IE-Vnet: Deep Learning-Based Segmentation of the Inner Ear's Total Fluid Space

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms

BACKGROUND

The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS.

METHODS

A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared.

RESULTS

The mean DCS of the 3D U-Net were 0.905 ± 0.080, 0.933 ± 0.018, and 0.928 ± 0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p = 0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r = 0.805).

CONCLUSIONS

A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.

Improving rehabilitation of deaf patients by advanced imaging before cochlear implantation

INTRODUCTION

Cochlear implants have advanced the management of severe to profound deafness. However, there is a strong disparity in hearing performance after implantation from one patient to another. Moreover, there are several advanced kinds of imaging assessment before cochlear implantation. Microstructural white fiber degeneration can be studied with Diffusion weighted MRI (DWI) or tractography of the central auditory pathways. Functional MRI (fMRI) allows us to evaluate brain function, and CT or MRI segmentation to better detect inner ear anomalies.

OBJECTIVE

This literature review aims to evaluate how helpful pre-implantation anatomic imaging can be to predict hearing rehabilitation outcomes in deaf patients. These techniques include DWI and fMRI of the central auditory pathways, and automated labyrinth segmentation by CT scan, cone beam CT and MRI.

DESIGN

This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were selected by searching in PubMed and by checking the reference lists of relevant articles. Inclusion criteria were adults over 18, with unilateral or bilateral hearing loss, who had DWI acquisition or fMRI or CT/ Cone Beam CT/ MRI image segmentation.

RESULTS

After reviewing 172 articles, we finally included 51. Studies on DWI showed changes in the central auditory pathways affecting the white matter, extending to the primary and non-primary auditory cortices, even in sudden and mild hearing impairment. Hearing loss patients show a reorganization of brain activity in various areas, such as the auditory and visual cortices, as well as regions involved in language and emotions, according to fMRI studies. Deep Learning's automatic segmentation produces the best CT segmentation in just a few seconds. MRI segmentation is mainly used to evaluate fluid space of the inner ear and determine the presence of an endolymphatic hydrops.

CONCLUSION

Before cochlear implantation, a DWI with tractography can evaluate the central auditory pathways up to the primary and non-primary auditory cortices. This data is then used to generate predictions on the auditory rehabilitation of patients. A CT segmentation with systematic 3D reconstruction allow a better evaluation of cochlear malformations and predictable difficulties during surgery.

Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework

Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmark detection of helicotrema, oval and round window. A fully automated pipeline with a single, dual-headed volumetric 3D U-Net was implemented, trained and evaluated using manually labeled in-house datasets from cadaveric specimen ([Formula: see text]) and clinical practice ([Formula: see text]). The model robustness was further evaluated on three independent open-source datasets ([Formula: see text] scans) consisting of cadaveric specimen scans. For the in-house datasets, Dice scores of [Formula: see text], intersection-over-union scores of [Formula: see text] and average Hausdorff distances of [Formula: see text] and [Formula: see text] voxel units were achieved. The landmark localization task was performed automatically with an average localization error of [Formula: see text] voxel units. A robust, albeit reduced performance could be attained for the catalogue of three open-source datasets. Results of the ablation studies with 43 mono-parametric variations of the basal architecture and training protocol provided task-optimal parameters for both categories. Ablation studies against single-task variants of the basal architecture showed a clear performance benefit of coupling landmark localization with segmentation and a dataset-dependent performance impact on segmentation ability.

Endolymphatic space is age-dependent

Knowledge of the physiological endolymphatic space (ELS) is necessary to estimate endolymphatic hydrops (ELH) in patients with vestibulocochlear syndromes. Therefore, the current study investigated age-dependent changes in the ELS of participants with normal vestibulocochlear testing. Sixty-four ears of 32 participants with normal vestibulocochlear testing aged between 21 and 75 years (45.8 ± 17.2 years, 20 females, 30 right-handed, two left-handed) were examined by intravenous delayed gadolinium-enhanced magnetic resonance imaging of the inner ear (iMRI). Clinical diagnostics included neuro-otological assessment, video-oculography during caloric stimulation, and head-impulse test. iMRI data analysis provided semi-quantitative visual grading and automatic algorithmic quantitative segmentation of ELS volume (3D, mm³) using a deep learning-based segmentation of the inner ear's total fluid space (TFS) and volumetric local thresholding, as described earlier. As a result, following a 4-point ordinal scale, a mild ELH (grade 1) was found in 21/64 (32.8%) ears uni- or bilaterally in either cochlear, vestibulum, or both. Age and ELS were found to be positively correlated for the inner ear (r(64) = 0.33, p < 0.01), and vestibulum (r(64) = 0.25, p < 0.05). For the cochlea, the values correlated positively without reaching significance (r(64) = 0.21). In conclusion, age-dependent increases of the ELS should be considered when evaluating potential ELH in single subjects and statistical group comparisons.

Vestibular paroxysmia entails vestibular nerve function, microstructure and endolymphatic space changes linked to root-entry zone neurovascular compression

Combining magnetic resonance imaging (MRI) sequences that permit the determination of vestibular nerve angulation (NA = change of nerve caliber or direction), structural nerve integrity via diffusion tensor imaging (DTI), and exclusion of endolymphatic hydrops (ELH) via delayed gadolinium-enhanced MRI of the inner ear (iMRI) could increase the diagnostic accuracy in patients with vestibular paroxysmia (VP). Thirty-six participants were examined, 18 with VP (52.6 ± 18.1 years) and 18 age-matched with normal vestibulocochlear testing (NP 50.3 ± 16.5 years). This study investigated whether (i) NA, (ii) DTI changes, or (iii) ELH occur in VP, and (iv) to what extent said parameters relate. Methods included vestibulocochlear testing and MRI data analyses for neurovascular compression (NVC) and NA verification, DTI and ELS quantification. As a result, (i) NA increased NVC specificity. (ii) DTI structural integrity was reduced on the side affected by VP (p < 0.05). (iii) 61.1% VP showed mild ELH and higher asymmetry indices than NP (p > 0.05). (iv) "Disease duration" and "total number of attacks" correlated with the decreased structural integrity of the affected nerve in DTI (p < 0.001). NVC distance within the nerve's root-entry zone correlated with nerve function (Roh = 0.72, p < 0.001), nerve integrity loss (Roh = - 0.638, p < 0.001), and ELS volume (Roh = - 0.604, p < 0.001) in VP. In conclusion, this study is the first to link eighth cranial nerve function, microstructure, and ELS changes in VP to clinical features and increased vulnerability of NVC in the root-entry zone. Combined MRI with NVC or NA verification, DTI and ELS quantification increased the diagnostic accuracy at group-level but did not suffice to diagnose VP on a single-subject level due to individual variability and lack of diagnostic specificity.