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-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.
Highlights
Analysis of the heterogeneity of cells is critical to discover the complexity and factuality of life system
Motivated by the good performance of U-Nets in cell segmentation (Van Valen et al, 2016; Hollandi et al, 2020; Salem et al, 2020), we developed Dice-XMBD, a deep neural network (DNN)-based cell segmentation method for multichannel Imaging mass cytometry (IMC) images
We used four IMC datasets of different channel configurations to evaluate the performance of Dice-XMBD and the results show that it can generate highly accurate cell segmentation results that are comparable to those from manual annotation for IMC images from both the same and different datasets to the training dataset, validating its applicability for generic IMC image segmentation tasks
Summary
Analysis of the heterogeneity of cells is critical to discover the complexity and factuality of life system. The development of multiplex IHC/IF (mIHC/mIF) technologies has enabled the simultaneous detection of multiple biomarkers and preserves spatial information, such as cyclic IHC/IF and metal-based multiplex imaging technologies (Zrazhevskiy and Gao, 2013; Angelo et al, 2014; Giesen et al, 2014; Tan et al, 2020). Imaging mass cytometry (IMC) (Giesen et al, 2014; Chang et al, 2017), one of. Dice-XMBD: IMC Cell Segmentation metal-based mIHC technologies, uses a high-resolution laser with a mass cytometer and makes the measurement of 100 markers possible. Due to its high resolution and large number of concurrent marker channels available, IMC has been proven to be highly effective in identifying the complex cell phenotypes and interactions coupled with spatial locations. Apart from using IMC techniques alone, several other technologies, such as RNA detection in situ and 3D imaging, have been combined with IMC to expand its applicability and utility (Schulz et al, 2018; Bouzekri et al, 2019; Catena et al, 2020; Flint et al, 2020)
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