PWD-3DNet: A Deep Learning-Based Fully-Automated Segmentation of Multiple Structures on Temporal Bone CT Scans

The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional anatomy. Segmentation of intra-temporal anatomy based on computed tomography (CT) images is necessary for applications such as surgical training and rehearsal, amongst others. However, temporal bone segmentation is challenging due to the similar intensities and complicated anatomical relationships among critical structures, undetectable small structures on standard clinical CT, and the amount of time required for manual segmentation. This paper describes a single multi-class deep learning-based pipeline as the first fully automated algorithm for segmenting multiple temporal bone structures from CT volumes, including the sigmoid sinus, facial nerve, inner ear, malleus, incus, stapes, internal carotid artery and internal auditory canal. The proposed fully convolutional network, PWD-3DNet, is a patch-wise densely connected (PWD) three-dimensional (3D) network. The accuracy and speed of the proposed algorithm was shown to surpass current manual and semi-automated segmentation techniques. The experimental results yielded significantly high Dice similarity scores and low Hausdorff distances for all temporal bone structures with an average of 86% and 0.755 millimeter (mm), respectively. We illustrated that overlapping in the inference sub-volumes improves the segmentation performance. Moreover, we proposed augmentation layers by using samples with various transformations and image artefacts to increase the robustness of PWD-3DNet against image acquisition protocols, such as smoothing caused by soft tissue scanner settings and larger voxel sizes used for radiation reduction. The proposed algorithm was tested on low-resolution CTs acquired by another center with different scanner parameters than the ones used to create the algorithm and shows potential for application beyond the particular training data used in the study

The BONEBRIDGE active transcutaneous bone conduction implant: effects of location, lifts and screws on sound transmission

Abstract Background The BONEBRIDGE (MED-EL, Innsbruck, Austria) is a bone-conduction implant used in the treatment of conductive and mixed hearing loss. The BONEBRIDGE consists of an external audio processor and a bone-conduction floating mass transducer that is surgically implanted into the skull in either the transmastoid, retrosigmoid or middle fossa regions. The manufacturer includes self-tapping screws to secure the transducer; however, self-drilling screws have also been used with success. In cases where the skull is not thick enough to house the transducer, lifts are available in a variety of sizes to elevate the transducer away from the skull. The objective of the present study was to investigate the effects of screw type, lift thickness, and implant location on the sound transmission of the BONEBRIDGE. Method Six cadaveric temporal bones were embalmed and dried for use in this study. In each sample, a hole was drilled in each of the three implant locations to house the implant transducer. At the middle fossa, six pairs of screw holes were pre-drilled; four pairs to be used with self-tapping screws and lifts (1, 2, 3, and 4 mm thick lifts, respectively), one pair with self-tapping screws and no lifts, and one pair with self-drilling screws and no lifts. At the transmastoid and retrosigmoid locations, one pair of screw holes were pre-drilled in each for the use of the self-tapping screws. The vibration of transmitted sound to the cochlea was measured using a laser Doppler vibrometry technique. The measurements were performed on the cochlear promontory at eight discrete frequencies (0.5, 0.75, 1, 1.5, 2, 3, 4 and 6 kHz). Vibration velocity of the cochlear wall was measured in all samples. Measurements were analyzed using a single-factor ANOVA to investigate the effect of each modification. Results No significant differences were found related to either screw type, lift thickness, or implant location. Conclusions This is the first known study to evaluate the effect of screw type, lift thickness, and implant location on the sound transmission produced by the BONEBRIDGE bone-conduction implant. Further studies may benefit from analysis using fresh cadaveric samples or in-vivo measurements