Winfree Seth
Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, United States.
Front Physiol. 2022 Mar 10;13:833333. doi: 10.3389/fphys.2022.833333. eCollection 2022.
Advanced image analysis with machine and deep learning has improved cell segmentation and classification for novel insights into biological mechanisms. These approaches have been used for the analysis of cells , within tissue, and confirmed existing and uncovered new models of cellular microenvironments in human disease. This has been achieved by the development of both imaging modality specific and multimodal solutions for cellular segmentation, thus addressing the fundamental requirement for high quality and reproducible cell segmentation in images from immunofluorescence, immunohistochemistry and histological stains. The expansive landscape of cell types-from a variety of species, organs and cellular states-has required a concerted effort to build libraries of annotated cells for training data and novel solutions for leveraging annotations across imaging modalities and in some cases led to questioning the requirement for single cell demarcation all together. Unfortunately, bleeding-edge approaches are often confined to a few experts with the necessary domain knowledge. However, freely available, and open-source tools and libraries of trained machine learning models have been made accessible to researchers in the biomedical sciences as software pipelines, plugins for open-source and free desktop and web-based software solutions. The future holds exciting possibilities with expanding machine learning models for segmentation via the brute-force addition of new training data or the implementation of novel network architectures, the use of machine and deep learning in cell and neighborhood classification for uncovering cellular microenvironments, and the development of new strategies for the use of machine and deep learning in biomedical research.
借助机器学习和深度学习的先进图像分析技术，已改善了细胞分割和分类，从而为深入了解生物学机制带来了新见解。这些方法已用于组织内细胞的分析，并证实了人类疾病中细胞微环境的现有模型并发现了新模型。这是通过开发针对细胞分割的特定成像模态和多模态解决方案来实现的，从而满足了对免疫荧光、免疫组织化学和组织学染色图像中高质量且可重复的细胞分割的基本要求。细胞类型的广泛领域——来自各种物种、器官和细胞状态——需要共同努力来构建带注释细胞库作为训练数据，并开发跨成像模态利用注释的新解决方案，在某些情况下甚至引发了对单细胞划分必要性的质疑。不幸的是，前沿方法往往局限于少数具备必要领域知识的专家。不过，生物医学领域的研究人员可以通过软件管道、开源和免费桌面及基于网络的软件解决方案的插件，使用免费且开源的经过训练的机器学习模型工具和库。随着通过强力添加新训练数据或实施新型网络架构来扩展用于分割的机器学习模型、在细胞和邻域分类中使用机器学习和深度学习来揭示细胞微环境，以及开发在生物医学研究中使用机器学习和深度学习的新策略，未来充满了令人兴奋的可能性。
