Correspondence Evaluation in Local Shape Analysis and Structural Subdivision

Styner, Martin; Xu, Shun; El-Sayed, Mohammed; Gerig, Guido

doi:10.1109/isbi.2007.357071

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…No significant di↵erences on either shape were found after FDR correction. The raw p-values, however, suggest that both structures may exhibit group di↵erences in the tail and that the right caudate contains more group di↵erences than the left, an observation that agrees with results given in [98], [74], [100], and [81]. …”

Section: Shape Analysis Of Neuroanatomical Structuressupporting

confidence: 90%

“…Di↵erences in the tail, especially on the right structure were also observed by Styner et al in [96]. These results also correlate with those reported for the spherical harmonics method (SPHARM) [100] and spherical wavelet analysis [81].…”

Section: Shape Analysis Of Neuroanatomical Structuressupporting

confidence: 88%

“…The first study addresses the shape of the hippocampus in patients with schizophrenia. The data consist of left and right hippocampus shapes from 56 male adult patients versus 26 healthy adult male controls, segmented from MRI using a template-based semi-automated method [100]. The second study addresses the shape of the caudate in males with schizo-typal personality disorder.…”

Section: Shape Analysis Of Neuroanatomical Structuresmentioning

confidence: 99%

“…The null hypothesis for these tests is that the distributions of the locations of corresponding sample points are the same regardless of group. The point-wise hypothesis test was chosen for this analysis, because it has also been applied to several other published studies of this data [100,81] and thus provides a basis for comparison of the results. The left hippocampus appears to exhibit few group di↵erences, with none detected after FDR correction.…”

Section: Shape Analysis Of Neuroanatomical Structuresmentioning

confidence: 99%

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Shape Modeling and Analysis with Entropy-Based Particle Systems

Cates

Fletcher

Styner

et al. 2007

Lecture Notes in Computer Science

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Many important fields of basic research in medicine and biology routinely employ tools for the statistical analysis of collections of similar shapes. Biologists, for example, have long relied on homologous, anatomical landmarks as shape models to characterize the growth and development of species. Increasingly, however, researchers are exploring the use of more detailed models that are derived computationally from three-dimensional images and surface descriptions. While computationally-derived models of shape are promising new tools for biomedical research, they also present some significant engineering challenges, which existing modeling methods have only begun to address.In this dissertation, I propose a new computational framework for statistical shape modeling that significantly advances the state-of-the-art by overcoming many of the limitations of existing methods. The framework uses a particle-system representation of shape, with a fast correspondence-point optimization based on information content. The optimization balances the simplicity of the model (compactness) with the accuracy of the shape representations by using two commensurate entropy metrics and no free parameters. The idea is to maximize both the geometric accuracy and the statistical simplicity of the shape model, in accordance with the principle of parsimony in model selection. The nonparametric representation allows the method to be applied to a larger class of problems than existing methods, including nonspherical surfaces, open surfaces, and sets of multiple surfaces.The relative simplicity of the surface representation and the low number of free parameters results in a framework that is easy to use and can operate directly on image segmentations. In collaboration with scientists from several important areas of biomedicine, I have demonstrated that the proposed method is indeed an e↵ective tool for scientific research.The specific research contributions of this dissertation are as follows. First, I describe a mathematical framework and a robust numerical algorithm for computing optimized correspondence-point shape models using an entropy-based optimization and particle-system technology. Second, I develop a series of extensions of the framework to more general classes of shape analysis problems, including the analysis of multiple-object complexes, the generalization to correspondence based on generic functions of position, an extension to handle surfaces with open boundaries, and shape modeling with simple regression. Third, I describe the application of statistical hypothesis testing, regression analysis, and multiple-analysis of covariance to the proposed shape models. I also introduce new techniques for visualization and interpretation of these statistics. Finally, in cooperation with biomedical researchers, I present validation of the above research contributions by their successful application to real-world research problems.

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Section: Shape Analysis Of Neuroanatomical Structuressupporting

confidence: 90%

Section: Shape Analysis Of Neuroanatomical Structuressupporting

confidence: 88%

Section: Shape Analysis Of Neuroanatomical Structuresmentioning

confidence: 99%

Section: Shape Analysis Of Neuroanatomical Structuresmentioning

confidence: 99%

See 2 more Smart Citations

Shape Modeling and Analysis with Entropy-Based Particle Systems

Cates

Fletcher

Styner

et al. 2007

Lecture Notes in Computer Science

Self Cite

139

224

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“…In addition to these three theoretical measures, it is possible to evaluate the segmentation accuracy of the model on test data. The first evaluations of various methods of correspondence analysis of the shape are shown in the publication Styner [9]. Due to the very difficult evaluation of selected algorithms and the quality of the achieved results, we propose a new segmentation procedure possible, which should provide very reliable results.…”

Section: Segmentation Medical Image Datamentioning

confidence: 99%

MedIDEA — Medical image data extraction and analysis

Kratochvil

Vcelak

Kleckova

et al. 2012

2012 5th International Conference on BioMedical Engineering and Informatics

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The most indicated imaging technique before operations is computed tomography. Preoperative deliberation requires not only its own evaluation of pathological findings, but also the overall anatomy, perfusion and volume of various anatomical parts, which must be accurately identified. There is a wide range for computer-assisted diagnosis, which should reduce and refine the assessment carried out by man and help better decide on the appropriate therapeutic procedure (resection, multiphase power, chemotherapy, ablation methods), and, among other things, reduce the number of unnecessary surgical procedures while on the other hand enabling the implementation of extensive resection. We aims to create a comprehensive system for computed imaging, which would allow the detection and analysis of focal lesions, determination of their segmental localisation, assessment of perfusion conditions and performance of a virtual resection including the establishment of volume resected and organ remaining. Such a system is not yet commercially available.

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Shape analysis, a field in need of careful validation

Gao

Riklin-Raviv

Bouix

2014

Human Brain Mapping

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In the last two decades, the statistical analysis of shape has become an actively studied field and finds applications in a wide range of areas. In addition to algorithmic development, many researchers have distributed end-user orientated toolboxes, which further enable the utilization of the algorithms in an "off the shelf" fashion. However, there is little work on the evaluation and validation of these techniques, which poses a rather serious challenge when interpreting their results. To address this lack of validation, we design a validation framework and then use it to test some of the most widely used toolboxes. Our initial results show inconsistencies and disagreement among four different methods. We believe this type of analysis to be critical not only for the community of algorithm designers but also perhaps more importantly to researchers who use these tools without knowing the algorithm details and seek objective criteria for tool selection.

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Correspondence Evaluation in Local Shape Analysis and Structural Subdivision

Cited by 12 publications

References 14 publications

Shape Modeling and Analysis with Entropy-Based Particle Systems

Shape Modeling and Analysis with Entropy-Based Particle Systems

MedIDEA — Medical image data extraction and analysis

Shape analysis, a field in need of careful validation

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Correspondence Evaluation in Local Shape Analysis and Structural Subdivision

Cited by 12 publications

References 14 publications

Shape Modeling and Analysis with Entropy-Based Particle Systems

Shape Modeling and Analysis with Entropy-Based Particle Systems

MedIDEA &#x2014; Medical image data extraction and analysis

Shape analysis, a field in need of careful validation

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MedIDEA — Medical image data extraction and analysis