Deep Volumetric Segmentation of Murine Cochlear Compartments from Micro-Computed Tomography Images

Local drug delivery to the inner ear via micropump implants has the potential to be much more effective than oral drug delivery for treating patients with sensorineural hearing loss and to protect hearing from ototoxic insult due to noise exposure or cancer treatments. Designing micropumps to deliver appropriate concentrations of drugs to the necessary cochlear compartments is of paramount importance; however, directly measuring local drug concentrations over time throughout the cochlea is not possible. Recent approaches for indirectly quantifying local drug concentrations in animal models capture a series of magnetic resonance (MR) or micro computed tomography (µCT) images before and after infusion of a contrast agent into the cochlea. These approaches require accurately segmenting important cochlear components (scala tympani (ST), scala media (SM) and scala vestibuli (SV)) in each scan and ensuring that they are registered longitudinally across scans. In this paper, we focus on segmenting cochlear compartments from µCT volumes using V-Net, a convolutional neural network (CNN) architecture for 3-D segmentation. We show that by modifying the V-Net architecture to decrease the numbers of encoder and decoder blocks and to use dilated convolutions enables extracting local estimates of drug concentration that are comparable to those extracted using atlas-based segmentation (3.37%, 4.81%, and 19.65% average relative error in ST, SM, and SV), but in a fraction of the time. We also test the feasibility of training our network on a larger MRI dataset, and then using transfer learning to perform segmentation on a smaller number of µCT volumes, which would enable this technique to be used in the future to characterize drug delivery in the cochlea of larger mammals.