9,500 manually segmented nuclei cultured in various stiffness conditions. Each original image in the BMGD is paired with one carefully annotated ground truth mask. This dataset will enable researchers to develop and evaluate automated nuclei segmentation algorithms, particularly for studying cellular responses in breast cancer research and treatment.