Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation

Rationale and Objectives-Manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. The authors have developed an automated system for brain tumor segmentation that provides objective, reproducible segmentations that are close to the manual results. Additionally, the method segments white matter, grey matter, cerebrospinal fluid, and edema. The segmentation of pathology and healthy structures is crucial for surgical planning and intervention.Material and Methods-The method performs the segmentation of a registered set of MR images using an Expectation-Maximization scheme. The segmentation is guided by a spatial probabilistic atlas that contains expert prior knowledge about brain structures. This atlas is modified with the subject specific brain tumor prior that is computed based on contrast enhancement. Conclusion-The automated method can be applied to different types of tumors. Although the semi-automated method generates results that have higher level of agreement with the manual raters, the automatic method has the advantage of requiring no user supervision. Results-Five

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“…The software used for the evaluation is VALMET (20). VALMET is used to generate two different classes of metrics for comparing two segmentations.…”

Section: Resultsmentioning

confidence: 99%

Automatic brain tumor segmentation by subject specific modification of atlas priors1

Prastawa¹,

Bullitt

Moon³

et al. 2003

Academic Radiology

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“…The distance between two surfaces is defined as the average of distances from all the points in one surface to the other surface [14]. Another quantitative measure is the volume of the lung field.…”

Section: Resultsmentioning

confidence: 99%

Online 4-D CT Estimation for Patient-Specific Respiratory Motion Based on Real-Time Breathing Signals

Xue

Xie

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. In image-guided lung intervention, the electromagnetic (EM) tracked needle can be visualized in a pre-procedural CT by registering the EM tracking and the CT coordinate systems. However, there exist discrepancies between the static pre-procedural CT and the patient due to respiratory motion. This paper proposes an online 4-D CT estimation approach to patient-specific respiratory motion compensation. First, the motion patterns between 4-D CT data and respiratory signals such as fiducials from a number of patients are trained in a template space after image registration. These motion patterns can be used to estimate the patient-specific serial CTs from a static 3-D CT and the real-time respiratory signals of that patient, who do not generally take 4-D CTs. Specifically, the respiratory lung field motion vectors are projected onto the Kernel Principal Component Analysis (K-PCA) space, and a motion estimation model is constructed to estimate the lung field motion from the fiducial motion using the ridge regression method based on the least squares support vector machine (LS-SVM). The algorithm can be performed onsite prior to the intervention to generate the serial CT images according to the respiratory signals in advance, and the estimated CTs can be visualized in real-time during the intervention. In experiments, we evaluated the algorithm using leave-one-out strategy on 30 4-D CT data, and the results showed that the average errors of the lung field surfaces are 1.63mm.

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“…For this reason a number of different metrics and benchmarks have been proposed over time (Gerig et al, 2008;Corsini et al, 2007;Benhabiles et al, 2009;Benhabiles et al, 2010). In this paper we chose to adopt the dataset and metrics proposed by Chen et al (2009).…”

Section: Experimental Validationmentioning

confidence: 99%

A graph-based technique for semi-supervised segmentation of 3D surfaces

Bergamasco

Albarelli

Torsello

2012

Pattern Recognition Letters

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a b s t r a c tA wide range of cheap and simple to use 3D scanning devices has recently been introduced in the market. These tools are no longer addressed to research labs and highly skilled professionals, but rather, they are mostly designed to allow inexperienced users to acquire surfaces and whole objects easily and independently. In this scenario, the demand for automatic or semi-automatic algorithms for 3D data processing is increasing. In this paper we address the task of segmenting the acquired surfaces into perceptually relevant parts. Such a problem is well known to be ill-defined both for 2D images and 3D objects, as even with a perfect understanding of the scene, many different and incompatible semantic or syntactic segmentations can exist together. For this reason recent years have seen a great research effort into semi-supervised approaches, that can make use of small bits of information provided by the user to attain better accuracy. We propose a semi-supervised procedure that exploits an initial set of seeds selected by the user. In our framework segmentation happens by propagating part labels over a weighted graph representation of the surface directly derived from its triangulated mesh. The assignment of each element is driven by a greedy approach that accounts for the curvature between adjacent triangles. The proposed technique does not require to perform edge detection or to fit parametrized surfaces and its implementation is very straightforward. Still, despite its simplicity, tests made on a standard database of scanned 3D objects show its effectiveness even with moderate user supervision.

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Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation

Cited by 275 publications

References 6 publications

Automatic brain tumor segmentation by subject specific modification of atlas priors1

Automatic brain tumor segmentation by subject specific modification of atlas priors1

Online 4-D CT Estimation for Patient-Specific Respiratory Motion Based on Real-Time Breathing Signals

A graph-based technique for semi-supervised segmentation of 3D surfaces

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