Toward an automatic preoperative pipeline for image-guided temporal bone surgery

Fauser, Johannes; Stenin, Igor; Bauer, Markus; Hsu, Wei-Chou; Kristin, Julia; Klenzner, Thomas; Schipper, Jörg; Mukhopadhyay, Anirban

doi:10.1007/s11548-019-01937-x

Cited by 33 publications

(52 citation statements)

References 24 publications

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“…We emphasize that due to our use of only the largest connected components from L U , the 3D U-Net robustly detects the majority of individual structures. In comparison to the 2D approach of [8], we see a notable difference in performance for the FN. While the slice-by-slice approach clearly offers better initialization for this small tubular nerve, the advantage does not apply to segmentation of its side branch, the chorda tympani.…”

Section: Resultsmentioning

confidence: 65%

“…6) and that general slight oversegmentation of the structures reduces the available free space. However, the rather low Dice of the JV comes again from the open boundaries at the inferior part of the structure ( [8]). The mean safety distances show only minor differences.…”

Section: Resultsmentioning

confidence: 99%

“…We adopt shape regularized deep learning, which has shown great potential in combining state-of-the-art accuracy while enforcing topological constraints [8,16]. Figure 3 shows the segmentation pipeline.…”

Section: Segmentationmentioning

confidence: 99%

“…1 Robotic drilling of a nonlinear access canal through the temporal bone requires preoperative planning consisting of two steps: segmentation of risk structures within the temporal bone (white bone on the CT slice) and trajectory planning for a collision-free trajectory from the surface of the skull (transparent) to the clinical target (e.g., the cochlea) nonlinear trajectories [8]. For the necessary segmentation of risk structures, approaches used either semiautomatic (Becker et al [1]), traditional fully automatic methods (Noble et al [12], Mangado et al [11]) or deep learning approaches (Fauser et al [8]). So far, existing solutions mostly rely on semiautomatic segmentation and linear planning, while automatic approaches and nonlinear planning show insufficient precision leading to unsafe trajectories.…”

Section: Introductionmentioning

confidence: 99%

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Retrospective in silico evaluation of optimized preoperative planning for temporal bone surgery

et al. 2020

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Purpose Robot-assisted surgery at the temporal bone utilizing a flexible drilling unit would allow safer access to clinical targets such as the cochlea or the internal auditory canal by navigating along nonlinear trajectories. One key sub-step for clinical realization of such a procedure is automated preoperative surgical planning that incorporates both segmentation of risk structures and optimized trajectory planning. Methods We automatically segment risk structures using 3D U-Nets with probabilistic active shape models. For nonlinear trajectory planning, we adapt bidirectional rapidly exploring random trees on Bézier Splines followed by sequential convex optimization. Functional evaluation, assessing segmentation quality based on the subsequent trajectory planning step, shows the suitability of our novel segmentation approach for this two-step preoperative pipeline. Results Based on 24 data sets of the temporal bone, we perform a functional evaluation of preoperative surgical planning. Our experiments show that the automated segmentation provides safe and coherent surface models that can be used in collision detection during motion planning. The source code of the algorithms will be made publicly available. Conclusion Optimized trajectory planning based on shape regularized segmentation leads to safe access canals for temporal bone surgery. Functional evaluation shows the promising results for both 3D U-Net and Bézier Spline trajectories.

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Section: Resultsmentioning

confidence: 65%

Section: Resultsmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retrospective in silico evaluation of optimized preoperative planning for temporal bone surgery

et al. 2020

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“…In our study, the additional time except for drilling was very similar to the results reported by Caversaccio et al 12 . In recent years, some researchers have proposed new methods for automatic segmentation of important structures in the temporal bone and automatic planning of trajectory, which can reduce the preoperative preparation time to several minutes 48 , 49 . Currently, our research group is testing another method that use the neural network based on deep learning to perform automatic segmentation and path planning in temporal bone CT.…”

Section: Discussionmentioning

confidence: 99%

Image-guided cochlear access by non-invasive registration: a cadaveric feasibility study

Wang

Liu

et al. 2020

Sci Rep

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Image-guided cochlear implant surgery is expected to reduce volume of mastoidectomy, accelerate recovery, and improve safety. The purpose of this study was to investigate the safety and effectiveness of image-guided cochlear implant surgery by a non-invasive registration method, in a cadaveric study. We developed a visual positioning frame that can utilize the maxillary dentition as a registration tool and completed the tunnels experiment on 5 cadaver specimens (8 cases in total). The accuracy of the entry point and the target point were 0.471 ± 0.276 mm and 0.671 ± 0.268 mm, respectively. The shortest distance from the margin of the tunnel to the facial nerve and the ossicular chain were 0.790 ± 0.709 mm and 1.960 ± 0.630 mm, respectively. All facial nerves, tympanic membranes, and ossicular chains were completely preserved. Using this approach, high accuracy was achieved in this preliminary study, suggesting that the non-invasive registration method can meet the accuracy requirements for cochlear implant surgery. Based on the above accuracy, we speculate that our method can also be applied to neurosurgery, orbitofacial surgery, lateral skull base surgery, and anterior skull base surgery with satisfactory accuracy.

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A Self‐Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone‐Beam CT Imaging

Ding

et al. 2023

Otolaryngol.--head neck surg.

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ObjectivePreoperative planning for otologic or neurotologic procedures often requires manual segmentation of relevant structures, which can be tedious and time‐consuming. Automated methods for segmenting multiple geometrically complex structures can not only streamline preoperative planning but also augment minimally invasive and/or robot‐assisted procedures in this space. This study evaluates a state‐of‐the‐art deep learning pipeline for semantic segmentation of temporal bone anatomy.Study DesignA descriptive study of a segmentation network.SettingAcademic institution.MethodsA total of 15 high‐resolution cone‐beam temporal bone computed tomography (CT) data sets were included in this study. All images were co‐registered, with relevant anatomical structures (eg, ossicles, inner ear, facial nerve, chorda tympani, bony labyrinth) manually segmented. Predicted segmentations from no new U‐Net (nnU‐Net), an open‐source 3‐dimensional semantic segmentation neural network, were compared against ground‐truth segmentations using modified Hausdorff distances (mHD) and Dice scores.ResultsFivefold cross‐validation with nnU‐Net between predicted and ground‐truth labels were as follows: malleus (mHD: 0.044 ± 0.024 mm, dice: 0.914 ± 0.035), incus (mHD: 0.051 ± 0.027 mm, dice: 0.916 ± 0.034), stapes (mHD: 0.147 ± 0.113 mm, dice: 0.560 ± 0.106), bony labyrinth (mHD: 0.038 ± 0.031 mm, dice: 0.952 ± 0.017), and facial nerve (mHD: 0.139 ± 0.072 mm, dice: 0.862 ± 0.039). Comparison against atlas‐based segmentation propagation showed significantly higher Dice scores for all structures (p < .05).ConclusionUsing an open‐source deep learning pipeline, we demonstrate consistently submillimeter accuracy for semantic CT segmentation of temporal bone anatomy compared to hand‐segmented labels. This pipeline has the potential to greatly improve preoperative planning workflows for a variety of otologic and neurotologic procedures and augment existing image guidance and robot‐assisted systems for the temporal bone.

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Toward an automatic preoperative pipeline for image-guided temporal bone surgery

Cited by 33 publications

References 24 publications

Retrospective in silico evaluation of optimized preoperative planning for temporal bone surgery

Retrospective in silico evaluation of optimized preoperative planning for temporal bone surgery

Image-guided cochlear access by non-invasive registration: a cadaveric feasibility study

A Self‐Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone‐Beam CT Imaging

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