Statistical analysis of manual segmentations of structures in medical images

Kurtek, Sebastian; Su, Jingyong; Grimm, Cindy; Vaughan, Michelle; Sowell, Ross; Srivastava, Anuj

doi:10.1016/j.cviu.2012.11.014

Cited by 25 publications

(35 citation statements)

References 19 publications

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“…While we do not compare the proposed model to any other methods in current literature, we point out that our model is guaranteed to be compact due to its invariance to re-parameterization. This idea was shown for curves and surfaces in [9,11]. Specificity refers to the ability of a shape model to represent only valid shapes, while generality quantifies the ability of the model to describe unseen shapes.…”

Section: Statistical Shape Model Of Endometrial Tissue Shapesmentioning

confidence: 99%

Statistical model for simulation of deformable elastic endometrial tissue shapes

Kurtek

Xie

Samir³

et al. 2016

Neurocomputing

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International audienceStatistical shape analysis plays a key role in various medical imaging applications. In particular , such methods provide tools for registering, deforming, comparing, averaging, and modeling anatomical shapes. In this work, we focus on the application of a recent method for statistical shape analysis of elastic parametrized surfaces to simulation of realistic en-dometrial tissue shapes. The clinical data used here contains ten magnetic resonance imaging (MRI) endometrial tissue surfaces, which are used to learn a generative shape model. We generate random samples from this model, and apply elastic semi-synthetic deformations to the randomly generated tissue shapes. This provides two types of simulated data: (1) MRI-type (without deformation) and (2) corresponding transvaginal utltrasound (TVUS) type endometrial tissue shapes, which undergo a deformation due to the transducer's pressure. The proposed models can be used for validation purposes of automatic, multimodal image registration techniques, which are crucial steps in diagnosing endometriosis

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Section: Statistical Shape Model Of Endometrial Tissue Shapesmentioning

confidence: 99%

Statistical model for simulation of deformable elastic endometrial tissue shapes

Kurtek

Xie

Samir³

et al. 2016

Neurocomputing

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“…We then describe a visualization approach for the overall change in shape of the myocardium based on strain. Finally, we describe a method that uses a shape space Kurtek et al (2013b) to visualize how the cross-sectional contours deform over the cardiac cycle.…”

Section: Cardiac Shape Visualizationmentioning

confidence: 99%

Visualization Techniques for the Developing Chicken Heart

Phan

Grimm

Rugonyi

2015

Advances in Visual Computing

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We present a geometric surface parameterization algorithm and several visualization techniques adapted to the problem of understanding the 4D peristaltic-like motion of the outflow tract (OFT) in an embryonic chick heart. We illustrated the techniques using data from hearts under normal conditions (four embryos), and hearts in which blood flow conditions are altered through OFT banding (four embryos). The overall goal is to create quantitative measures of the temporal heart-shape change both within a single subject and between multiple subjects. These measures will help elucidate how altering hemodynamic conditions changes the shape and motion of the OFT walls, which in turn influence the stresses and strains on the developing heart, causing it to develop differently. We take advantage of the tubular shape and periodic motion of the OFT to produce successively lower dimensional visualizations and quantifications of the cardiac motion.

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“…An important goal in medical imaging is to assess the morphology of anatomical structures for the purposes of disease detection or monitoring. While most research focuses on 3D shape analysis in this setting (Kurtek et al, ; Samir, Kurtek, Srivastava, & Canis, ), curve‐based approaches have been used to model shapes of diffusion tensor magnetic resonance imaging fiber tracts (Kurtek, Srivastava, Klassen, & Ding, ), to assess variability in manual segmentation of medical images (Kurtek et al, ), and to model survival based on glioblastoma multiforme tumor shapes (Bharath, Kurtek, Rao, & Baladandayuthapani, ). Other popular applications of statistical shape analysis include biometrics (Kaziska & Srivastava, ; Samir, Srivastava, Daoudi, & Klassen, ; Srivastava, Samir, Joshi, & Daoudi, ), military (Joshi & Srivastava, ), activity recognition and modeling (Su, Kurtek, Klassen, & Srivastava, ), and anthropology (O'Higgins & Dryden, ), among others.…”

Section: Introductionmentioning

confidence: 99%

Analysis of shape data: From landmarks to elastic curves

Bharath

Kurtek

2020

WIREs Computational Stats

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Proliferation of high-resolution imaging data in recent years has led to substantial improvements in the two popular approaches for analyzing shapes of data objects based on landmarks and/or continuous curves. We provide an expository account of elastic shape analysis of parametric planar curves representing shapes of two-dimensional (2D) objects by discussing its differences, and its commonalities, to the landmark-based approach. Particular attention is accorded to the role of reparameterization of a curve, which in addition to rotation, scaling and translation, represents an important shape-preserving transformation of a curve. The transition to the curve-based approach moves the mathematical setting of shape analysis from finite-dimensional non-Euclidean spaces to infinite-dimensional ones. We discuss some of the challenges associated with the infinite-dimensionality of the shape space, and illustrate the use of geometry-based methods in the computation of intrinsic statistical summaries and in the definition of statistical models on a 2D imaging dataset consisting of mouse vertebrae. We conclude with an overview of the current state-of-the-art in the field.

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Statistical analysis of manual segmentations of structures in medical images

Cited by 25 publications

References 19 publications

Statistical model for simulation of deformable elastic endometrial tissue shapes

Statistical model for simulation of deformable elastic endometrial tissue shapes

Visualization Techniques for the Developing Chicken Heart

Analysis of shape data: From landmarks to elastic curves

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