Automated Segmentation of the Inner Ear and Round Window in Computed Tomography scans using Convolutional Neural Networks
Abstract
Computed tomography (CT) scans are acquired prior to cochlear implant (CI) surgery. Three-dimensional segmentations of the inner ear (IE) and round window (RW) based on clinical CTs can improve the CI procedure. Software pipelines are presented here which employ convolutional neural networks to automatically segment the IE and RW. The first pipeline produces high resolution segmentations of the IE and RW in tightly cropped CTs. Mean IE Dice score and RW centroid error were 0.88, 0.57mm and 0.93, 0.18mm in implanted and non-implanted samples, respectively. The second pipeline automatically segments the IE in large field of view CTs of any rotational orientation. Mean Dice scores of 0.83 and 0.89 were achieved in implanted and non-implanted samples, respectively. This is the first known study to present automated segmentations of the IE and RW in CTs with CIs. The pipelines provide quick and accurate segmentations.