Background: Time-lapse microscopy imaging is a key approach for an increasing number of biological and biomedical studies to observe the dynamic behavior of cells over time which helps quantify important data, such as the number of cells and their sizes, shapes, and dynamic interactions across time. Label-free imaging is an essential strategy for such studies as it ensures that native cell behavior remains uninfluenced by the recording process. Computer vision and machine/deep learning approaches have made significant progress in this area. Methods: In this review, we present an overview of methods, software, data, and evaluation metrics for the automatic analysis of label-free microscopy imaging. We aim to provide the interested reader with a unique source of information, with links for further detailed information. Results: We review the most recent methods for cell segmentation, event detection, and tracking. Moreover, we provide lists of publicly available software and datasets. Finally, we summarize the metrics most frequently adopted for evaluating the methods under exam. Conclusions: We provide hints on open challenges and future research directions.

Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-free Microscopy Imaging

Maddalena L.;Antonelli L.
;
Guarracino M. R.
2022

Abstract

Background: Time-lapse microscopy imaging is a key approach for an increasing number of biological and biomedical studies to observe the dynamic behavior of cells over time which helps quantify important data, such as the number of cells and their sizes, shapes, and dynamic interactions across time. Label-free imaging is an essential strategy for such studies as it ensures that native cell behavior remains uninfluenced by the recording process. Computer vision and machine/deep learning approaches have made significant progress in this area. Methods: In this review, we present an overview of methods, software, data, and evaluation metrics for the automatic analysis of label-free microscopy imaging. We aim to provide the interested reader with a unique source of information, with links for further detailed information. Results: We review the most recent methods for cell segmentation, event detection, and tracking. Moreover, we provide lists of publicly available software and datasets. Finally, we summarize the metrics most frequently adopted for evaluating the methods under exam. Conclusions: We provide hints on open challenges and future research directions.
2022
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
label-free microscopy
cell classification
cell segmentation
cell event detection
cell tracking
artificial intelligence
machine learning deep learning
File in questo prodotto:
File Dimensione Formato  
prod_470321-doc_190722.pdf

accesso aperto

Descrizione: Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-free Microscopy Imaging
Tipologia: Versione Editoriale (PDF)
Licenza: Dominio pubblico
Dimensione 1.04 MB
Formato Adobe PDF
1.04 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/413564
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 14
  • ???jsp.display-item.citation.isi??? 10
social impact