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定义边界:在显微镜图像中识别细胞的挑战和进步

Nodar Gogoberidze¹, Beth A Cimini¹

¹Imaging Platform, Broad Institute, Cambridge, MA 02142, USA.

Current opinion in biotechnology

|December 24, 2023

概括

包括Cellpose等专业模型在内的深度学习方法正在显微镜中推进细胞细分. 创新侧重于准确性,效率和可用性,以提供通用解决方案.

科学领域:

* 生物学和图像分析
* 计算机显微镜
* 生命科学中的人工智能

背景情况:

*细胞细分对于分析显微镜图像至关重要.
* 经典的细分方法正在被深度学习方法所超越.
* 像Cellpose这样的专业工具可以提高准确性和用户友好性.

研究的目的:

* 突出深度学习的进展,用于细胞细分.
*讨论诸如多模式细胞细分挑战等挑战的影响.
* 要强调向通用细胞细分方法的推动力.

主要方法:

* 基于深度学习的细分工具的审查.
*分析由细分挑战驱动的创新.
* 审查文档,共享和评估标准的改进.

主要成果:

* 深度学习模型显示,细胞细分的占主导地位越来越大.
* 像Cellpose这样的专业模型证明了改进的准确性和可用性.
*细分挑战促进了准确性,效率和可用性方面的创新.

结论:

* 深度学习正在显微镜中彻底改变细胞细分.
* 对标准和挑战的持续关注加速了进步.
* 一种通用细胞细分方法的开发正在接近.

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