Sparse Statistical Deformation Model for the Analysis of Craniofacial Malformations in the Crouzon Mouse

Ólafsdóttir, Hildur; Hansen, Michael Sass; Sjöstrand, Karl; Darvann, Tron A.; Hermann, Nuno V.; Oubel, Estanislao; Ersbøll, Bjarne Kjær; Larsen, Rasmus; Frangi, Alejandro F.; Larsen, Per; Perlyn, Chad A.; Morriss‐Kay, Gillian M.

doi:10.1007/978-3-540-73040-8_12

Cited by 4 publications

(3 citation statements)

References 17 publications

(25 reference statements)

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“…Independent Component Analysis (ICA) and Sparse Principal Component Analysis (SPCA) were then evaluated with the aim of distributing the discriminating variation over more modes [26]. ICA and SPCA had similar performance and discovered up to six deformation modes.…”

Section: Deformation-based Morphologymentioning

confidence: 99%

A New Landmark-Independent Tool for Quantifying and Characterizing Morphologic Variation

Rolfe

Cox

Shapiro

et al. 2014

Lecture Notes in Computer Science

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A New Landmark-Independent Tool for Quantifying and Characterizing Morphologic Variation Sara RolfeChair of the Supervisory Committee:Title of Chair Linda Shapiro Department of ChairAs advances in multi-dimensional imaging are made, there is a growing need for tools that enable quantitative assessment of both embryonic and postnatal structure and form. To this end, this dissertation develops a landmark-independent, deformable-registration based framework for quantifying and characterizing morphologic variation. This framework can utilize 3D surface images generated by any multi-dimensional biomedical imaging modality and provides quantitative assessment of potentially biologically relevant changes in 3D shape whether natural, caused by mutation, or influenced by non-genetic factors. In this work, using different example datasets, the utility and sensitivity of the tools developed in this work are demonstrated: 1) to detect subtle morphological phenotypes in specimens; 2) to quantify morphologic change over a normal developmental time series of embryonic development; and 3) to determine the impact of transient localized genetic manipulation and environmental change on morphogenesis. The development of these tools will assist in defining trajectories of normal growth and help underpin hypotheses as to the developmental basis and the specific role of the causative gene in conditions causing disruptions to normal growth.

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Section: Deformation-based Morphologymentioning

confidence: 99%

A New Landmark-Independent Tool for Quantifying and Characterizing Morphologic Variation

Rolfe

Cox

Shapiro

et al. 2014

Lecture Notes in Computer Science

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“…By including more training subjects into the atlas, inter-subject variance is inherently included. Multi-subject atlases have already gained success in human image registration [21], [22] and mouse brain and kidney image analysis [23]–[25]. Based on our survey, no study has used a multi-subject atlas for the estimation of multiple mouse trunk organs.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Mouse Organ Locations Through Registration of a Statistical Mouse Atlas With Micro-CT Images

Wang

Stout²,

Chatziioannou³

2012

IEEE Trans. Med. Imaging

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Micro-CT is widely used in preclinical studies of small animals. Due to the low soft-tissue contrast in typical studies, segmentation of soft tissue organs from noncontrast enhanced micro-CT images is a challenging problem. Here, we propose an atlas-based approach for estimating the major organs in mouse micro-CT images. A statistical atlas of major trunk organs was constructed based on 45 training subjects. The statistical shape model technique was used to include inter-subject anatomical variations. The shape correlations between different organs were described using a conditional Gaussian model. For registration, first the high-contrast organs in micro-CT images were registered by fitting the statistical shape model, while the low-contrast organs were subsequently estimated from the high-contrast organs using the conditional Gaussian model. The registration accuracy was validated based on 23 noncontrast-enhanced and 45 contrast-enhanced micro-CT images. Three different accuracy metrics (Dice coefficient, organ volume recovery coefficient, and surface distance) were used for evaluation. The Dice coefficients vary from 0.45 ± 0.18 for the spleen to 0.90 ± 0.02 for the lungs, the volume recovery coefficients vary from for the liver to 1.30 ± 0.75 for the spleen, the surface distances vary from 0.18 ± 0.01 mm for the lungs to 0.72 ± 0.42 mm for the spleen. The registration accuracy of the statistical atlas was compared with two publicly available single-subject mouse atlases, i.e., the MOBY phantom and the DIGIMOUSE atlas, and the results proved that the statistical atlas is more accurate than the single atlases. To evaluate the influence of the training subject size, different numbers of training subjects were used for atlas construction and registration. The results showed an improvement of the registration accuracy when more training subjects were used for the atlas construction. The statistical atlas-based registration was also compared with the thin-plate spline based deformable registration, commonly used in mouse atlas registration. The results revealed that the statistical atlas has the advantage of improving the estimation of low-contrast organs.

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“…The term and concept of SDMs are due to Rueckert et al [3]. SDMs have been applied directly to registration by optimization in the space of principal components [6,7,4], for the automatic construction of shape models [8,9] and for interpretation [10].…”

Section: Introductionmentioning

confidence: 99%

Statistical Regularization of Deformation Fields for Atlas-Based Segmentation of Bone Scintigraphy Images

Sjöstrand¹,

Ohlsson²,

Edenbrandt³

2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. The construction and application of statistical models of deformations based on non-rigid image registration methods have gained recent popularity. This paper presents the application of such a model to restricting a general-purpose registration algorithm to anatomically plausible solutions. Specifically, the Morphon registration method is used for atlas-based segmentation of bone scintigraphy images. From a training set of 734 images, a model of characteristic deformation fields is built and used for regularizing the registration of 113 test images. Results show that around 300 training images and 30 principal modes are sufficient for building a useful model. The segmentation succeeded in 106 of 113 test images.

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Sparse Statistical Deformation Model for the Analysis of Craniofacial Malformations in the Crouzon Mouse

Cited by 4 publications

References 17 publications

A New Landmark-Independent Tool for Quantifying and Characterizing Morphologic Variation

A New Landmark-Independent Tool for Quantifying and Characterizing Morphologic Variation

Estimation of Mouse Organ Locations Through Registration of a Statistical Mouse Atlas With Micro-CT Images

Statistical Regularization of Deformation Fields for Atlas-Based Segmentation of Bone Scintigraphy Images

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