Image Analysis and Visualization of the Human Mastoid Air Cell System

Cros, Olivier

doi:10.3384/lic.diva-122179

Cited by 3 publications

(2 citation statements)

References 29 publications

(38 reference statements)

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“…The most general technique utilized to elicit the details of images of the ear cavity is computed tomography scan (CT scan) [25]. The processing of mastoid air cells is only partially represented on a CT scan by Olivier Cros [26]. In [27] a two-class (normal and abnormal) classifier based on convolutional neural networks deep learning model was introduced.…”

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Classification of mastoid air cells by CT scan images using deep learning method

et al. 2022

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Purpose Mastoid abnormalities show different types of ear illnesses, however inadequacy of experts and low accuracy of diagnostic demand a new approach to detect these abnormalities and reduce human mistakes. The manual analysis of mastoid CT scans is time-consuming and labor-intensive. In this paper the first and robust deep learning-based approaches is introduced to diagnose mastoid abnormalities using a large database of CT images obtained in the clinical center with remarkable accuracy. Methods In this paper, mastoid abnormalities are classified using the Xception based Convolutional Neural Network (CNN) model, with optimizer Adamax into five categories (Complete pneumatized, Opacification in pneumatization, Partial pneumatization, Opacification in partial pneumatization, None pneumatized). For this reason, a total of 24,800 slides of 152 patients were selected that include the mastoid from most upper to the lowest part of the middle ear cavity to complete the construction of the proposed deep neural network model. Results The proposed model had the best accuracy of 87.80% (based on grader 1) and 88.44% (based on grader 2) on the 20th epoch and 87.70% (based on grader 1) and 87.56% (based on grader 2) on average and also significantly faster than other types of implemented architectures in terms of the computer running time (in seconds). The 99% confidence interval of the average accuracy was 0.012 which means that the true accuracy is 87.80% and 87.56% ± 1.2% that indicates the power of the model. Conclusions The manual analysis of ear cavity CT scans is often time-consuming and prone to errors due to various inter- or intra operator variability studies. The proposed method can be used to automatically analyze the middle ear cavity to classify mastoid abnormalities, which is markedly faster than most types of models with the highest accuracy.

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Classification of mastoid air cells by CT scan images using deep learning method

et al. 2022

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“…more internal, cells and information on how they communicate with the midline. As explained in [13], clipping planes can be used to partially hide some of the rendered data in order to see the more internal parts of the air cell system.…”

Section: What Is the Size Of An Air Cell?mentioning

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Structural properties of the mastoid using image analysis and visualization

Cros¹

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Volume rendering of a 3D shape analysis where medial balls are fitting the mastoid air cell system of a human temporal bone specimen, overlaid over a manually cropped version of the original data. The different colours represent the different size classes: from very small in dark blue to vary large in purple. The bone is rendered with a modified transfer function so as to reveal the micro-channels.Copyright © 2017 Olivier Cros, unless otherwise noted. AbstractThe mastoid, located in the temporal bone, houses an air cell system whose cells have a variation in size that can go far below current conventional clinical CT scanner resolution. Therefore, the mastoid air cell system is only partially represented in a clinical CT scan. Where the conventional clinical CT scanner lacks level of minute details, micro-CT scanning provides an overwhelming amount of fine details. The temporal bone being one of the most complex in the human body, visualization of micro-CT scanning of this bone awakens the curiosity of the experimenter, especially with the correct visualization settings.This thesis first presents a statistical analysis determining the surface area to volume ratio of the mastoid air cell system of human temporal bone, from micro-CT scanning using methods previously applied for conventional clinical CT scans. The study compared current results with previous studies, with successive downsampling the data down to a resolution found in conventional clinical CT scans. The results from the statistical analysis showed that all the small mastoid air cells, that cannot be detected in conventional clinical CT scans, do heavily contribute to the estimation of the surface area, and in consequence to the estimation of the surface area to volume ratio by a factor of about 2.6. Such a result further strengthens the idea of the mastoid to play an active role in pressure regulation and gas exchange.Discovery of micro-channels through specific use of a non-traditional transfer function was then reported, where a qualitative and a quantitative preanalysis were performed and reported. To gain more knowledge about these micro-channels, a local structure tensor analysis was applied where structures are described in terms of planar, tubular, or isotropic structures. The results from this structural tensor analysis suggest these micro-channels to potentially be part of a more complex framework, which hypothetically would provide a separate blood supply for the mucosa lining the mastoid air cell system.The knowledge gained from analysing the micro-channels as locally providing blood to the mucosa, led to the consideration of how inflammation of the mucosa could impact the pneumatization of the mastoid air cell system. Though very primitive, a 3D shape analysis of the mastoid air cell system was carried out. The mastoid air cell system was first represented in a compact form through a medial axis, from which medial balls could be used. The medial balls, representative of how large the mastoid air cells can be locally, were used in two co...

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