Automated Microscopy Image Segmentation and Analysis with Machine Learning

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.

Keywords: Deep learning; Machine learning; Microscopy; Quantitative analysis; Segmentation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Image Processing, Computer-Assisted / methods
  • Machine Learning*
  • Microscopy*