A community-based approach to image analysis of cells, tissues and tumors.
A community-based approach to image analysis of cells, tissues and tumors.
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基于社区的细胞，组织和肿瘤分析的方法。
DOI：
10.1016/j.compmedimag.2021.102013
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发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Sokolov A
中科院分区：
文献类型：
--
作者：
CSBC/PS-ON Image Analysis Working Group;Vizcarra JC;Burlingame EA;Hug CB;Goltsev Y;White BS;Tyson DR;Sokolov A
新兴的多重成像平台为越来越多的亚细胞分辨率分子标记和肿瘤细胞组成的动态演化提供了前所未有的视角。因此，它们能够阐明肿瘤微环境中影响临床结果和治疗反应的细胞间相互作用。然而，这些平台的快速发展远远超过了处理和分析它们生成的数据的计算方法。虽然技术上不同，但所有成像分析对于收集后数据处理都有许多计算要求。因此，我们的图像分析工作组 (IAWG) 由癌症系统生物学联盟 (CSBC) 和物理科学 - 肿瘤学网络 (PS-ON) 的研究人员组成，召开了一次关于“不同成像平台共享的计算挑战”的研讨会，以描述这些常见问题，并举办了后续黑客马拉松，为其中选定的子集实施解决方案。在这里，我们描绘了反映该领域研究主要轴的这些领域，包括图像配准、细胞和亚细胞结构的分割以及从形态学识别细胞类型。我们进一步描述了这些活动的后勤组织，相信我们学到的经验教训可以帮助其他人围绕共同感兴趣的自我确定的主题团结成像社区，设计和实施操作程序来解决这些主题，并减轻图像分析中固有的问题（例如，共享大型数据集的示例图像并通过开源代码存储库传播黑客马拉松挑战的基线解决方案）。
Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. As such, our Image Analysis Working Group (IAWG), composed of researchers in the Cancer Systems Biology Consortium (CSBC) and Physical Sciences - Oncology Network (PS-ON), convened a workshop on “Computational Challenges Shared by Diverse Imaging Platforms” to characterize these common issues and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).
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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：
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：
--
作者：
Ahmed, Azza E;Mpangase, Phelelani T;Mulder, Nicola
通讯作者：
Mulder, Nicola
影响因子：
48
作者：
Giesen, Charlotte;Wang, Hao A. O.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd
影响因子：
82.9
作者：
通讯作者：
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