Segmentation of risk structures for otologic surgery using the Probabilistic Active Shape Model (PASM)

Becker, Meike; Kirschner, Matthias; Sakas, Georgios

doi:10.1117/12.2043411

Cited by 10 publications

(11 citation statements)

References 10 publications

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“…Code of methods and experiments will be made publicly available on GitHub. 1 Experiment Setup For each patient, we created surface models of the different structures from the expert annotations. In these environments, we manually placed start states q I at the skull's surface and goal states q G at the round window of the cochlea as well as directly posterior and inferior to the IAC.…”

Section: Resultsmentioning

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: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…The realworld medical image data were extracted from CT scans. The liver segmentation algorithm is [15] and the segmentation of the other datasets was done using [2].…”

Section: Data Descriptionmentioning

confidence: 99%

Extended surface distance for local evaluation of 3D medical image segmentations

2015

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The evaluation of 3D medical image segmentation quality requires a reliable detailed comparison of a reference segmentation with an automatic segmentation. It should be able to measure the quality accurately and, thus, to reveal problematic regions. While several (global) measures, providing a single quality value, are available, the only widely used local measure is the Surface Distance (i.e., point-to-surface distance). This measure, however, has significant drawbacks such as asymmetry and underestimation in distant and differently formed regions. Other available measures have limited suitability for 3D medical segmentation evaluation. We present a more reliable distance measure for assessing and analyzing local differences between automatic and reference (i.e., ground truth) 3D segmentations. We identify and overcome Surface Distance drawbacks, esp. in regions with larger dissimilarities. We evaluated our approach on four real medical image datasets. The results indicate that our measure provides more accurate local distance values

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“…All instances have a manually created ground truth segmentation. Automatic segmentations have been generated using three variations of the SSM-based algorithm by Becker et al [1].…”

Section: Applicationmentioning

confidence: 99%

Visual Comparison of 3D Medical Image Segmentation Algorithms Based on Statistical Shape Models

Geurts

Sakas

Kuijper

et al. 2015

Lecture Notes in Computer Science

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Abstract. 3D medical image segmentation is needed for diagnosis and treatment. As manual segmentation is very costly, automatic segmentation algorithms are needed. For finding best algorithms, several algorithms need to be evaluated on a set of organ instances. This is currently difficult due to dataset size and complexity. In this paper, we present a novel method for comparison and evaluation of several algorithms that automatically segment 3D medical images. It combines algorithmic data analysis with interactive data visualization. A clustering algorithm identifies regions of common quality across the segmented data set for each algorithm. The comparison identifies best algorithms per region. Interactive views show the algorithm quality. We applied our approach to a real-world cochlea dataset, which was segmented with several algorithms. Our approach allowed segmentation experts to compare algorithms on regional level and to identify best algorithms per region.

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Segmentation of risk structures for otologic surgery using the Probabilistic Active Shape Model (PASM)

Cited by 10 publications

References 10 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

Extended surface distance for local evaluation of 3D medical image segmentations

Visual Comparison of 3D Medical Image Segmentation Algorithms Based on Statistical Shape Models

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