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Greenwald, N.F., Miller, G., Moen, E., Kong, A., Kagel, A., Dougherty, T., et al. (2022) Whole-cell Segmentation of Tissue Images with Human-Level Performance Using Large-Scale Data Annotation and Deep Learning. Nature Biotechnology, 40, 555-565.
https://doi.org/10.1038/s41587-021-01094-0

has been cited by the following article:

• TITLE: Integrative Cell Bin Segmentation on Spatial Transcriptomics by Voronoi

  AUTHORS: Ming Lin

  KEYWORDS: Spatial Transcriptomics, Voronoi, Bioinformatics, High Resolution, Cell Segmentation, Clustering, Tissue Integration

  JOURNAL NAME: Advances in Bioscience and Biotechnology, Vol.16 No.10, October 22, 2025

  ABSTRACT: Spatial transcriptomics is undergoing rapid advancements and iterations. It is a beneficial tool to significantly enhance our understanding of tissue organization and relationships between cells. Recent technological advancements have achieved subcellular resolution, providing much denser spot placement for downstream analysis. A key challenge for this following analysis is accurate cell segmentation and the assignment of spots to individual cells. The primary objective of this study was to evaluate the effectiveness of a new cell segmentation approach based on subcellular level spatial transcriptomic data by confirming nuclei positions and using Voronoi diagrams, compared to direct clustering with cellbin data. Our findings demonstrate that the Voronoi method not only outperforms traditional methods in providing clearer boundaries and better separation of cell types, but also excels in preserving the most transcripts, addressing the issue of low capture efficiency. This integrative methodology presents a substantial advancement in spatial transcriptomics, offering improved cell type classification and spatial pattern recognition.