CochleaNet: deep learning-based image analysis for cochlear connectomics and gene therapy

With the emergence of gene and optogenetic therapies targeting deafness, the comprehensive analysis of the molecular anatomy and physiology of the cochlea has become ever more important. Here, we introduce CochleaNet, a deep learning-based framework to analyze volumetric imaging data obtained by light-sheet microscopy of decalcified, cleared and fluorescently labeled cochleae. CochleaNet covers the workflow from reconstruction of the cochlea to segmentation of inner hair cells, spiral ganglion neurons and their afferent synapses, to the analysis of tonotopy and specific fluorescent expression patterns. We validated CochleaNet by comparison to manual image analysis, also demonstrating its superiority to prior segmentation methods. We then analyzed different spiral ganglion neuron types and validated different gene therapy products with CochleaNet. Trained on high isotropic resolution mouse data, CochleaNet was also applicable to the cochlea of the gerbil, another relevant animal model, and lower-resolution mouse and marmoset data from a commercially available microscope. We conclude that the combination of light-sheet microscopy and image analysis with CochleaNet paves the way for rapid and reliable quantification of cochlear molecular anatomy and preclinical gene therapy outcomes.