Xiao Xu, Qiao Ying, Jiao Yudi, Fu Na, Yang Wenxian, Wang Liansheng, Yu Rongshan, Han Jiahuai
Department of Computer Science, School of Informatics, Xiamen University, Xiamen, China.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
Front Genet. 2021 Sep 15;12:721229. doi: 10.3389/fgene.2021.721229. eCollection 2021.
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.
高度多重成像技术是一种强大的工具，有助于在亚细胞分辨率下理解肿瘤微环境中细胞的组成和相互作用，这对于基础研究和临床应用都至关重要。成像质谱流式细胞术（IMC）是最近引入的一种多重成像方法，通过使用带有质谱细胞仪的高分辨率激光，可在一个组织切片中同时测量多达100种标志物。然而，由于其高分辨率和大量通道，如何处理和解释来自IMC的图像数据仍然是其进一步应用的关键挑战。准确可靠的单细胞分割是处理IMC图像数据的首要且关键步骤。不幸的是，现有的分割流程要么产生不准确的细胞分割结果，要么需要手动注释，这非常耗时。在此，我们开发了Dice-XMBD，一种基于深度学习的用于组织多重成像数据的细胞分割算法。与目前用于IMC图像的其他先进细胞分割方法相比，Dice-XMBD能够在由不同核、膜和细胞质标志物生成的IMC图像上高效地生成更准确的单细胞掩码。所有代码和数据集可在https://github.com/xmuyulab/Dice-XMBD获取。
