ArticlePLoS biology2023
The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification.
Article in PLoS biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 13 citations in OpenAlex.
- SKOOTS: Skeleton-Oriented Object Segmentation for Mitochondria in High-Resolution Cochlear EM Datasets.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cochlear isolation in neonatal mice forFrontiers in pharmacology · 2026Article
- VASCilia is an open-source, deep learning-based tool for 3D analysis of cochlear hair cell stereocilia bundles.PLoS biology · 2026Article
- Computer models and artificial intelligence increase the fidelity and efficiency of the in vitro models for hearing loss.Biomedical engineering online · 2025Review
- VASCilia (Vision Analysis StereoCilia): A Napari Plugin for Deep Learning-Based 3D Analysis of Cochlear Hair Cell Stereocilia Bundles.bioRxiv : the preprint server for biology · 2025Article
- SKOOTS: Skeleton-oriented object segmentation for mitochondria.bioRxiv : the preprint server for biology · 2025Article
- Spiner, deep learning-based automated detection of spiral ganglion neurons in intact cochleae.iScience · 2025Article
- Large-scale annotated dataset for cochlear hair cell detection and classification.Scientific data · 2024Article
- PCP Auto Count: A Novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting.bioRxiv : the preprint server for biology · 2024Article
- PCP auto count: a novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting.Frontiers in cell and developmental biology · 2024Article
- Large-scale annotated dataset for cochlear hair cell detection and classification.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
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Abstract
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.
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