&lt;title&gt;Automatic registration of 3D MR images with a computerized brain atlas&lt;/title&gt;

Cuisenaire, O.; Thiran, Jean-Philippe; Macq, Benoı̂t; Michel, Christian; Volder, Anne De; Marqués, Ferran

doi:10.1117/12.237946

Cited by 25 publications

(18 citation statements)

References 7 publications

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“…It includes the following steps: (1) affine registration of the MRI data with the atlas [103]; (2) a synthetic tumor is seeded into the brain atlas to provide a template for the lesion; (3) combining a method of optical flow principles and a model of lesion growth, the seeded atlas is deformed. This is a semiautomatic method where a seed voxel of tumors is chosen manually.…”

Section: Deformable Modelsmentioning

confidence: 99%

Segmentation of multiple sclerosis lesions in MR images: a review

2011

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Section: Deformable Modelsmentioning

confidence: 99%

Segmentation of multiple sclerosis lesions in MR images: a review

2011

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“…(1) An affine transformation [25] is applied to the atlas image in order to globally match the patient. (2) The lesion is segmented using the adaptive template moderated spatially varying statistical classification (ATM SVC) algorithm [16].…”

Section: Atlas To Pathological Brain Non-rigid Registrationmentioning

confidence: 99%

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

Cuadra

Craene

Duay

et al. 2006

Computer Methods and Programs in Biomedicine

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“…As proposed by Cuisenaire et al (1996), the global transformation from the patient image to the atlas is modeled as a threedimensional affine transform, that is, y = Ax + b with A as a 3 Â 3 matrix and b as a 3D vector. The 12 parameters of this transform are optimized in order to minimize the distance between the patient and atlas cortical surfaces, both segmented using mathematical morphology operators.…”

Section: Affine Transformationmentioning

confidence: 99%

Segmentation of brain structures in presence of a space-occupying lesion

et al. 2005

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Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage. D 2004 Elsevier Inc. All rights reserved.

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<title>Automatic registration of 3D MR images with a computerized brain atlas</title>

Cited by 25 publications

References 7 publications

Segmentation of multiple sclerosis lesions in MR images: a review

Segmentation of multiple sclerosis lesions in MR images: a review

Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

Segmentation of brain structures in presence of a space-occupying lesion

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