Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-Free Microscopy Imaging. Maddalena, L., Antonelli, L., Albu, A., Hada, A., & Guarracino, M. R. Algorithms, 15(9):313, September, 2022. Number: 9 Publisher: Multidisciplinary Digital Publishing Institute![Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-Free Microscopy Imaging [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{maddalena_artificial_2022,
title = {Artificial {Intelligence} for {Cell} {Segmentation}, {Event} {Detection}, and {Tracking} for {Label}-{Free} {Microscopy} {Imaging}},
volume = {15},
copyright = {http://creativecommons.org/licenses/by/3.0/},
issn = {1999-4893},
url = {https://www.mdpi.com/1999-4893/15/9/313},
doi = {10.3390/a15090313},
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.},
language = {en},
number = {9},
urldate = {2022-10-04},
journal = {Algorithms},
author = {Maddalena, Lucia and Antonelli, Laura and Albu, Alexandra and Hada, Aroj and Guarracino, Mario Rosario},
month = sep,
year = {2022},
note = {Number: 9
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {Pub1, artificial intelligence, cell classification, cell event detection, cell segmentation, cell tracking, deep learning, label-free microscopy, machine learning},
pages = {313},
}
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