AUTHOR=Xiao Xu , Qiao Ying , Jiao Yudi , Fu Na , Yang Wenxian , Wang Liansheng , Yu Rongshan , Han Jiahuai 

TITLE=Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.721229

DOI=10.3389/fgene.2021.721229

ISSN=1664-8021

ABSTRACT=Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment 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 40 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 for 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.
%, which combine nuclear proteins and membrane/cytoplasm proteins as two channels of input. 
In comparison with other state-of-the-art cell segmentation methods currently used in IMC, 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.