Show simple item record

dc.contributor.authorDursun, Gizem
dc.contributor.authorBijelić, Dunja
dc.contributor.authorAyşit, Neşe
dc.contributor.authorKurt Vatandaşlar, Burcu
dc.contributor.authorRadenović, Lidija
dc.contributor.authorÇapar, Abdulkerim
dc.contributor.authorKerman, Bilal Ersen
dc.contributor.authorAndjus, Pavle R.
dc.contributor.authorKorenić, Andrej
dc.contributor.authorÖzkaya, Ufuk
dc.date.accessioned2023-02-20T09:03:57Z
dc.date.available2023-02-20T09:03:57Z
dc.date.issued2023en_US
dc.identifier.citationDursun, G., Bijelić, D., Ayşit, N., Kurt Vatandaşlar, B., Radenović, L. ve Çapar, A. (2023). Combined segmentation and classificationbased approach to automated analysis of biomedical signals obtained from calcium imaging. PLoS One, 18(2). https://doi.org/10.1371/journal.pone.0281236en_US
dc.identifier.issn1932-6203
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0281236
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10484
dc.description.abstractAutomated 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.en_US
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBiomedical Signalsen_US
dc.subjectCalcium Imagingen_US
dc.subjectCombined Segmentationen_US
dc.titleCombined segmentation and classificationbased approach to automated analysis of biomedical signals obtained from calcium imagingen_US
dc.typearticleen_US
dc.relation.ispartofPLoS Oneen_US
dc.departmentİstanbul Medipol Üniversitesi, Rektörlük, Rejeneratif ve Restoratif Tıp Araştırmaları Merkezi (REMER)en_US
dc.departmentİstanbul Medipol Üniversitesi, Rektörlük, Sağlık Bilim ve Teknolojileri Araştırma Enstitüsüen_US
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Histoloji ve Embriyoloji Ana Bilim Dalıen_US
dc.authorid0000-0003-1106-3288en_US
dc.identifier.volume18en_US
dc.identifier.issue2en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1371/journal.pone.0281236en_US
dc.institutionauthorAyşit, Neşe
dc.institutionauthorKurt Vatandaşlar, Burcu
dc.institutionauthorKerman, Bilal Ersen
dc.identifier.wosqualityQ2en_US
dc.identifier.wos000966740700001en_US
dc.identifier.scopus2-s2.0-85147536496en_US
dc.identifier.pmid36745648en_US
dc.identifier.scopusqualityQ1en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record