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Peer-reviewedCC BY-NC-NDData in brief

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

  • Zabina Tasneem1
  • Joy Zhao2
  • Jinwei Fang1
  • Aishwarya Shrestha1
  • Qingsu Cheng1
  1. 1Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.
  2. 2Whitefish Bay High School, Whitefish Bay, WI 53217, USA.
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Abstract

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 >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.

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Cite

BibTeXRIS
BibTeX
@article{tasneem2026breast,
  title = {{Breast mammary gland dataset (BMGD): DAPI-stained fluorescent images for nuclei segmentation.}},
  author = {Zabina Tasneem and Joy Zhao and Jinwei Fang and Aishwarya Shrestha and Qingsu Cheng},
  journal = {Data in brief},
  year = {2026},
  month = aug,
  doi = {10.1016/j.dib.2026.113297},
  url = {https://europepmc.org/article/MED/42898449},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Breast mammary gland dataset (BMGD): DAPI-stained fluorescent images for nuclei segmentation.
AU  - Zabina Tasneem
AU  - Joy Zhao
AU  - Jinwei Fang
AU  - Aishwarya Shrestha
AU  - Qingsu Cheng
PY  - 2026
DA  - 2026/08/01
JO  - Data in brief
DO  - 10.1016/j.dib.2026.113297
UR  - https://europepmc.org/article/MED/42898449
AB  - 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 >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.
SN  - 2352-3409
ER  -