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

• Main Authors: Xu Xiao, Ying Qiao, Yudi Jiao, Na Fu, Wenxian Yang, Liansheng Wang, Rongshan Yu, Jiahuai Han
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-09-01
• Series: Frontiers in Genetics
• Subjects: imaging mass cytometry; multiplexed imaging; single cell segmentation; U-net; knowledge distillation; digital pathology
• Online Access: https://www.frontiersin.org/articles/10.3389/fgene.2021.721229/full

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-XMBD1, 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.