Biology subjects

Rosenberg, D. B.

Publications and source records attributed to Rosenberg, D. B..

2 recordsLinked to original sources

bioRxiv2023

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 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.

neuroscience↗

bioRxiv2021

The Hair Cell Analysis Toolbox: A machine learning-based whole cochlea analysis pipeline

Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into two subtypes: outer hair cells (OHCs) which amplify sound-induced mechanical vibration, and inner hair cells (IHCs) which convert vibrations into electrical signals for interpretation by the brain. One row of IHCs and three rows of OHCs are arranged tonotopically; cells at a particular location respond best to a specific frequency which decreases from base to apex of the cochlea. Loss of hair cells at a specific place affects hearing performance at the corresponding tonotopic frequency. To better understand the underlying cause of hearing loss in patients (or experimental animals) a plot of hair cell survival along the cochlear frequency map, known as a cochleogram, can be generated post-mortem, involving manually counting thousands of cells. 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. Current 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. Here, we present a machine learning-based hair cell analysis toolbox for the comprehensive analysis of whole cochleae (or smaller regions of interest). The Hair Cell Analysis Toolbox (HCAT) is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs vs 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, HCATs core codebase can be trained to develop a custom deep learning detection model for any object on an image.

neuroscience↗