Maturation of heterogeneity in afferent synapse ultrastructure in the mouse cochlea

Auditory nerve fibers (ANFs) innervating the same inner hair cell (IHC) may have identical frequency tuning but different sound response properties. In cat and guinea pig, ANF response properties correlate with afferent synapse morphology and position on the IHC, suggesting a causal structure-function relationship. In mice, this relationship has not been fully characterized. Here we measured the emergence of synaptic morphological heterogeneities during maturation of the C57BL/6J mouse cochlea by comparing postnatal day 17 (p17, ∼3 days after hearing onset) with p34, when the mouse cochlea is mature. Using serial block face scanning electron microscopy and three-dimensional reconstruction we measured the size, shape, vesicle content, and position of 70 ribbon synapses from the mid-cochlea. Several features matured over late postnatal development. From p17 to p34, presynaptic densities (PDs) and post-synaptic densities (PSDs) became smaller on average (PDs: 0.75 to 0.33; PSDs: 0.58 to 0.31 μ

Semi-automated quantification of retinal IS/OS damage in en-face OCT image

A variety of vision ailments are indicated by structural changes in the retinal substructures of the posterior segment of the eye. In particular, integrity of the inner-segment/outer-segment (IS/OS) junction directly relates to the visual acuity. In the en-face optical coherence tomography (OCT) image, IS/OS damage manifests as a dark spot in the foveal region, and its quantification, usually performed by experts, assumes diagnostic significance. In this context, in view of the general scarcity of experts, it becomes imperative to develop algorithmic methods to reduce expert time and effort. Accordingly, we propose a semi-automated method based on level sets. As the energy function, we adopt mutual information which exploits the difference in statistical properties of the lesion and its surroundings. On a dataset of 27 en-face OCT images, segmentation obtained by the proposed algorithm exhibits close visual agreement with that obtained manually. Importantly, our results also match manual results in various statistical criteria. In particular, we achieve a mean Dice coefficient of 85.69%, desirably close to the corresponding observer repeatability index of 89.45%. Finally, we quantify algorithmic accuracy in terms of two quotient measures, defined relative to observer repeatability, which could be used as bases for comparison with future algorithms, even if the latter are tested on disparate datasets