ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation
Counting cells is a cornerstone of tracking disease progression in neuroscience. A common approach for this process is having trained researchers individually select and count cells within an image, which is not only difficult to standardize but also very time-consuming. While tools exist to automat...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202925/ https://www.ncbi.nlm.nih.gov/pubmed/37217558 http://dx.doi.org/10.1038/s41598-023-34943-w |
_version_ | 1785045523011469312 |
---|---|
author | Kataras, Theodore J. Jang, Tyler J. Koury, Jeffrey Singh, Hina Fok, Dominic Kaul, Marcus |
author_facet | Kataras, Theodore J. Jang, Tyler J. Koury, Jeffrey Singh, Hina Fok, Dominic Kaul, Marcus |
author_sort | Kataras, Theodore J. |
collection | PubMed |
description | Counting cells is a cornerstone of tracking disease progression in neuroscience. A common approach for this process is having trained researchers individually select and count cells within an image, which is not only difficult to standardize but also very time-consuming. While tools exist to automatically count cells in images, the accuracy and accessibility of such tools can be improved. Thus, we introduce a novel tool ACCT: Automatic Cell Counting with Trainable Weka Segmentation which allows for flexible automatic cell counting via object segmentation after user-driven training. ACCT is demonstrated with a comparative analysis of publicly available images of neurons and an in-house dataset of immunofluorescence-stained microglia cells. For comparison, both datasets were manually counted to demonstrate the applicability of ACCT as an accessible means to automatically quantify cells in a precise manner without the need for computing clusters or advanced data preparation. |
format | Online Article Text |
id | pubmed-10202925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102029252023-05-24 ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation Kataras, Theodore J. Jang, Tyler J. Koury, Jeffrey Singh, Hina Fok, Dominic Kaul, Marcus Sci Rep Article Counting cells is a cornerstone of tracking disease progression in neuroscience. A common approach for this process is having trained researchers individually select and count cells within an image, which is not only difficult to standardize but also very time-consuming. While tools exist to automatically count cells in images, the accuracy and accessibility of such tools can be improved. Thus, we introduce a novel tool ACCT: Automatic Cell Counting with Trainable Weka Segmentation which allows for flexible automatic cell counting via object segmentation after user-driven training. ACCT is demonstrated with a comparative analysis of publicly available images of neurons and an in-house dataset of immunofluorescence-stained microglia cells. For comparison, both datasets were manually counted to demonstrate the applicability of ACCT as an accessible means to automatically quantify cells in a precise manner without the need for computing clusters or advanced data preparation. Nature Publishing Group UK 2023-05-22 /pmc/articles/PMC10202925/ /pubmed/37217558 http://dx.doi.org/10.1038/s41598-023-34943-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kataras, Theodore J. Jang, Tyler J. Koury, Jeffrey Singh, Hina Fok, Dominic Kaul, Marcus ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation |
title | ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation |
title_full | ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation |
title_fullStr | ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation |
title_full_unstemmed | ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation |
title_short | ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation |
title_sort | acct is a fast and accessible automatic cell counting tool using machine learning for 2d image segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202925/ https://www.ncbi.nlm.nih.gov/pubmed/37217558 http://dx.doi.org/10.1038/s41598-023-34943-w |
work_keys_str_mv | AT katarastheodorej acctisafastandaccessibleautomaticcellcountingtoolusingmachinelearningfor2dimagesegmentation AT jangtylerj acctisafastandaccessibleautomaticcellcountingtoolusingmachinelearningfor2dimagesegmentation AT kouryjeffrey acctisafastandaccessibleautomaticcellcountingtoolusingmachinelearningfor2dimagesegmentation AT singhhina acctisafastandaccessibleautomaticcellcountingtoolusingmachinelearningfor2dimagesegmentation AT fokdominic acctisafastandaccessibleautomaticcellcountingtoolusingmachinelearningfor2dimagesegmentation AT kaulmarcus acctisafastandaccessibleautomaticcellcountingtoolusingmachinelearningfor2dimagesegmentation |