Nugget-Cut: A Segmentation Scheme for Spherically- and Elliptically-Shaped 3D Objects

Egger, Jan; Bauer, Mathias; Kuhnt, Daniela; Carl, Barbara; Kappus, Christoph; Freisleben, Bernd; Nimsky, Christopher

doi:10.1007/978-3-642-15986-2_38

Cited by 38 publications

(39 citation statements)

References 19 publications

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“…We have compared our algorithm with a single-step approach, similar to [3], [4], by initializing the Round Cut algorithm with high resolution, but without refinement. In our comparison we made sure that the number of vertices in both resulting meshes is the same.…”

Section: Resultsmentioning

confidence: 99%

“…Instead, a mesh defining a more regular tiling of a sphere should be used as a base surface in this case. In [3] and [4] a polyhedron to set up a graph and surface is detected in a single optimization step.…”

Section: B Mesh Based Surface Searchmentioning

confidence: 99%

“…The resulting number of mesh vertices is V = V 0 + F0 2 (ν + 1)(ν − 1), and even though V grows quadratically with ν, this allows us to choose suitable polyhedra resolution, unlike e.g. [3], where only two resolution settings are used.…”

Section: B Mesh Based Surface Searchmentioning

confidence: 99%

“…To detect spherical objects a search can be performed along lines from a single seed point through the vertices of a regular polyhedron. Such an approach, suggested by Egger et al [3] and Wang and Beichel [4], allows for finding boundaries of objects in the so-called star domain which might be sufficient in many applications [5].…”

Section: Introductionmentioning

confidence: 99%

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Surface Detection Using Round Cut

Dahl¹,

Dahl²,

Larsen³

2014

2014 2nd International Conference on 3D Vision

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Abstract-We propose an iterative method for detecting closed surfaces in a volumetric data, where an optimal search is performed in a graph build upon a triangular mesh. Our approach is based on previous techniques for detecting an optimal terrain-like or tubular surface employing a regular grid. Unlike similar adaptations for triangle meshes, our method is capable of capturing complex geometries by iteratively refining the surface, where we obtain a high level of robustness by applying explicit mesh processing to intermediate results. Our method uses on-surface data support, but it also exploits data information about the region inside and outside the surface. This provides additional robustness to the algorithm. We demonstrate the capabilities of the approach by detecting surfaces of CT scanned objects.

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Section: Resultsmentioning

confidence: 99%

Section: B Mesh Based Surface Searchmentioning

confidence: 99%

Section: B Mesh Based Surface Searchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surface Detection Using Round Cut

Dahl¹,

Dahl²,

Larsen³

2014

2014 2nd International Conference on 3D Vision

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“…The algorithm presented here belongs to the class of graph-based approaches [9][10][11][12][13] , where an image is interpreted as a graph…”

Section: Methodsmentioning

confidence: 99%

US-Cut: interactive algorithm for rapid detection and segmentation of liver tumors in ultrasound acquisitions

Egger¹,

Voglreiter²,

Dokter³

et al. 2016

SPIE Proceedings

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Ultrasound (US) is the most commonly used liver imaging modality worldwide. It plays an important role in follow-up of cancer patients with liver metastases. We present an interactive segmentation approach for liver tumors in US acquisitions. Due to the low image quality and the low contrast between the tumors and the surrounding tissue in US images, the segmentation is very challenging. Thus, the clinical practice still relies on manual measurement and outlining of the tumors in the US images. We target this problem by applying an interactive segmentation algorithm to the US data, allowing the user to get real-time feedback of the segmentation results. The algorithm has been developed and tested hand-in-hand by physicians and computer scientists to make sure a future practical usage in a clinical setting is feasible. To cover typical acquisitions from the clinical routine, the approach has been evaluated with dozens of datasets where the tumors are hyperechoic (brighter), hypoechoic (darker) or isoechoic (similar) in comparison to the surrounding liver tissue. Due to the interactive real-time behavior of the approach, it was possible even in difficult cases to find satisfying segmentations of the tumors within seconds and without parameter settings, and the average tumor deviation was only 1.4mm compared with manual measurements. However, the long term goal is to ease the volumetric acquisition of liver tumors in order to evaluate for treatment response. Additional aim is the registration of intraoperative US images via the interactive segmentations to the patient's pre-interventional CT acquisitions.

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