Patient-specific estimation of detailed cochlear shape from clinical CT images

Kjer, Hans Martin; Fagertun, Jens; Wimmer, Wilhelm; Gerber, Nicholas; Vera, Sergio; Barazzetti, Luigi; Mangado, Nerea; Ceresa, Mario; Piella, Gemma; Stark, Thomas; Stauber, Martin; Weber, Stefan; Caversaccio, Marco; Ballester, Miguel Ángel González; Paulsen, Rasmus Reinhold

doi:10.1007/s11548-017-1701-7

Cited by 19 publications

(24 citation statements)

References 28 publications

(42 reference statements)

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“…There are also likely differences in the way the gold-standard and clinical coordinate systems were aligned. Kjer et al 23 reported mean surface errors of 0.11 mm for the cochlear scalae.…”

Section: Experiments Results and Discussionmentioning

confidence: 98%

“…Evaluation metrics were based on Dice similarity coefficients and surface errors, so it is unclear how well this method can estimate surgically relevant parameters. Kjer et al 23 developed a similar approach using a statistical deformation model, reporting measurement accuracy and precision for cochlear length, width and height in addition to surface errors, but with no consideration of vertical trajectories. van der Jagt et al 24 describe an automatic, three-dimensional tracing method that was used to estimate inner and outer wall radii, duct diameter and vertical trajectory in low-resolution CT scans of 242 patients.…”

mentioning

confidence: 99%

“…In this work, we describe a fast, simple and freely available method to fit surface models of the otic capsule to CT data. The fitting may be directed by a statistical model, in the spirit of Noble et al 22 and Kjer et al 23 , or constrained only by a smoothness criterion, in the spirit of Iyaniwura et al 10 . We compare the performance of the two approaches, with specific reference to three-dimensional, surgically relevant measurements like those considered by van der Jagt et al 24 .…”

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confidence: 99%

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Practicable assessment of cochlear size and shape from clinical CT images

Gee

Zhao

Treece

et al. 2021

Sci Rep

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There is considerable interpersonal variation in the size and shape of the human cochlea, with evident consequences for cochlear implantation. The ability to characterize a specific cochlea, from preoperative computed tomography (CT) images, would allow the clinician to personalize the choice of electrode, surgical approach and postoperative programming. In this study, we present a fast, practicable and freely available method for estimating cochlear size and shape from clinical CT. The approach taken is to fit a template surface to the CT data, using either a statistical shape model or a locally affine deformation (LAD). After fitting, we measure cochlear size, duct length and a novel measure of basal turn non-planarity, which we suggest might correlate with the risk of insertion trauma. Gold-standard measurements from a convenience sample of 18 micro-CT scans are compared with the same quantities estimated from low-resolution, noisy, pseudo-clinical data synthesized from the same micro-CT scans. The best results were obtained using the LAD method, with an expected error of 8–17% of the gold-standard sample range for non-planarity, cochlear size and duct length.

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“…There are also likely differences in the way the gold-standard and clinical coordinate systems were aligned. Kjer et al 23 reported mean surface errors of 0.11 mm for the cochlear scalae.…”

Section: Experiments Results and Discussionmentioning

confidence: 98%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Practicable assessment of cochlear size and shape from clinical CT images

Gee

Zhao

Treece

et al. 2021

Sci Rep

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“…These algorithms can then be used to build anatomical models from clinical CT imaging, allowing accurate 3D reconstruction of a patient’s anatomy. Creation of automatic segmentation algorithms requires tedious manual segmentation of micro-CT data, therefore many groups have been working on automating segmentation with polynomial functions [10], atlas-based registration [11], SSMs [12–14], and deep learning [15, 16]. Further, labour intensive manual segmentation often leads to smaller sample sizes.…”

Section: Discussionmentioning

confidence: 99%

“…Segmentation of these structures in 3D is important for surgical planning [3], robotic surgery [4], virtual-reality surgical simulation [5–7], and patient-specific cochlear implant programming [8, 9]. Unfortunately, manual segmentation is very labour intensive, therefore many groups have been working on automating segmentation with polynomial functions [10], atlas-based registration [11], statistical shape models [12–14], and deep learning [15, 16]. This type of research requires large datasets, and many groups have made their datasets publicly available to help the larger research community [17].…”

Section: Introductionmentioning

confidence: 99%

Morphological analysis of sigmoid sinus anatomy: clinical applications to neurotological surgery

Osch

Allen

Gare

et al. 2019

J of Otolaryngol - Head & Neck Surg

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ObjectivesThe primary objective of this study was to use high-resolution micro-CT images to create accurate three-dimensional (3D) models of several intratemporal structures, and to compare several surgically important dimensions within the temporal bone. The secondary objective was to create a statistical shape model (SSM) of a dominant and non-dominant sigmoid sinus (SS) to provide a template for automated segmentation algorithms.MethodsA free image processing software, 3D Slicer, was utilized to create three-dimensional reconstructions of the SS, jugular bulb (JB), facial nerve (FN), and external auditory canal (EAC) from micro-CT scans. The models were used to compare several clinically important dimensions between the dominant and non-dominant SS. Anatomic variability of the SS was also analyzed using SSMs generated using the Statismo software framework.ResultsThree-dimensional models from 38 temporal bones were generated and analyzed. Right dominance was observed in 74% of the paired SSs. All distances were significantly shorter on the dominant side (p < 0.05), including: EAC – SS (dominant: 13.7 ± 3.4 mm; non-dominant: 15.3 ± 2.7 mm), FN – SS (dominant: 7.2 ± 1.8 mm; non-dominant: 8.1 ± 2.3 mm), 2nd genu FN – superior tip of JB (dominant: 8.7 ± 2.2 mm; non-dominant: 11.2 ± 2.6 mm), horizontal distance between the superior tip of JB – descending FN (dominant: 9.5 ± 2.3 mm; non-dominant: 13.2 ± 3.5 mm), and horizontal distance between the FN at the stylomastoid foramen – JB (dominant: 5.4 ± 2.2 mm; non-dominant: 7.7 ± 2.1). Analysis of the SSMs indicated that SS morphology is most variable at its junction with the transverse sinus, and least variable at the JB.ConclusionsThis is the first known study to investigate the anatomical variation and relationships of the SS using high resolution scans, 3D models and statistical shape analysis. This analysis seeks to guide neurotological surgical approaches and provide a template for automated segmentation and surgical simulation.

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Fully Automated Measurement of Cochlear Duct Length From Clinical Temporal Bone Computed Tomography

et al. 2021

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Objectives/Hypothesis: To present and validate a novel fully automated method to measure cochlear dimensions, including cochlear duct length (CDL).Study Design: Cross-sectional study.Methods: The computational method combined 1) a deep learning (DL) algorithm to segment the cochlea and otic capsule and 2) geometric analysis to measure anti-modiolar distances from the round window to the apex. The algorithm was trained using 165 manually segmented clinical computed tomography (CT). A Testing group of 159 CTs were then measured for cochlear diameter and width (A-and B-values) and CDL using the automated system and compared against manual measurements. The results were also compared with existing approaches and historical data. In addition, pre-and postimplantation scans from 27 cochlear implant recipients were studied to compare predicted versus actual array insertion depth.Results: Measurements were successfully obtained in 98.1% of scans. The mean CDL to 900 was 35.52 mm (SD, 2.06; range, [30.91-40.50]), the mean A-value was 8.88 mm (0.47; [7.67-10.49]), and mean B-value was 6.38 mm (0.42; [5.16-7.38]). The R 2 fit of the automated to manual measurements was 0.87 for A-value, 0.70 for B-value, and 0.71 for CDL. For anti-modiolar arrays, the distance between the imaged and predicted array tip location was 0.57 mm (1.25; [0.13-5.28]). Conclusion:Our method provides a fully automated means of cochlear analysis from clinical CTs. The distribution of CDL, dimensions, and cochlear quadrant lengths is similar to those from historical data. This approach requires no radiographic experience and is free from user-related variation.

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Patient-specific estimation of detailed cochlear shape from clinical CT images

Abstract: The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method.

Cited by 19 publications

References 28 publications

Practicable assessment of cochlear size and shape from clinical CT images

Practicable assessment of cochlear size and shape from clinical CT images

Morphological analysis of sigmoid sinus anatomy: clinical applications to neurotological surgery

Fully Automated Measurement of Cochlear Duct Length From Clinical Temporal Bone Computed Tomography

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