CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images.
CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images.
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CellSeg：一个强大的、预先训练的细胞核分割和像素量化软件，用于高度复用的荧光图像。
DOI：
10.1186/s12859-022-04570-9
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发表时间：
2022-01-18
影响因子：
3
通讯作者：
Schürch CM
中科院分区：
文献类型：
--
作者：
Lee MY;Bedia JS;Bhate SS;Barlow GL;Phillips D;Fantl WJ;Nolan GP;Schürch CM
细胞图像分割是定量分析高分辨率组织图像的重要步骤。目前的分割管道通常需要手动数据集注释和额外的培训，重要的参数调整，或编程的复杂理解，以适应研究人员的需要。在这里，我们介绍了CellSeg，这是一种基于Mask区域卷积神经网络（R-CNN）架构的开源，预训练的细胞核分割和信号量化软件。具有广泛编程技能的用户可以访问CellSeg。 CellSeg在2018年Kaggle数据挑战赛中定性和定量地表现出顶级分割算法的水平，并且与已建立的最先进的分割算法相比，可以很好地推广到多种多样的多重成像癌症组织。CellSeg流水线中的自动分割后处理步骤提高了下游单细胞分析的免疫细胞群体的分辨率。最后，将CellSeg应用于在CO-Detection by indEXing（CODEX）平台上获得的高度多重的结直肠癌数据集，表明CellSeg可以集成到多重组织成像管道中，并导致准确识别经验证的细胞群。CellSeg是一个强大的细胞分割软件，用于分析高度复用的组织图像，任何编程技能水平的生物学研究人员都可以使用。
Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the software to the researcher’s need. Here, we present CellSeg, an open-source, pre-trained nucleus segmentation and signal quantification software based on the Mask region-convolutional neural network (R-CNN) architecture. CellSeg is accessible to users with a wide range of programming skills. CellSeg performs at the level of top segmentation algorithms in the 2018 Kaggle Data Challenge both qualitatively and quantitatively and generalizes well to a diverse set of multiplexed imaged cancer tissues compared to established state-of-the-art segmentation algorithms. Automated segmentation post-processing steps in the CellSeg pipeline improve the resolution of immune cell populations for downstream single-cell analysis. Finally, an application of CellSeg to a highly multiplexed colorectal cancer dataset acquired on the CO-Detection by indEXing (CODEX) platform demonstrates that CellSeg can be integrated into a multiplexed tissue imaging pipeline and lead to accurate identification of validated cell populations. CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level.
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影响因子：
46.9
作者：
Greenwald, Noah F.;Miller, Geneva;Moen, Erick;Kong, Alex;Kagel, Adam;Dougherty, Thomas;Fullaway, Christine Camacho;McIntosh, Brianna J.;Leow, Ke Xuan;Schwartz, Morgan Sarah;Pavelchek, Cole;Cui, Sunny;Camplisson, Isabella;Bar-Tal, Omer;Singh, Jaiveer;Fong, Mara;Chaudhry, Gautam;Abraham, Zion;Moseley, Jackson;Warshawsky, Shiri;Soon, Erin;Greenbaum, Shirley;Risom, Tyler;Hollmann, Travis;Bendall, Sean C.;Keren, Leeat;Graf, William;Angelo, Michael;Van Valen, David
通讯作者：
Van Valen, David
影响因子：
9.8
作者：
McQuin C;Goodman A;Chernyshev V;Kamentsky L;Cimini BA;Karhohs KW;Doan M;Ding L;Rafelski SM;Thirstrup D;Wiegraebe W;Singh S;Becker T;Caicedo JC;Carpenter AE
通讯作者：
Carpenter AE
影响因子：
64.5
作者：
Goltsev Y;Samusik N;Kennedy-Darling J;Bhate S;Hale M;Vazquez G;Black S;Nolan GP
通讯作者：
Nolan GP
影响因子：
22.7
作者：
Ali, H. Raza;Jackson, Hartland W.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd
影响因子：
7.3
作者：
Phillips D;Schürch CM;Khodadoust MS;Kim YH;Nolan GP;Jiang S
通讯作者：
Jiang S