Multilevel statistical shape models: A new framework for modeling hierarchical structures

Lecron, Fabian; Boisvert, Jonathan; Benjelloun, M.; Mahmoudi, Saïd

doi:10.1109/isbi.2012.6235797

Cited by 16 publications

(30 citation statements)

References 7 publications

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“…A previous application of mPCA to form ASMs related to the segmentation of the human spine [9]. The results of this study showed that mPCA offers more flexibility and allows deformations that classical statistical models cannot generate.…”

Section: Fig 1 Flowchart Illustrating the "Nested" Nature Of Multilmentioning

confidence: 78%

“…This approach also retains the desirable feature that any segmentation can still be constrained so that a fit of the model never "strays too far" from the training set used in forming the model (described in the methods section below). A previous application of mPCA to form ASMs related to the segmentation of the human spine [9]. The results of this study showed that mPCA offers more flexibility and allows deformations that classical statistical models cannot generate.…”

Section: Introductionmentioning

confidence: 78%

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Initial Results of Multilevel Principal Components Analysis of Facial Shape

Farnell

Galloway

Zhurov

et al. 2017

Communications in Computer and Information Science

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Abstract. Traditionally, active shape models (ASMs) do not make a distinction between groups in the subject population and they rely on methods such as (single-level) principal components analysis (PCA). Multilevel principal components analysis (PCA) allows one to model betweengroup effects and within-group effects explicitly. Three dimensional (3D) laser scans were taken from 240 subjects (38 Croatian female, 35 Croatian male, 40 English female, 40 English male, 23 Welsh female, 27 Welsh male, 23 Finnish female, and 24 Finnish male) and 21 landmark points were created subsequently for each scan. After Procrustes transformation, eigenvalues from mPCA and from single-level PCA based on these points were examined. mPCA indicated that the first two eigenvalues of largest magnitude related to within-groups components, but that the next largest eigenvalue related to between-groups components. Eigenvalues from single-level PCA always had a larger magnitude than either within-group or between-group eigenvectors at equivalent eigenvalue number. An examination of the first mode of variation indicated possible mixing of between-group and within-group effects in single-level PCA. Component scores for mPCA indicated clustering with country and gender for the between-groups components (as expected), but not for the within-group terms (also as expected). Clustering of component scores for single-level PCA was harder to resolve. In conclusion, mPCA is viable method of forming shape models that offers distinct advantages over single-level PCA when groups occur naturally in the subject population.Keywords: multilevel principal components analysis; active shape models; facial shape IntroductionActive shape models (ASMs) and active appearance models (AAMs) [1][2][3][4][5][6][7][8] are common techniques in image processing that are used to search for specific features or shapes in images. However, if clustering or multilevel data structures exist naturally in the data set, e.g., as illustrated by the flowchart in Fig. 1, the eigenvectors and eigenvalues from principal components analysis (PCA) will only be partially reflective of the true variation in the set of images / shapes. Multilevel principal components analysis (mPCA) provides a convenient method of modelling both the underlying structures within the images and also any groupings between images. mPCA carries out PCA at both withingroup and between-group levels independently. Note that the within-group level might be thought of as being "nested" within the broader between-group level, e.g., as shown in Fig. 1 for human facial expression. This approach also retains the desirable feature that any segmentation can still be constrained so that a fit of the model never "strays too far" from the training set used in forming the model (described in the methods section below). A previous application of mPCA to form ASMs related to the segmentation of the human spine [9]. The results of this study showed that mPCA offers more flexibility and allows deformations that classical statist...

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Section: Fig 1 Flowchart Illustrating the "Nested" Nature Of Multilmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 78%

Initial Results of Multilevel Principal Components Analysis of Facial Shape

Farnell

Galloway

Zhurov

et al. 2017

Communications in Computer and Information Science

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“…Aiming to overcome the inherent limitations of global models, Lecron et al (Lecron et al, 2012a) proposed the use of multi-level component analysis (MLCA), a generalization of the popular PCA for analyzing multi-group or multi-set data (Timmerman, 2006) (i.e., data that can be divided into conceptually meaningful blocks). Unlike classic PCA, MLCA creates different sub-models for different blocks of information, allowing to analyze the within-block (i.e., localities) and between-block (i.e., global changes) variation separately.…”

Section: Nested Statistical Modelsmentioning

confidence: 99%

“…Unlike classic PCA, MLCA creates different sub-models for different blocks of information, allowing to analyze the within-block (i.e., localities) and between-block (i.e., global changes) variation separately. The flexibility of this multi-level-based model has been explored by several authors to analyze the vertebral body (Lecron et al, 2012a(Lecron et al, , 2012bNeubert et al, 2014), where each vertebra represents a block of information. The potential of MLCA to model multi-organ structures was also used by Lee et al (Lee et al, 2016) to create a hybrid multi-object model-based multi-atlas segmentation method for rodent brains.…”

Section: Nested Statistical Modelsmentioning

confidence: 99%

Computational anatomy for multi-organ analysis in medical imaging: A review

Cerrolaza

Picazo

Humbert³

et al. 2019

Medical Image Analysis

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The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of medical imaging applications on the future of healthcare.

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“…Let us assume a sample of I patients characterized by K vertebrae. In [7], the authors proposed a multilevel modelization of the vertebrae. Here, we develop a deformable model that can represent all the spine.…”

Section: Multilevel Statistical Shape Modelmentioning

confidence: 99%

Fast 3D Spine Reconstruction of Postoperative Patients Using a Multilevel Statistical Model

Lecron

Boisvert

Mahmoudi

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. NRC Publications Record / Notice d'Archives des publications de CNRC:http://nparc.cisti-icist.nrc-cnrc.gc.ca/eng/view/object/?id=94a1e287-477c-46f4-b248-71162647f8c9 http://nparc.cisti-icist.nrc-cnrc.gc.ca/fra/voir/objet/?id=94a1e287-477c-46f4-b248-71162647f8c9 Abstract. Severe cases of spinal deformities such as scoliosis are usually treated by a surgery where instrumentation (hooks, screws and rods) is installed to the spine to correct deformities. Even if the purpose is to obtain a normal spine curve, the result is often straighter than normal. In this paper, we propose a fast statistical reconstruction algorithm based on a general model which can deal with such instrumented spines. To this end, we present the concept of multilevel statistical model where the data are decomposed into a within-group and a between-group component. The reconstruction procedure is formulated as a second-order cone program which can be solved very fast (few tenths of a second). Reconstruction errors were evaluated on real patient data and results showed that multilevel modeling allows better 3D reconstruction than classical models.

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Multilevel statistical shape models: A new framework for modeling hierarchical structures

Cited by 16 publications

References 7 publications

Initial Results of Multilevel Principal Components Analysis of Facial Shape

Initial Results of Multilevel Principal Components Analysis of Facial Shape

Computational anatomy for multi-organ analysis in medical imaging: A review

Fast 3D Spine Reconstruction of Postoperative Patients Using a Multilevel Statistical Model

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