Université Laval, Québec, QC, Canada.
CERVO Brain research center, Québec, QC, Canada.
Methods Mol Biol. 2022;2440:349-365. doi: 10.1007/978-1-0716-2051-9_20.
The development of automated quantitative image analysis pipelines requires thoughtful considerations to extract meaningful information. Commonly, extraction rules for quantitative parameters are defined and agreed beforehand to ensure repeatability between annotators. Machine/Deep Learning (ML/DL) now provides tools to automatically extract the set of rules to obtain quantitative information from the images (e.g. segmentation, enumeration, classification, etc.). Many parameters must be considered in the development of proper ML/DL pipelines. We herein present the important vocabulary, the necessary steps to create a thorough image segmentation pipeline, and also discuss technical aspects that should be considered in the development of automated image analysis pipelines through ML/DL.
自动化定量图像分析管道的开发需要深思熟虑，以提取有意义的信息。通常，定量参数的提取规则是事先定义和商定的，以确保注释者之间的可重复性。机器/深度学习 (ML/DL) 现在提供了工具，可以自动提取规则集，以便从图像中获取定量信息（例如分割、计数、分类等）。在开发适当的 ML/DL 管道时，必须考虑许多参数。本文介绍了重要的词汇、创建全面的图像分割管道的必要步骤，并讨论了通过 ML/DL 开发自动化图像分析管道时应考虑的技术方面。
