Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations. I. Methodology and validation on normal subjects

Dawant, Benoît M.; Hartmann, Steven L.; Thirion, Jean-Philippe; Maes, Frederik; Vandermeulen, Dirk; Demaerel, Philippe

doi:10.1109/42.811271

Cited by 200 publications

(120 citation statements)

References 15 publications

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“…Dawant et al [49] developed an automatic method for 3D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformation. An adaptive fuzzy segmentation algorithm for 3D magnetic resonance image was employed in Pham and Prince [50].…”

Section: Intensity-based Methodsmentioning

confidence: 99%

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Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

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Section: Intensity-based Methodsmentioning

confidence: 99%

“…Dawant et al [49] Combination of a global similarity transformation and local free-form deformations.…”

Section: D Sagittal Oriented Imagesmentioning

confidence: 99%

Methods on Skull Stripping of MRI Head Scan Images—a Review

2015

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“…Atlas-based MR segmentation algorithms have already shown to be reliable when performed on nonshifted brain structures (Bach Cuadra et al, 2001;Dawant et al, 1999a) but are still of limited use when a space-occupying lesion induces brain deformations that are yet not clearly predictable.…”

Section: Introductionmentioning

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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“…Such segmentation can be based on non-rigid registration of the atlas on this subject [3] or on hybrid schemes that combine registration and segmentation. A first example can be found in Pohl et al [4] where an Expectation Maximization algorithm is presented to combine the registration of an atlas with the segmentation of magnetic resonance images.…”

Section: Introductionmentioning

confidence: 99%

Unbiased group-wise alignment by iterative central tendency estimations

Craene

Macq

Marqués

et al. 2008

Math. Model. Nat. Phenom.

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Abstract. This paper introduces a new approach for the joint alignment of a large collection of segmented images into the same system of coordinates while estimating at the same time an optimal common coordinate system. The atlas resulting from our group-wise alignment algorithm is obtained as the hidden variable of an Expectation-Maximization (EM) estimation. This is achieved by identifying the most consistent label across the collection of images at each voxel in the common frame of coordinates.In an iterative process, each subject is iteratively aligned with the current probabilistic atlas until convergence of the estimated atlas is reached. Two different transformation models are successively applied in the alignment process: an affine transformation model and a dense non-rigid deformation field. The metric for both transformation models is the mutual information that is computed between the probabilistic atlas and each subject. This metric is optimized in the affine alignment step using a gradient based stochastic optimization (SPSA) and with a variational approach to estimate the non-rigid atlas to subject transformations.A first advantage of our method is that the computational cost increases linearly with the number of subjects in the database. This method is therefore particularly suited for a large number of subjects. Another advantage is that, when computing the common coordinate system, the estimation algorithm identifies weights for each subject on the basis of the typicality of the segmentation. This makes the common coordinate system robust to outliers in the population.Several experiments are presented in this paper to validate our atlas construction method on a population of 80 brain images segmented into 4 labels (background, white and gray matters Unbiased group-wise alignment and ventricles). First, the 80 subjects were aligned using affine and dense non-rigid deformation models. The results are visually assessed by examining how the population converges closer to a central tendency when the deformation model allows more degrees of freedom (from affine to dense non-rigid field). Second, the stability of the atlas construction procedure for various sizes of population was investigated by starting from a subset of the total population which was incrementally augmented until the total population of 80 subjects was reached. Third, the consistency of our group-wise reference (hidden variable of the EM algorithm) was also compared to the choice of an arbitrary subject for a subset of 10 subjects. According to William's index, our reference choice performed favorably. Finally, the performance of our algorithm was quantified on a synthetic population of 10 subjects (generated using random B-Spline transformations) using a global overlap measure for each label. We also measured the robustness of this measure to the introduction of noisy subjects in the population.

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Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations. I. Methodology and validation on normal subjects

Cited by 200 publications

References 15 publications

Methods on Skull Stripping of MRI Head Scan Images—a Review

Methods on Skull Stripping of MRI Head Scan Images—a Review

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

Unbiased group-wise alignment by iterative central tendency estimations

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