Segmentation of 3D objects from MRI volume data using constrained elastic deformations of flexible Fourier surface models

Székely, G.; Kelemen, A.; Brechbühler, Christian; Gerig, G.

doi:10.1007/bfb0034992

“…Furthermore, by utilizing a hierarchical (multi-scale) and regional principal component analysis to capture the shape variation statistics in a training set (Hamarneh and McInerney, 2001), we can keep the deformations consistent with prior knowledge of possible shape variations. Whereas general statistically-derived shape models produce only global shape variation modes (Cootes et al, 1999;Szekely et al, 1996), we are able to produce spatially-localized feasible deformations at desired scales, thus supporting our goal of intelligent deformation planning.…”

Section: Motor Systemmentioning

confidence: 62%

Deformable Organisms for Automatic Medical Image Analysis

Hamarneh

¹

,

McInerney

²

,

Terzopoulos

³

2001

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001

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We introduce a new approach to medical image analysis that combines deformable model methodologies with concepts from the field of artificial life. In particular, we propose 'deformable organisms', autonomous agents whose task is the automatic segmentation, labeling, and quantitative analysis of anatomical structures in medical images. Analogous to natural organisms capable of voluntary movement, our artificial organisms possess deformable bodies with distributed sensors, as well as (rudimentary) brains with motor, perception, behavior, and cognition centers. Deformable organisms are perceptually aware of the image analysis process. Their behaviors, which manifest themselves in voluntary movement and alteration of body shape, are based upon sensed image features, pre-stored anatomical knowledge, and a deliberate cognitive plan. We demonstrate several prototype deformable organisms based on a multiscale axisymmetric body morphology, including a 'corpus callosum worm' that can overcome noise, incomplete edges, considerable anatomical variation, and interference from collateral structures to segment and label the corpus callosum in 2D mid-sagittal MR brain images.

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“…The average brain provides strong prior information about the expected image data and can be used to form probabilistic brain atlases (Collins et al, 1992;Thompson et al, 1997). Specific models for prior shape have been used successfully in our lab Duncan, 1992, 1996;Wang and Staib, 1998;Yang et al, , 2004a and by other groups (Cootes et al, 1993;Szekély et al, 1995) for segmentation. The statistics of a sample of images can be used to guide the deformation in a way governed by the measured variation of individuals.…”

Section: Subcortical Structure Segmentationmentioning

confidence: 99%

Geometric strategies for neuroanatomic analysis from MRI

Duncan

¹

,

Papademetris

²

,

Yang

³

et al. 2004

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In this paper, we describe ongoing work in the Image Processing and Analysis Group (IPAG) at Yale University specifically aimed at the analysis of structural information as represented within magnetic resonance images (MRI) of the human brain. Specifically, we will describe our applied mathematical approaches to the segmentation of cortical and subcortical structure, the analysis of white matter fiber tracks using diffusion tensor imaging (DTI), and the intersubject registration of neuroanatomical (aMRI) data sets. Many of our methods rally around the use of geometric constraints, statistical (MAP) estimation, and the use of level set evolution strategies. The analysis of gray matter structure and connecting white matter paths combined with the ability to bring all information into a common space via intersubject registration should provide us with a rich set of data to investigate structure and variation in the human brain in neuropsychiatric disorders, as well as provide a basis for current work in the development of integrated brain function-structure analysis.

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“…The models are fitted to images by minimizing energy functions, simulating dynamical systems, or applying probabilistic inference methods, but they do not control this optimization process other than in primitive ways, such as monitoring convergence or equilibrium. Some deformable models incorporate prior information to constrain shape and image appearance and the observed statistical variation of these quantities (Cootes et al, 1995(Cootes et al, , 1999Szekely et al, 1996). These models have no explicit awareness of where they or their parts are, and therefore the effectiveness of such constraints is dependent upon appropriate model initialization.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Deformable organisms for automatic medical image analysis

McInerney

¹

,

Hamarneh

²

,

Shenton

³

et al. 2002

Medical Image Analysis

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We introduce a new approach to medical image analysis that combines deformable model methodologies with concepts from the field of artificial life. In particular, we propose 'deformable organisms', autonomous agents whose task is the automatic segmentation, labeling, and quantitative analysis of anatomical structures in medical images. Analogous to natural organisms capable of voluntary movement, our artificial organisms possess deformable bodies with distributed sensors, as well as (rudimentary) brains with motor, perception, behavior, and cognition centers. Deformable organisms are perceptually aware of the image analysis process. Their behaviors, which manifest themselves in voluntary movement and alteration of body shape, are based upon sensed image features, pre-stored anatomical knowledge, and a deliberate cognitive plan. We demonstrate several prototype deformable organisms based on a multiscale axisymmetric body morphology, including a 'corpus callosum worm' that can overcome noise, incomplete edges, considerable anatomical variation, and interference from collateral structures to segment and label the corpus callosum in 2D mid-sagittal MR brain images.

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Segmentation of 3D objects from MRI volume data using constrained elastic deformations of flexible Fourier surface models

Cited by 41 publications

References 0 publications

Deformable Organisms for Automatic Medical Image Analysis

Deformable Organisms for Automatic Medical Image Analysis

Geometric strategies for neuroanatomic analysis from MRI

Deformable organisms for automatic medical image analysis

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