[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125618-en":3,"doc-seo-125618-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125618,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Classification of Microglial Morphological Phenotypes Using Machine Learning","Microglia act as CNS immune macrophages that continuously survey the brain microenvironment and undergo rapid, morphology-dependent transformations after tissue damage, infection, or homeostatic disturbances. Conventional automated phenotype classification often relies on a limited set of manually chosen quantitative criteria, introducing selection bias. The study presents a convolutional neural network-based microglial classification method using manually selected cells plus classical morphological parameters, focusing on four morphologies and validating the approach in ischemic stroke mouse models.","Edited by:  \nRenato Socodato, Universidade do Porto, Portugal  \nReviewed by:  \nStefano Fumagalli,  \nIstituto di Ricerche Farmacologiche Mario Negri (IRCCS), Italy Stefano Garofalo,  \nSapienza University of Rome, Italy  \n*Correspondence:  \nKarsten Winter [kwinter@rz.uni-leipzig.de](kwinter@rz.uni-leipzig.de)  \n􀂆 These authors have contributed equally to this work and share first authorship  \nSpecialty section:  \nThis article was submitted to Non-Neuronal Cells,  \na section of the journal Frontiers in Cellular Neuroscience  \nReceived: 28 April 2021  \nAccepted: 07 June 2021  \nPublished: 29 June 2021  \nCitation:  \nLeyh J, Paeschke S, Mages B, Michalski D, Nowicki M, Bechmann I and Winter K (2021) Classification of Microglial Morphological Phenotypes Using Machine Learning.  \nFront. Cell. Neurosci. 15:701673.  \ndoi: 10.3389/fncel.2021.701673  \nClassification of Microglial Morphological Phenotypes Using Machine Learning  \nJudith Leyh 1􀂆, Sabine Paeschke 1􀂆, Bianca Mages 1, Dominik Michalski 2, Marcin Nowicki 1, Ingo Bechmann 1 and Karsten Winter 1 *  \n1Institute of Anatomy, University of Leipzig, Leipzig, Germany, 2Department of Neurology, University of Leipzig, Leipzig, Germany  \nMicroglia are the brain’s immunocompetent macrophages with a unique feature that allows surveillance of the surrounding microenvironment and subsequent reactions to tissue damage, infection, or homeostatic perturbations. Thereby, microglia’s striking morphological plasticity is one of their prominent characteristics and the categorization of microglial cell function based on morphology is well established. Frequently, automated classification of microglial morphological phenotypes is performed by using quantitative parameters. As this process is typically limited to a few and especially manually chosen criteria, a relevant selection bias may compromise the resulting classifications. In our study, we describe a novel microglial classification method by morphological evaluation using a convolutional neuronal network on the basis of manually selected cells in addition to classical morphological parameters. We focused on four microglial morphologies, ramified, rod-like, activated and amoeboid microglia within the murine hippocampus and cortex. The developed method for the classification was confirmed in a mouse model of ischemic stroke which is already known to result in microglial activation within affected brain regions. In conclusion, our classification of microglial morphological phenotypes using machine learning can serve as a time-saving and objective method for post-mortem characterization of microglial changes in healthy and disease mouse models, and might also represent a useful tool for human brain autopsy samples.  \nKeywords: microglia, morphology, machine learning, stroke, hippocampus, cortex  \nINTRODUCTION  \nMicroglia serve as the central nervous system (CNS)’s immunocompetent macrophages, which crucially contribute to homeostasis, plasticity, and learning by taking up pathogens, apoptotic cells, synaptic remnants, toxins, and myelin debris (Bradl and Lassmann, 2010; Sofroniewand Vinters, 2010; Goldmann and Prinz, 2013; Parkhurst et al., 2013; Nutma et al., 2020; Traiffort, 2020) . Our current understanding is that these highly specialized brain-resident immune cells constantly monitor the brain’s microenvironment enabling them to detect  \nAbbreviations: CNS, central nervous system; CNN, convolutional neural network; DAPI, 40 ,6-diamidino-2-phenylindole; PFA, paraformaldehyde; PBS, phosphate buffered saline; SRI, Schoenen ramification index; BBB, blood-brain barrier; DAMP, damage-associated molecular patterns; NF-L, neurofilament light; MAP2, microtubule-associated-protein-2; Coll IV, collagen IV; Iba1, ionized calcium-binding adapter molecule 1; ROI, Regions of interest; CLAHE, contrast limited adaptive histogram equalization; MCA, middle cerebral artery.  \nFrontiers [in Cellular Neuroscience | www.frontiersin.org](in Cellular Neuroscience | www.frontiersi","cbCaissusVjbQE7p","https://ap.wps.com/l/cbCaissusVjbQE7p","pdf",6337807,1,17,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Microglial roles and morphology in homeostasis\n## Morphological transformation after injury\n## Transition states and emerging phenotypes","[{\"question\":\"What problem does the study address in automated microglial morphology classification?\",\"answer\":\"Automated classification often uses only a small number of manually chosen quantitative criteria, which can introduce relevant selection bias and compromise classification outcomes.\"},{\"question\":\"How does the proposed method classify microglial morphological phenotypes?\",\"answer\":\"It combines classical morphological parameters with a convolutional neural network trained on manually selected cells, targeting four microglial morphologies.\"},{\"question\":\"Why was the method validated using a mouse model of ischemic stroke?\",\"answer\":\"Ischemic stroke is known to trigger microglial activation in affected brain regions, providing a biological context to confirm that the classification captures disease-relevant morphological changes.\"}]","Classification of Microglial Morphological 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