Medial axis segmentation of cranial nerves using shape statistics-aware discrete deformable models.

Autor: Sultana S; Department of Modeling, Simulation and Visualization Engineering, Old Dominion University, Norfolk, USA., Agrawal P; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA., Elhabian S; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA., Whitaker R; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA., Blatt JE; Department of Neurosurgery, University of Florida, Gainesville, USA., Gilles B; Montpellier Laboratory of Informatics, Robotics, and Microelectronics (LIRMM), Montpellier, France., Cetas J; Department of Neurosurgery, Oregon Health and Science University, Portland, USA., Rashid T; Department of Modeling, Simulation and Visualization Engineering, Old Dominion University, Norfolk, USA., Audette MA; Department of Modeling, Simulation and Visualization Engineering, Old Dominion University, Norfolk, USA. maudette@odu.edu.
Jazyk: angličtina
Zdroj: International journal of computer assisted radiology and surgery [Int J Comput Assist Radiol Surg] 2019 Nov; Vol. 14 (11), pp. 1955-1967. Date of Electronic Publication: 2019 Jun 24.
DOI: 10.1007/s11548-019-02014-z
Abstrakt: Purpose: We propose a segmentation methodology for brainstem cranial nerves using statistical shape model (SSM)-based deformable 3D contours from T 2 MR images.
Methods: We create shape models for ten pairs of cranial nerves. High-resolution T 2 MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model. These segmented centerlines comprise training datasets for the shape model. Point correspondence for the training dataset is performed using an entropy-based energy minimization framework applied to particles located on the centerline curve. The shape information is incorporated into the 1-Simplex model by introducing a shape-based internal force, making the deformation stable against low resolution and image artifacts.
Results: The proposed method is validated through extensive experiments using both synthetic and patient MRI data. The robustness and stability of the proposed method are experimented using synthetic datasets. SSMs are constructed independently for ten pairs (CNIII-CNXII) of brainstem cranial nerves using ten non-pathological image datasets of the brainstem. The constructed ten SSMs are assessed in terms of compactness, specificity and generality. In order to quantify the error distances between segmented results and ground truths, two metrics are used: mean absolute shape distance (MASD) and Hausdorff distance (HD). MASD error using the proposed shape model is 0.19 ± 0.13 (mean ± std. deviation) mm and HD is 0.21 mm which are sub-voxel accuracy given the input image resolution.
Conclusion: This paper described a probabilistic digital atlas of the ten brainstem-attached cranial nerve pairs by incorporating a statistical shape model with the 1-Simplex deformable contour. The integration of shape information as a priori knowledge results in robust and accurate centerline segmentations from even low-resolution MRI data, which is essential in neurosurgical planning and simulations for accurate and robust 3D patient-specific models of critical tissues including cranial nerves.
Databáze: MEDLINE
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