Micro-CT of the human ossicular chain: Statistical shape modeling and implications for otologic surgery

The ossicular chain is a middle ear structure consisting of the small incus, malleus and stapes bones, which transmit tympanic membrane vibrations caused by sound to the inner ear. Despite being shown to be highly variable in shape, there are very few morphological studies of the ossicles. The objective of this study was to use a large sample of cadaveric ossicles to create a set of three-dimensional models and study their statistical variance. Thirty-three cadaveric temporal bone samples were scanned using micro-computed tomography (μCT) and segmented. Statistical shape models (SSMs) were then made for each ossicle to demonstrate the divergence of morphological features. Results revealed that ossicles were most likely to vary in overall size, but that more specific feature variability was found at the manubrium of the malleus, the long process and lenticular process of the incus, and the crura and footplate of the stapes. By analyzing samples as whole ossicular chains, it was revealed that when fixed at the malleus, changes along the chain resulted in a wide variety of final stapes positions. This is the first known study to create high-quality, three-dimensional SSMs of the human ossicles. This information can be used to guide otological surgical training and planning, inform ossicular prosthesis development, and assist with other ossicular studies and applications by improving automated segmentation algorithms. All models have been made publicly available

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 crit- ical 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 similar- ity 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

Volume 1 Issue 1 (1991)

Gentelle Pierre. Dresh, Jean. Géographie des régions arides, 1983. In: Études chinoises, n°2, 1983. pp. 83-84