The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification.
Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into outer hair cells (OHCs) and inner hair cells (IHCs). Light microscopy tools allow for imaging of auditory hair cells along the full length of the cochlea, often yielding more data...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-03-01
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| Series: | PLoS Biology |
| Online Access: | https://doi.org/10.1371/journal.pbio.3002041 |
| _version_ | 1797848207079768064 |
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| author | Christopher J Buswinka Richard T Osgood Rubina G Simikyan David B Rosenberg Artur A Indzhykulian |
| author_facet | Christopher J Buswinka Richard T Osgood Rubina G Simikyan David B Rosenberg Artur A Indzhykulian |
| author_sort | Christopher J Buswinka |
| collection | DOAJ |
| description | Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into outer hair cells (OHCs) and inner hair cells (IHCs). Light microscopy tools allow for imaging of auditory hair cells along the full length of the cochlea, often yielding more data than feasible to manually analyze. Currently, there are no widely applicable tools for fast, unsupervised, unbiased, and comprehensive image analysis of auditory hair cells that work well either with imaging datasets containing an entire cochlea or smaller sampled regions. Here, we present a highly accurate machine learning-based hair cell analysis toolbox (HCAT) for the comprehensive analysis of whole cochleae (or smaller regions of interest) across light microscopy imaging modalities and species. The HCAT is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs versus OHCs), determining their best frequency based on their location along the cochlea, and generating cochleograms. These automated tools remove a considerable barrier in cochlear image analysis, allowing for faster, unbiased, and more comprehensive data analysis practices. Furthermore, HCAT can serve as a template for deep learning-based detection tasks in other types of biological tissue: With some training data, HCAT's core codebase can be trained to develop a custom deep learning detection model for any object on an image. |
| first_indexed | 2024-04-09T18:24:52Z |
| format | Article |
| id | doaj.art-d96be83ac301437c8eef1e41f4d45c0c |
| institution | Directory Open Access Journal |
| issn | 1544-9173 1545-7885 |
| language | English |
| last_indexed | 2024-04-09T18:24:52Z |
| publishDate | 2023-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Biology |
| spelling | doaj.art-d96be83ac301437c8eef1e41f4d45c0c2023-04-12T05:30:40ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852023-03-01213e300204110.1371/journal.pbio.3002041The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification.Christopher J BuswinkaRichard T OsgoodRubina G SimikyanDavid B RosenbergArtur A IndzhykulianOur sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into outer hair cells (OHCs) and inner hair cells (IHCs). Light microscopy tools allow for imaging of auditory hair cells along the full length of the cochlea, often yielding more data than feasible to manually analyze. Currently, there are no widely applicable tools for fast, unsupervised, unbiased, and comprehensive image analysis of auditory hair cells that work well either with imaging datasets containing an entire cochlea or smaller sampled regions. Here, we present a highly accurate machine learning-based hair cell analysis toolbox (HCAT) for the comprehensive analysis of whole cochleae (or smaller regions of interest) across light microscopy imaging modalities and species. The HCAT is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs versus OHCs), determining their best frequency based on their location along the cochlea, and generating cochleograms. These automated tools remove a considerable barrier in cochlear image analysis, allowing for faster, unbiased, and more comprehensive data analysis practices. Furthermore, HCAT can serve as a template for deep learning-based detection tasks in other types of biological tissue: With some training data, HCAT's core codebase can be trained to develop a custom deep learning detection model for any object on an image.https://doi.org/10.1371/journal.pbio.3002041 |
| spellingShingle | Christopher J Buswinka Richard T Osgood Rubina G Simikyan David B Rosenberg Artur A Indzhykulian The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification. PLoS Biology |
| title | The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification. |
| title_full | The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification. |
| title_fullStr | The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification. |
| title_full_unstemmed | The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification. |
| title_short | The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification. |
| title_sort | hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification |
| url | https://doi.org/10.1371/journal.pbio.3002041 |
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