ArticleFrontiers in genetics2021
Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.BMC bioinformatics · 2026Article
- OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.Research square · 2026Article
- From surfing to diving into the tumor microenvironment through multiparametric imaging mass cytometry.Frontiers in immunology · 2025Review
- Robust consensus nuclear and cell segmentation.Frontiers in genetics · 2025Article
- OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2024Article
- LYN kinase programs stromal fibroblasts to facilitate leukemic survival via regulation of c-JUN and THBS1.Nature communications · 2023Article
- Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure.Biological imaging · 2023Article
- Different approaches to Imaging Mass Cytometry data analysis.Bioinformatics advances · 2023Review
- A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations.PeerJ. Computer science · 2023Article
- Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma.Communications medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.
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