Automated 3-D PDM construction from segmented images using deformable models

Kaus, Michael R.; Pekar, Vladimir; Lorenz, Cristian; Truyen, Roel; Lobregt, S.; Weese, Jürgen

doi:10.1109/tmi.2003.815864

Cited by 104 publications

(84 citation statements)

References 15 publications

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“…Unfortunately, such models are not directly available from standard segmentation algorithms (such as the Marching Cubes procedure [24]) since these include inside and outside information into the meshes. One solution is the use of statistically trained deformable models (ASM), in which a mean shape of the bone outer surface is deformed characteristically to best fit the patient CT data [20][21][22]27]. Using leaveone-out experiments, these studies typically report mean 3D surface deviations of 1.5 mm (max: 3 mm) [22] and 1.79 mm (max: 5.75 mm) [21] for the proximal femur.…”

Section: Discussionmentioning

confidence: 99%

“…This is fully understandable in the case of in-vivo imaging of brain or other soft tissues, where opening up the cadaver for dissection and measurement causes a collapse of the soft tissue target structure. A possible solution is to perform relative comparisons, e.g., by comparing with another, clinically established, imaging technique [15]; by performing leave-one-out experiments when working with a statistical database approach and training set for testing the accuracy of deformable models data [20][21][22]; or by checking coherency within the same data set [23].…”

Section: Introductionmentioning

confidence: 99%

“…Pinsky et al performed 3D measurements for small simulated cylindrical defects in an acrylic mandible model (64), and on a dry mandible bone (21). Multiple examiners assessed the physical sample dimensions twice, while one examiner performed manual software measurements with the same frequency [16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accuracy assessment of CT-based outer surface femur meshes

Gelaude

Sloten

Lauwers

2008

Computer Aided Surgery

100

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Objectives: Computer-aided bone surgery planning and implant design applications require accurate and compact representations of the patient's bone. The accuracy of bone segmentation from medical images has been studied extensively, with each study using a specific ground truth and a specific type and number of accuracy measurements. However, for convenience and practical reasons these three specifications have always been limited. The goal of this study is to thoroughly assess the absolute 3D accuracy of CT-based bone outer surface meshes, using femora as the examples. Materials and Methods: Using dense and very accurate optical surface scans of 15 dried femora as an absolute ground truth, this paper reports on the absolute 3D geometric accuracy of triangulated bone outer surface meshes, which were segmented from the CT scans of the corresponding formalin-fixed intact cadaver specimens using the author's previously presented contour-based segmentation algorithm on the one hand, and the commercially available Mimics Õ software (Materialise N.V., Leuven, Belgium) on the other. The study incorporates the effect of soft tissue presence on hard tissue segmentation and simultaneously reveals the accuracy shift introduced as a result of boiling the cadaver bones by processing extra CT scans of the dried bones. Results: The presented study demonstrates that, when using the optimal parameter settings for the respective segmentation procedures, sub-voxel mesh accuracies can be attained. Compact surface representations of femora can be generated with mean absolute accuracies of up to one fifth of the voxel size and Root Mean Square (RMS) error of half the voxel size. Conclusions: The 3D accuracy of the contour-based segmentation previously presented by the author makes it most suitable for generating outer bone surface meshes for use in the aforementioned applications. The optimal parameter settings for this segmentation procedure have been identified. For the Mimics Õ bone surface meshes, a single, but excellent, pre-defined set of parameters was identified.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy assessment of CT-based outer surface femur meshes

Gelaude

Sloten

Lauwers

2008

Computer Aided Surgery

100

View full text Add to dashboard Cite

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“…The model is automatically generated from a set of L segmented images [6]. First, a shape template is generated by triangulation of an arbitrarily selected image, which is adapted to each of the remaining images by rigid and non-rigid adaptation (see above).…”

Section: Integration Of Prior Knowledgementioning

confidence: 99%

“…Earlier work by this group proposed shape constrained deformable models, enabling local deviation from a statistical shape model by embedding it into a 3D triangular deformable mesh [6,11]. This approach used deterministic feature search and was successfully applied to segment bones in CT.…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of the Left Ventricle in Cardiac MRI

Kaus¹,

Berg²,

Niessen

et al. 2003

Lecture Notes in Computer Science

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Abstract. We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model. Inter-individual shape variation is represented by a statistical point distribution model, and the spatial relationship of the epi-and endocardium is modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature model is established by classification of grey value surface profiles. Quantitative validation of 60 end-diastolic 3D MRI datasets demonstrates accuracy and robustness, with 1.28±0.81 mm mean deviation from manual segmentation. We investigate the extension to 4D by incorporating a constraint on the allowed deformation based on a learned example and show illustrative results for 4D MRI.

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Three‐dimensional surface deformation‐based shape analysis of hippocampus and caudate nucleus in children with fetal alcohol spectrum disorders

Joseph

Warton

Jacobson

et al. 2012

Human Brain Mapping

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Surface deformation-based analysis was used to assess local shape variations in the hippocampi and caudate nuclei of children with fetal alcohol spectrum disorders. High-resolution structural magnetic resonance imaging images were acquired for 31 children (19 controls and 12 children diagnosed with fetal alcohol syndrome/partial FAS). Hippocampi and caudate nuclei were manually segmented, and surface meshes were reconstructed. An iterative closest point algorithm was used to register the template of one control subject to all other shapes in order to capture the true geometry of the shape with a fixed number of landmark points. A point distribution model was used to quantify the shape variations in terms of a change in co-ordinate positions. Using the localized Hotelling T(2) method, regions of significant shape variations between the control and exposed subjects were identified and mapped onto the mean shapes. Binary masks of hippocampi and caudate nuclei were generated from the segmented volumes of each brain. These were used to compute the volumes and for further statistical analysis. The Mann-Whitney test was performed to predict volume differences between the groups. Although the exposed and control subjects did not differ significantly in their volumes, the shape analysis showed the hippocampus to be more deformed at the head and tail regions in the alcohol-exposed children. Between-group differences in caudate nucleus morphology were dispersed across the tail and head regions. Correlation analysis showed associations between the degree of compression and the level of alcohol exposure. These findings demonstrate that shape analysis using three-dimensional surface measures is sensitive to fetal alcohol exposure and provides additional information than volumetric measures alone.

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Automated 3-D PDM construction from segmented images using deformable models

Cited by 104 publications

References 15 publications

Accuracy assessment of CT-based outer surface femur meshes

Accuracy assessment of CT-based outer surface femur meshes

Automated Segmentation of the Left Ventricle in Cardiac MRI

Three‐dimensional surface deformation‐based shape analysis of hippocampus and caudate nucleus in children with fetal alcohol spectrum disorders

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