A validated methodology for the 3D reconstruction of cochlea geometries using human microCT images

Abstract

Accurate reconstruction of the inner ear is a prerequisite for the modelling and understanding of the inner ear mechanics. In this study, we present a semi-automated methodology for accurate reconstruction of the major inner ear structures (scalae, basilar membrane, stapes and semicircular canals). For this purpose, high resolution microCT images of a human specimen were used. The segmentation methodology is based on an iterative level set algorithm which provides the borders of the structures of interest. An enhanced coupled level set method which allows the simultaneous multiple image labeling without any overlapping regions has been developed for this purpose. The marching cube algorithm was applied in order to extract the surface from the segmented volume. The reconstructed geometries are then post-processed to improve the basilar membrane geometry to realistically represent physiologic dimensions. The final reconstructed model is compared to the available data from the literature. The results show that our generated inner ear structures are in good agreement with the published ones, while our approach is the most realistic in terms of the basilar membrane thickness and width reconstruction.

Three-Dimensional Reconstruction of Fuzzy Medical Images Using Quantum Algorithm

“…Literature [11] and Literature [13] are always lower than the proposed method. The maximum texture self-similarity coefficient can reach 0.994 in this study.…”

“…The method in Literature [10] produces good image contour feature matching effect, but the center part of the image is poorly matched. In comparison, the methods in Literature [11] and Literature [13] have better overall matching effect. However, it can be clearly found from the figure that the image feature matching point of the proposed method has a large amount of data and a wide distribution, and the matching effect is superior to the method in Literature [11] and Literature [13].…”

“…where, 1 P and 2 P stand for the power value of effective signal and noise, respectively. (a)Literature [7] (b) Literature [10] (c)Literature [11] (d)Literature [13] (e) The proposed method According to Figure 5. In Literature [7], the feature matching points of the algorithm are basically distributed below the image, but the matching effect of the feature points above the image is poor.…”

“…The maximum texture self-similarity coefficient can reach 0.994 in this study. Therefore, the proposed fuzzy medical image 3D reconstruction method using the quantum algorithm has a higher image quality while the reconstruction error of the methods in Literature [7], Literature [11] and Literature [13] is always greater than the proposed method. Literature [7] has the largest error, which can be as high as 1.7mm, while the maximum reconstruction error of the proposed method is not higher than 0.9mm.…”

A Finite Element Model of the Human Head for Simulation of Bone-conducted Sound

“…Another possible solution would be used the vibratory response around the temporal bone area in the LiUHead as the general input for other FE models of the inner ear. For example, Kim et al (2013) published a 3D FE model of the human auditory periphery including a coiled-cochlea model and Sakellarios et al (2017) presented 3D cochlea geometries using human microCT images. Such models could be used to investigate inner ear responses based on vibratory patterns obtained in the LiUHead.…”