Statistical Shape Model with Random Walks for Inner Ear Segmentation

Pujadas, Esmeralda Ruiz; Kjer, Hans Martin; Piella, Gemma; Ballester, Miguel Ángel González

doi:10.1007/978-3-319-51237-2_8

Cited by 3 publications

(3 citation statements)

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“…A topology preservation method is also proposed to avoid leakage in the interior and turns of the cochlea [14]. This paper extends a previous conference communication [15] by presenting a more detailed description of the method, as well as a novel way to regulate the influence of the confidence map in the proposed formulation. We also present novel results, including a thorough analysis of the method as well as a comparison with other similar methods.…”

Section: Introductionmentioning

confidence: 84%

Random walks with statistical shape prior for cochlea and inner ear segmentation in micro-CT images

Pujadas

Piella

Kjer

et al. 2017

Machine Vision and Applications

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Section: Introductionmentioning

confidence: 84%

Random walks with statistical shape prior for cochlea and inner ear segmentation in micro-CT images

Pujadas

Piella

Kjer

et al. 2017

Machine Vision and Applications

Self Cite

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“…Using a shape prior, i.e., learning "plausible" shapes from training data, enables regularizing segmentations obtained from noisy or partially occluded images. The first formal application of this concept in intracochlear segmentation utilized ASM by Noble et al 8 for pre-implantation CTs, with extensions to post-implantation CT's in Reda et al 23 and a multi-region approach by Romera et al 24 An evaluation of the ASM's candidate search strategy was performed by Gaa et al 25 A shape prior approach used to constrain the solution of a level set of the entire cochlear labyrinth, but not the intracochlear anatomy, was developed by Zhu et al, 26 whereas Ruiz Pujadas et al 27 developed a solution for intracochlear anatomy but only for segmenting μCT images. Demarcy 28 proposed using statistical modeling to represent a shape parameterization and image appearance for use in intracochlear segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…An evaluation of the ASM’s candidate search strategy was performed by Gaa et al 25 . A shape prior approach used to constrain the solution of a level set of the entire cochlear labyrinth, but not the intracochlear anatomy, was developed by Zhu et al., 26 whereas Ruiz Pujadas et al 27 . developed a solution for intracochlear anatomy but only for segmenting μCT images.…”

Section: Introductionmentioning

confidence: 99%

Validation of active shape model techniques for intracochlear anatomy segmentation in computed tomography images

2023

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Cochlear implants (CIs) have been shown to be highly effective restorative devices for patients suffering from severe-to-profound hearing loss. Hearing outcomes with CIs depend on electrode positions with respect to intracochlear anatomy. Intracochlear anatomy can only be directly visualized using high-resolution modalities, such as micro-computed tomography (μCT), which cannot be used in vivo. However, active shape models (ASM) have been shown to be robust and effective for segmenting intracochlear anatomy in large scale datasets of patient computed tomographies (CTs). We present an extended dataset of μCT specimens and aim to evaluate the ASM's performance more comprehensively than has been previously possible.Approach: Using a dataset of 16 manually segmented cochlea specimens on μCTs, we found parameters that optimize mean CT segmentation performance and then evaluate the effect of library size on the ASM. The optimized ASM was further evaluated on a clinical dataset of 134 CT images to assess method reliability Results: Optimized parameters lead to mean CT segmentation performance to 0.36 mm point-to-point error, 0.10 mm surface error, and 0.83 Dice score. Larger library sizes provide diminishing returns on segmentation performance and total variance captured by the ASM. We found our method to be clinically reliable with the main performance limitation that was found to be the candidate search process rather than model representation. Conclusions:We have presented a comprehensive validation of the ASM for use in intracochlear anatomy segmentation. These results are critical to understand the limitations of the method for clinical use and for future development.

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