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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

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Notes

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

  2. 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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Acknowledgments

The research leading to these results received funding from the European Union Seventh Frame Programme (FP7/2007-2013) under Grant agreement 304857.

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Authors and Affiliations

  1. Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018, Barcelona, Spain

    Esmeralda Ruiz Pujadas, Gemma Piella, Mario Ceresa & Miguel Angel González Ballester

  2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800, Kgs. Lyngby, Denmark

    Hans Martin Kjer

  3. ICREA, Barcelona, Spain

    Miguel Angel González Ballester

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  1. Esmeralda Ruiz Pujadas
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Correspondence to Esmeralda Ruiz Pujadas.

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Conflict of interest

Esmeralda Ruiz Pujadas, Hans Martin Kjer, Gemma Piella, Mario Ceresa and Miguel Angel González declare that they have no conflict of interest.

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All human and animal studies have been approved.

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Ruiz Pujadas, E., Kjer, H.M., Piella, G. et al. Random walks with shape prior for cochlea segmentation in ex vivo \(\mu \hbox {CT}\) . Int J CARS 11, 1647–1659 (2016). https://doi.org/10.1007/s11548-016-1365-8

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  • DOI: https://doi.org/10.1007/s11548-016-1365-8

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