Scaffold-A549: A Benchmark 3D Fluorescence Image Dataset for Unsupervised Nuclei Segmentation

Kai Yao, Kaizhu Huang*, Jie Sun, Linzhi Jing, Dejian Huang, Curran Jude

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


A general trend of nuclei segmentation is the transition from two-dimensional to three-dimensional nuclei segmentation and from traditional image processing methods to data-driven cognitively inspired methods. Existing nuclei segmentation datasets do not meet this trend: They either do not contain enough samples for training the deep learning model or not contain challenging 3D structure. Thus, large-scale datasets are critically demanded for nuclei segmentation tasks. In this paper, we introduce a new benchmark nuclei segmentation dataset termed as Scaffold-A549 for 3D cell culture on bio-scaffold. The A549 human non-small cell lung cancer cells are seeded in the bio-scaffold for cell culture and the samples with different density of nuclei are captured using confocal laser scanning microscope at the first, third, and eighth culture day. A total of 21 3D images are collected containing more than 10,000 nucleus and each of the images containing more than 800 nucleus are annotated manually for evaluation. Scaffold-A549 presents one large, diverse, challenging, and publicly available dataset and can be widely used for the research on 3D unsupervised nuclei segmentation.

Original languageEnglish
Pages (from-to)1603-1608
Number of pages6
JournalCognitive Computation
Issue number6
Publication statusPublished - Nov 2021


  • Dataset
  • Deep Learning
  • Fluorescence Image
  • Nuclei Segmentation


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