Work

Breast mammary gland dataset (BMGD): DAPI-stained fluorescent images for nuclei segmentation

Year: 2026

Type: data-paper

Abstract: 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 >9500 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.

Source: Data in Brief

DOI: 10.1016/j.dib.2026.113297

Authors: Zabina Tasneem, Joy Zhao, Jinwei Fang, Aishwarya Shrestha, Qingsu Cheng

Institution: University of Wisconsin–Milwaukee

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $1,220