Combined segmentation and classification-based approach to automated analysis of biomedical signals obtained from calcium imaging.

Autor: Dursun G; Electrical and Electronics Engineering Department, Süleyman Demirel University, Isparta, Turkey., Bijelić D; Center for Laser Microscopy, Faculty of Biology, University of Belgrade, Belgrade, Serbia., Ayşit N; Department of Medical Biology, Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), School of Medicine, Istanbul Medipol University, Istanbul, Turkey., Kurt Vatandaşlar B; Department of Medical Biology, Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), School of Medicine, Istanbul Medipol University, Istanbul, Turkey., Radenović L; Center for Laser Microscopy, Faculty of Biology, University of Belgrade, Belgrade, Serbia., Çapar A; Informatics Institute of İstanbul Technical University, İstanbul, Turkey., Kerman BE; Department of Medical Biology, Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), School of Medicine, Istanbul Medipol University, Istanbul, Turkey.; Department of Histology and Embryology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey.; Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America., Andjus PR; Center for Laser Microscopy, Faculty of Biology, University of Belgrade, Belgrade, Serbia., Korenić A; Center for Laser Microscopy, Faculty of Biology, University of Belgrade, Belgrade, Serbia., Özkaya U; Electrical and Electronics Engineering Department, Süleyman Demirel University, Isparta, Turkey.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Feb 06; Vol. 18 (2), pp. e0281236. Date of Electronic Publication: 2023 Feb 06 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0281236
Abstrakt: Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand-crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Dursun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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