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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

01.09.2016 | Original Article

Random walks with shape prior for cochlea segmentation in ex vivo \(\mu \hbox {CT}\)

verfasst von: Esmeralda Ruiz Pujadas, Hans Martin Kjer, Gemma Piella, Mario Ceresa, Miguel Angel González Ballester

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 9/2016

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Abstract

Purpose

Cochlear implantation is a safe and effective surgical procedure to restore hearing in deaf patients. However, the level of restoration achieved may vary due to differences in anatomy, implant type and surgical access. In order to reduce the variability of the surgical outcomes, we previously proposed the use of a high-resolution model built from \(\mu \hbox {CT}\) images and then adapted to patient-specific clinical CT scans. As the accuracy of the model is dependent on the precision of the original segmentation, it is extremely important to have accurate \(\mu \hbox {CT}\) segmentation algorithms.

Methods

We propose a new framework for cochlea segmentation in ex vivo \(\mu \hbox {CT}\) images using random walks where a distance-based shape prior is combined with a region term estimated by a Gaussian mixture model. The prior is also weighted by a confidence map to adjust its influence according to the strength of the image contour. Random walks is performed iteratively, and the prior mask is aligned in every iteration.

Results

We tested the proposed approach in ten \(\mu \hbox {CT}\) data sets and compared it with other random walks-based segmentation techniques such as guided random walks (Eslami et al. in Med Image Anal 17(2):236–253, 2013) and constrained random walks (Li et al. in Advances in image and video technology. Springer, Berlin, pp 215–226, 2012). Our approach demonstrated higher accuracy results due to the probability density model constituted by the region term and shape prior information weighed by a confidence map.

Conclusion

The weighted combination of the distance-based shape prior with a region term into random walks provides accurate segmentations of the cochlea. The experiments suggest that the proposed approach is robust for cochlea segmentation.

http://www.hear-eu.eu/.

The performance measures are defined as: \(\mathrm{Overlap}= \frac{\mathrm{TP}}{\mathrm{TP}\,+\,\mathrm{FN}\,+\,\mathrm{FP}}\), \(\mathrm{Sensitivity}= \frac{\mathrm{TP}}{\mathrm{TP}\,+\,\mathrm{FN}}\), \(\mathrm{Specificity}=\frac{\mathrm{TN}}{\mathrm{TN}\,+\,\mathrm{FP}}\) and \(\mathrm{Similarity}= \frac{2\mathrm{TP}}{2\mathrm{TP}\,+\,\mathrm{FN}+\mathrm{FP}}\) where TP and FP stand for true positive and false positive, respectively, and TN and FN stand for true negative and false negative, respectively.

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Titel: Random walks with shape prior for cochlea segmentation in ex vivo
verfasst von: Esmeralda Ruiz Pujadas
Hans Martin Kjer
Gemma Piella
Mario Ceresa
Miguel Angel González Ballester
Publikationsdatum: 01.09.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 9/2016
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-016-1365-8

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Random walks with shape prior for cochlea segmentation in ex vivo \(\mu \hbox {CT}\)