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Cochlear implants (CIs) are neural prosthetics which are used to treatpatients with hearing loss. CIs use an array of electrodes which are surgicallyinserted into the cochlea to stimulate the auditory nerve endings. Aftersurgery, CIs need to be programmed. Studies have shown that the spatialrelationship between the intra-cochlear anatomy and electrodes derived frommedical images can guide CI programming and lead to significant improvement inhearing outcomes. However, clinical head CT images are usually obtained fromscanners of different brands with different protocols. The field of view thusvaries greatly and visual inspection is needed to document their content priorto applying algorithms for electrode localization and intra-cochlear anatomysegmentation. In this work, to determine the presence/absence of inner ears andto accurately localize them in head CTs, we use a volume-to-volumeconvolutional neural network which can be trained end-to-end to map a raw CTvolume to probability maps which indicate inner ear positions. We incorporate afalse positive suppression strategy in training and apply a shape-basedconstraint. We achieve a labeling accuracy of 98.59% and a localization errorof 2.45mm. The localization error is significantly smaller than a randomforest-based approach that has been proposed recently to perform the same task.

Accurate Detection of Inner Ears in Head CTs Using a Deep  Volume-to-Volume Regression Network with False Positive Suppression and a  Shape-Based Constraint