Abstract
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.
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Notes
Scaffold-A549 is available at: https://github.com/Kaiseem/Scaffold-A549.
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Acknowledgements
The work was partially supported by the following: National Natural Science Foundation of China under no.61876155; Jiangsu Science and Technology Programme (Natural Science Foundation of Jiangsu Province) under no. BK20181189, BE2020006-4; Key Program Special Fund in XJTLU under no. KS-A-09, KSF-A-10, KSF-T-06, KSF-E-26, KSF-E-37.
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Yao, K., Huang, K., Sun, J. et al. Scaffold-A549: A Benchmark 3D Fluorescence Image Dataset for Unsupervised Nuclei Segmentation. Cogn Comput 13, 1603–1608 (2021). https://doi.org/10.1007/s12559-021-09944-4
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DOI: https://doi.org/10.1007/s12559-021-09944-4