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Large-scale annotated dataset for cochlear hair cell detection and classification
Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands o...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491224/ https://www.ncbi.nlm.nih.gov/pubmed/37693382 http://dx.doi.org/10.1101/2023.08.30.553559 |
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author | Buswinka, Christopher J. Rosenberg, David B. Simikyan, Rubina G. Osgood, Richard T. Fernandez, Katharine Nitta, Hidetomi Hayashi, Yushi Liberman, Leslie W. Nguyen, Emily Yildiz, Erdem Kim, Jinkyung Jarysta, Amandine Renauld, Justine Wesson, Ella Thapa, Punam Bordiga, Pierrick McMurtry, Noah Llamas, Juan Kitcher, Siân R. López-Porras, Ana I. Cui, Runjia Behnammanesh, Ghazaleh Bird, Jonathan E. Ballesteros, Angela Vélez-Ortega, A. Catalina Edge, Albert SB Deans, Michael R. Gnedeva, Ksenia Shrestha, Brikha R. Manor, Uri Zhao, Bo Ricci, Anthony J. Tarchini, Basile Basch, Martin Stepanyan, Ruben S. Landegger, Lukas D. Rutherford, Mark Liberman, M. Charles Walters, Bradley J. Kros, Corné J. Richardson, Guy P. Cunningham, Lisa L. Indzhykulian, Artur A. |
author_facet | Buswinka, Christopher J. Rosenberg, David B. Simikyan, Rubina G. Osgood, Richard T. Fernandez, Katharine Nitta, Hidetomi Hayashi, Yushi Liberman, Leslie W. Nguyen, Emily Yildiz, Erdem Kim, Jinkyung Jarysta, Amandine Renauld, Justine Wesson, Ella Thapa, Punam Bordiga, Pierrick McMurtry, Noah Llamas, Juan Kitcher, Siân R. López-Porras, Ana I. Cui, Runjia Behnammanesh, Ghazaleh Bird, Jonathan E. Ballesteros, Angela Vélez-Ortega, A. Catalina Edge, Albert SB Deans, Michael R. Gnedeva, Ksenia Shrestha, Brikha R. Manor, Uri Zhao, Bo Ricci, Anthony J. Tarchini, Basile Basch, Martin Stepanyan, Ruben S. Landegger, Lukas D. Rutherford, Mark Liberman, M. Charles Walters, Bradley J. Kros, Corné J. Richardson, Guy P. Cunningham, Lisa L. Indzhykulian, Artur A. |
author_sort | Buswinka, Christopher J. |
collection | PubMed |
description | Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90’000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease. |
format | Online Article Text |
id | pubmed-10491224 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-104912242023-09-09 Large-scale annotated dataset for cochlear hair cell detection and classification Buswinka, Christopher J. Rosenberg, David B. Simikyan, Rubina G. Osgood, Richard T. Fernandez, Katharine Nitta, Hidetomi Hayashi, Yushi Liberman, Leslie W. Nguyen, Emily Yildiz, Erdem Kim, Jinkyung Jarysta, Amandine Renauld, Justine Wesson, Ella Thapa, Punam Bordiga, Pierrick McMurtry, Noah Llamas, Juan Kitcher, Siân R. López-Porras, Ana I. Cui, Runjia Behnammanesh, Ghazaleh Bird, Jonathan E. Ballesteros, Angela Vélez-Ortega, A. Catalina Edge, Albert SB Deans, Michael R. Gnedeva, Ksenia Shrestha, Brikha R. Manor, Uri Zhao, Bo Ricci, Anthony J. Tarchini, Basile Basch, Martin Stepanyan, Ruben S. Landegger, Lukas D. Rutherford, Mark Liberman, M. Charles Walters, Bradley J. Kros, Corné J. Richardson, Guy P. Cunningham, Lisa L. Indzhykulian, Artur A. bioRxiv Article Our sense of hearing is mediated by cochlear hair cells, localized within the sensory epithelium called the organ of Corti. There are two types of hair cells in the cochlea, which are organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains a few thousands of hair cells, and their survival is essential for our perception of sound because they are terminally differentiated and do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. However, the sheer number of cells along the cochlea makes manual quantification impractical. Machine learning can be used to overcome this challenge by automating the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, human, pig and guinea pig cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 90’000 hair cells, all of which have been manually identified and annotated as one of two cell types: inner hair cells and outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to supply other groups within the hearing research community with the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease. Cold Spring Harbor Laboratory 2023-09-01 /pmc/articles/PMC10491224/ /pubmed/37693382 http://dx.doi.org/10.1101/2023.08.30.553559 Text en https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Buswinka, Christopher J. Rosenberg, David B. Simikyan, Rubina G. Osgood, Richard T. Fernandez, Katharine Nitta, Hidetomi Hayashi, Yushi Liberman, Leslie W. Nguyen, Emily Yildiz, Erdem Kim, Jinkyung Jarysta, Amandine Renauld, Justine Wesson, Ella Thapa, Punam Bordiga, Pierrick McMurtry, Noah Llamas, Juan Kitcher, Siân R. López-Porras, Ana I. Cui, Runjia Behnammanesh, Ghazaleh Bird, Jonathan E. Ballesteros, Angela Vélez-Ortega, A. Catalina Edge, Albert SB Deans, Michael R. Gnedeva, Ksenia Shrestha, Brikha R. Manor, Uri Zhao, Bo Ricci, Anthony J. Tarchini, Basile Basch, Martin Stepanyan, Ruben S. Landegger, Lukas D. Rutherford, Mark Liberman, M. Charles Walters, Bradley J. Kros, Corné J. Richardson, Guy P. Cunningham, Lisa L. Indzhykulian, Artur A. Large-scale annotated dataset for cochlear hair cell detection and classification |
title | Large-scale annotated dataset for cochlear hair cell detection and classification |
title_full | Large-scale annotated dataset for cochlear hair cell detection and classification |
title_fullStr | Large-scale annotated dataset for cochlear hair cell detection and classification |
title_full_unstemmed | Large-scale annotated dataset for cochlear hair cell detection and classification |
title_short | Large-scale annotated dataset for cochlear hair cell detection and classification |
title_sort | large-scale annotated dataset for cochlear hair cell detection and classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491224/ https://www.ncbi.nlm.nih.gov/pubmed/37693382 http://dx.doi.org/10.1101/2023.08.30.553559 |
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