ACCT is a fast and accessible automatic cell counting tool using machine learning for 2D image segmentation.

Autor: Kataras TJ; Graduate Program of Genetics, Genomics and Bioinformatics, University of California, Riverside, Riverside, CA, 92521, USA.; School of Medicine, Division of Biomedical Sciences, University of California, Riverside, Riverside, CA, 92521, USA., Jang TJ; Graduate Program of Genetics, Genomics and Bioinformatics, University of California, Riverside, Riverside, CA, 92521, USA.; School of Medicine, Division of Biomedical Sciences, University of California, Riverside, Riverside, CA, 92521, USA., Koury J; School of Medicine, Division of Biomedical Sciences, University of California, Riverside, Riverside, CA, 92521, USA., Singh H; School of Medicine, Division of Biomedical Sciences, University of California, Riverside, Riverside, CA, 92521, USA., Fok D; School of Medicine, Division of Biomedical Sciences, University of California, Riverside, Riverside, CA, 92521, USA., Kaul M; Graduate Program of Genetics, Genomics and Bioinformatics, University of California, Riverside, Riverside, CA, 92521, USA. Marcus.Kaul@medsch.ucr.edu.; School of Medicine, Division of Biomedical Sciences, University of California, Riverside, Riverside, CA, 92521, USA. Marcus.Kaul@medsch.ucr.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 May 22; Vol. 13 (1), pp. 8213. Date of Electronic Publication: 2023 May 22.
DOI: 10.1038/s41598-023-34943-w
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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