New Morphological Features Based on the Sholl Analysis for Automatic Classification of Traced Neurons

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dc.contributor.authorLópez Cabrera, José Daniel
dc.contributor.authorHernández Pérez, Leonardo Agustín
dc.contributor.authorOrozco Morales, Rubén
dc.contributor.authorLorenzo Ginori, Juan Valentín
dc.contributor.departmentUniversidad Central "Marta Abreu" de Las Villas. Dpto de Automática.en_US
dc.contributor.departmentUniversidad Central "Marta Abreu" de Las Villas. Centro de Investigaciones de la Informática.en_US
dc.contributor.otherEmpresa de Telecomunicaciones de Cuba S.A,en_US
dc.date.accessioned2022-02-17T16:28:20Z
dc.date.available2022-02-17T16:28:20Z
dc.date.issued2020
dc.description.abstractBackground: This article addresses the automatic classification of reconstructed neurons through their morphological features. The purpose was to extend the capabilities of the L-Measure software. Methods: New morphological features were developed, based on modifications of the conventional Sholl analysis. The lengths of the compartments, as well as their volumes, were added to the features used in the classical analysis in order to improve the results during automatic neuron classification. FSM were used to obtain subsets of lower cardinality from the full feature sets and the usefulness of these subsets was tested through their use in supervised classification tasks. The study was based on two types of neurons belonging to mice: pyramidal and GABAergic interneurons. Furthermore, a set of pyramidal neurons belonging to Later 4 and Layer 5 was analyzed. Results: RF classifier shown the best performance combined with a Wrapper method.U-WNAD set allowed to obtain higher values than WN, A and D in all cases and better results than LM for the filters and wrappers FSM. U-LM-WNAD set, led to the highest AUC values for all the FSM studied. Similar results for different regions of cortex were obtained. Comparison with Existing Methods The new features exhibited high discriminatory power with which the values of AUC and Acc obtained in the experiments exceeded those obtained using only the features provided by L-Measure. Conclusions: The highest values of AUC and Acc were obtained from the sets U-WNAD and U-LM-WNAD, evidencing the discriminatory power of the new proposed features.en_US
dc.identifier.doi10.1016/j.jneumeth.2020.108835en_US
dc.identifier.urihttps://dspace.uclv.edu.cu/handle/123456789/13440
dc.language.isoen_USen_US
dc.relation.journalJournal of Neuroscience Methodsen_US
dc.source.endpage8en_US
dc.source.initialpage1en_US
dc.source.issue108835en_US
dc.source.volume343en_US
dc.subjectMorphological featuresen_US
dc.subjectTraced neuronsen_US
dc.subjectSholl analysisen_US
dc.titleNew Morphological Features Based on the Sholl Analysis for Automatic Classification of Traced Neuronsen_US
dc.typeArticleen_US
dc.type.article1en_US

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Artículos - Centro de Estudios de Mecánica Computacional Aplicada a Ingeniería