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. 2017 Sep 19:4:170132.
doi: 10.1038/sdata.2017.132.

A multiscale imaging and modelling dataset of the human inner ear

Nicolas Gerber  1 , Mauricio Reyes  2 , Livia Barazzetti  2 , Hans Martin Kjer  3 , Sergio Vera  4 , Martin Stauber  5 , Pavel Mistrik  6 , Mario Ceresa  7 , Nerea Mangado  7 , Wilhelm Wimmer  1   8 , Thomas Stark  8 , Rasmus R Paulsen  3 , Stefan Weber  1 , Marco Caversaccio  9 , Miguel A González Ballester  7   10
Affiliations

A multiscale imaging and modelling dataset of the human inner ear

Nicolas Gerber et al. Sci Data. .

Abstract

Understanding the human inner ear anatomy and its internal structures is paramount to advance hearing implant technology. While the emergence of imaging devices allowed researchers to improve understanding of intracochlear structures, the difficulties to collect appropriate data has resulted in studies conducted with few samples. To assist the cochlear research community, a large collection of human temporal bone images is being made available. This data descriptor, therefore, describes a rich set of image volumes acquired using cone beam computed tomography and micro-CT modalities, accompanied by manual delineations of the cochlea and sub-compartments, a statistical shape model encoding its anatomical variability, and data for electrode insertion and electrical simulations. This data makes an important asset for future studies in need of high-resolution data and related statistical data objects of the cochlea used to leverage scientific hypotheses. It is of relevance to anatomists, audiologists, computer scientists in the different domains of image analysis, computer simulations, imaging formation, and for biomedical engineers designing new strategies for cochlear implantations, electrode design, and others.

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Conflict of interest statement

Scanco Medical AG has used its own scanners μCT 50 and μCT 100 for the acquisition of the μCT data.

Figures

Figure 1
Figure 1. Spiral of cochlear data generation:
From harvested cochlea specimens, μCT and Cone-beam CT scans, image segmentations, 3D models of the human cochlea, statistical shape model, geometrical models of surrounding cochlear structures, finite-element data objects for electrode insertion and electrical simulation. The resulting data are summarized in Tables 2 and 3.
Figure 2
Figure 2
Figure 3
Figure 3. Overview and origin of acquired image datasets with number of specimens (s) and number of image volumes (n).
Each image set was given an ID composed of the specimen provenance (letter A–D), low, high resolution or segmentation mask (L, H or S) and a set identification number.
Figure 4
Figure 4. Finite element mesh obtained for a single patient.
Faces are cut for visualization purpose.
See this image and copyright information in PMC

References

Data Citations

    1. 2017. SICAS Medical Image Repository. https://doi.org/10.22016/smir.o.121756 - DOI
    1. 2017. SICAS Medical Image Repository. https://doi.org/10.22016/smir.o.121755 - DOI
    1. 2017. SICAS Medical Image Repository. https://doi.org/10.22016/smir.o.121754 - DOI
    1. 2017. SICAS Medical Image Repository. https://doi.org/10.22016/smir.o.121753 - DOI
    1. 2017. SICAS Medical Image Repository. https://doi.org/10.22016/smir.o.121752 - DOI

References

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