Automated Segmentation of Temporal Bone Structures

Mastoidectomy is a challenging surgical procedure that is difficult to perform and practice. As supplementation to current training techniques, surgical simulators have been developed with the ability to visualize and operate on temporal bone anatomy. Medical image segmentation is done to create three-dimensional models of anatomical structures for simulation. Manual segmentation is an accurate but time-consuming process that requires an expert to label each structure on images. An automatic method for segmentation would allow for more practical model creation. The objective of this work was to create an automated segmentation algorithm for structures of the temporal bone relevant to mastoidectomy. The first method explored was multi-atlas based segmentation of the sigmoid sinus which produced accurate and consistent results. In order to segment other structures and improve robustness and accuracy, two convolutional neural networks were compared. The convolutional neural network implementation produced results that were more accurate than previously published work

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

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

Intrinsic Measures and Shape Analysis of the Intratemporal Facial Nerve

Hypothesis: To characterize anatomical measurements and shape variation of the facial nerve within the temporal bone, and to create statistical shape models (SSMs) to enhance knowledge of temporal bone anatomy and aid in automated segmentation. Background: The facial nerve is a fundamental structure in otologic surgery, and detailed anatomic knowledge with surgical experience are needed to avoid its iatrogenic injury. Trainees can use simulators to practice surgical techniques, however manual segmentation required to develop simulations can be time consuming. Consequently, automated segmentation algorithms have been developed that use atlas registration, SSMs, and deep learning. Methods: Forty cadaveric temporal bones were evaluated using three dimensional microCT (μCT) scans. The image sets were aligned using rigid fiducial registration, and the facial nerve canals were segmented and analyzed. Detailed measurements were performed along the various sections of the nerve. Shape variability was then studied using two SSMs: one involving principal component analysis (PCA) and a second using the Statismo framework. Results: Measurements of the nerve canal revealed mean diameters and lengths of the labyrinthine, tympanic, and mastoid segments. The landmark PCA analysis demonstrated significant shape variation along one mode at the distal tympanic segment, and along three modes at the distal mastoid segment. The Statismo shape model was consistent with this analysis, emphasizing the variability at the mastoid segment. The models were made publicly available to aid in future research and foster collaborative work. Conclusion: The facial nerve exhibited statistical variation within the temporal bone. The models used form a framework for automated facial nerve segmentation and simulation for trainees

Brain Differences in the Prefrontal Cortex, Amygdala, and Hippocampus in Youth with Congenital Adrenal Hyperplasia

Context: Classical Congenital Adrenal Hyperplasia (CAH) due to 21-hydroxylase deficiency results in hormone imbalances present both prenatally and postnatally that may impact the developing brain. Objective: To characterize gray matter morphology in the prefrontal cortex and subregion volumes of the amygdala and hippocampus in youth with CAH, compared to controls. Design: A cross-sectional study of 27 CAH youth (16 female; 12.6 ± 3.4 year) and 35 typically developing, healthy controls (20 female; 13.0 ± 2.8 year) with 3-T magnetic resonance imaging scans. Brain volumes of interest included bilateral prefrontal cortex, and nine amygdala and six hippocampal subregions. Between-subject effects of group (CAH vs control) and sex, and their interaction (group-by-sex) on brain volumes were studied, while controlling for intracranial volume (ICV) and group differences in body mass index and bone age. Results: CAH youth had smaller ICV and increased cerebrospinal fluid volume compared to controls. In fully-adjusted models, CAH youth had smaller bilateral, superior and caudal middle frontal volumes, and smaller left lateral orbito-frontal volumes compared to controls. Medial temporal lobe analyses revealed the left hippocampus was smaller in fully-adjusted models. CAH youth also had significantly smaller lateral nucleus of the amygdala and hippocampal subiculum and CA1 subregions. Conclusions: This study replicates previous findings of smaller medial temporal lobe volumes in CAH patients, and suggests that lateral nucleus of the amygdala, as well as subiculum and subfield CA1 of the hippocampus are particularly affected within the medial temporal lobes in CAH youth