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A deep learning approach to quantify auditory hair cells

descriptionPublicationkeyboard_double_arrow_right Article 01 Sep 2021 Switzerland Publisher:Elsevier BVJournal:Hearing Research, volume 409, page 108,317 (issn: 0378-5955,

Authors: Maurizio Cortada; Loïc Sauteur; Michael Lanz; Soledad Levano; Daniel Bodmer;

doi: 10.1016/j.heares.2021.108317 , 10.5281/zenodo.4590066 , 10.5281/zenodo.4590065

pmid: 34343849

A deep learning approach to quantify auditory hair cells

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Abstract

StarDist 2D deep learning model and training dataset.

Country

Switzerland

Related Organizations

University of Basel
Switzerland
University Hospital of Basel
Switzerland

Keywords

inner ear, Hearing Loss, Sensorineural, cochlea, Neural Network, Reproducibility of Results, deep learning, hair cell, HC counting, Mice, Deep Learning, HC survival, Segmentation, StarDist, Hair Cells, Auditory, Animals, Gentamicins, Organ of Corti, HC quantification

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	15
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%