Interactive, GPU-Based Level Sets for 3D Segmentation

Lefohn, Aaron; Cates, Joshua; Whitaker, Ross T.

doi:10.1007/978-3-540-39899-8_70

Cited by 113 publications

(103 citation statements)

References 11 publications

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“…These tools carry a steep learning curve, due the large number of features that they provide. More specific tools include GIST (Lefohn et al, 2003), which has a very fast level set implementation but a limited user interface. In contrast, SNAP is both easy to learn, since it is streamlined towards one specific task, and powerful, including a full set of complimentary editing tools and a user interface that provides live feedback mechanisms intended to make parameter selection easier for non-expert users.…”

Section: Previous Workmentioning

confidence: 99%

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Yushkevich¹,

Piven²,

Hazlett³

et al. 2006

NeuroImage

7,361

4,931

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Active contour segmentation and its robust implementation using level set methods are well established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intra/interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing.Analogous results for lateral ventricle segmentation are provided.

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Section: Previous Workmentioning

confidence: 99%

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Yushkevich¹,

Piven²,

Hazlett³

et al. 2006

NeuroImage

7,361

4,931

View full text Add to dashboard Cite

show abstract

“…However, the current process lends itself to parallelism. The advent of cheap, specialized, stream-processing hardware promises significantly faster implementations when the inherent parallelism in the process is exploited [43], [41], [42]. Multithreading could also be utilized to exploit the parallelism in the process.…”

Section: Resultsmentioning

confidence: 99%

“…We now demonstrate results of our method used in conjunction with an algorithm for level set surface segmentation from volumetric MRI data. For this purpose, we adopt the data term introduced in [41], [42]. This data term deforms the surface model in such a way that voxels falling between a low and a high intensity threshold will be contained in the interior of the surface.…”

Section: Surface Reconstruction From Measured Range Datamentioning

confidence: 99%

Higher-order nonlinear priors for surface reconstruction

Taşdizen

Whitaker

2004

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-For surface reconstruction problems with noisy and incomplete range data, a Bayesian estimation approach can improve the overall quality of the surfaces. The Bayesian approach to surface estimation relies on a likelihood term, which ties the surface estimate to the input data, and the prior, which ensures surface smoothness or continuity. This paper introduces a new high-order, nonlinear prior for surface reconstruction. The proposed prior can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion. An exact solution would require solving a fourth-order partial differential equation (PDE), which can be difficult with conventional numerical techniques. Our approach is to solve a cascade system of two second-order PDEs, which resembles the original fourth-order system. This strategy is based on the observation that the generalization of image processing to surfaces entails filtering the surface normals. We solve one PDE for processing the normals and one for refitting the surface to the normals. Furthermore, we implement the associated surface deformations using level sets. Hence, the algorithm can accommodate very complex shapes with arbitrary and changing topologies. This paper gives the mathematical formulation and describes the numerical algorithms. We also show results using range and medical data.

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“…Many tumor segmentation methods found in literature are not fully automatic as they need user interaction to place a seed inside the tumor or edema region [17], [8]. Region growing [19] based tumor detection techniques suffer high time complexity.…”

Section: Introductionmentioning

confidence: 99%