Automated Microscopy Image Segmentation and Analysis with Machine Learning.
Methods Mol Biol
; 2440: 349-365, 2022.
Article
en En
| MEDLINE
| ID: mdl-35218549
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Automático
/
Microscopía
Idioma:
En
Revista:
Methods Mol Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2022
Tipo del documento:
Article
País de afiliación:
Canadá