Adaptive Segmentation of MRI Data

Wells, William M.; Grimson, W. Eric L.; Kikinis, Ron; Jólesz, Ferenc A.

doi:10.1007/978-3-540-49197-2_7

Cited by 230 publications

(345 citation statements)

References 12 publications

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“…Intensity-based methods assign tissue classes to image voxels (Wells III et al, 1995;Alsabti et al, 1998;Van Leemput et al, 1999a,b) with high accuracy, but they can not identify the individual organs and anatomical regions within each tissue class. Methods based on elastic and fluid registration can identify anatomical structures in the brain by deforming a labeled probabilistic brain atlas onto the subject brain (Joshi and Miller, 2000;Avants et al, 2005;Davatzikos et al, 2001;Thirion, 1996).…”

Section: Previous Workmentioning

confidence: 99%

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Yushkevich¹,

Piven²,

Hazlett³

et al. 2006

NeuroImage

7,155

4,931

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Active contour segmentation and its robust implementation using level set methods are well established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intra/interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing.Analogous results for lateral ventricle segmentation are provided.

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Section: Previous Workmentioning

confidence: 99%

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Yushkevich¹,

Piven²,

Hazlett³

et al. 2006

NeuroImage

7,155

4,931

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“…Perhaps most importantly, MR images are known to contain low frequency spatial intensity variations often called RF inhomogeneity or shading artifact. The correction for this artifact can be assumed to be performed before PV estimation and there exist several methods for the task (e.g., Sled et al, 1998;Wells et al, 1996). Another somewhat controversial issue of the model is the assumed Gaussian distributions for tissue classes and for the noise component.…”

Section: Statistical Model For the Partial Volume Effectmentioning

confidence: 99%

Fast and robust parameter estimation for statistical partial volume models in brain MRI

2004

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Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation -maximization-like (EM-like) procedure for simultaneous parameter and partial volume estimation are reported. The robust estimators based on a pruned classification as presented here are shown to perform well even if the initial classification is of poor quality. The results obtained are comparable to those obtained using the EM-like procedure, but require considerably less computation time. Segmentation results of real data based on partial volume estimation are also reported. In addition to considering the parameter estimation problem, we discuss differences between different approximations to the complete mixel model. In summary, the proposed TMCD method allows for the accurate, robust, and efficient estimation of partial volume model parameters, which is crucial to a variety of brain MRI data analysis procedures such as the accurate estimation of tissue volumes and the accurate delineation of the cortical surface. D 2004 Elsevier Inc. All rights reserved.

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“…Automated segmentation has been an elusive goal in the processing of medical images, leaving expert anatomical knowledge as the sole basis for the manual segmentation or definition of brain ROIs. The manual definition of ROIs, however, is problematic because different experts usually produce different segmentations of the same image (5), and even the same expert will produce differing segmentations when segmenting the same image twice (6). The reliability and validity of segmentation can suffer even when subdividing highly discrete anatomical structures, such as the CC.…”

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confidence: 99%

Unifying the analyses of anatomical and diffusion tensor images using volume‐preserved warping

Hao

Bansal

et al. 2007

Magnetic Resonance Imaging

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Purpose: To introduce a framework that automatically identifies regions of anatomical abnormality within anatomical MR images and uses those regions in hypothesisdriven selection of seed points for fiber tracking with diffusion tensor (DT) imaging (DTI). Materials and Methods:Regions of interest (ROIs) are first extracted from MR images using an automated algorithm for volume-preserved warping (VPW) that identifies localized volumetric differences across groups. ROIs then serve as seed points for fiber tracking in coregistered DT images. Another algorithm automatically clusters and compares morphologies of detected fiber bundles. We tested our framework using datasets from a group of patients with Tourette's syndrome (TS) and normal controls. Results:Our framework automatically identified regions of localized volumetric differences across groups and then used those regions as seed points for fiber tracking. In our applied example, a comparison of fiber tracts in the two diagnostic groups showed that most fiber tracts failed to correspond across groups, suggesting that anatomical connectivity was severely disrupted in fiber bundles leading from regions of known anatomical abnormality. Conclusion:Our framework automatically detects volumetric abnormalities in anatomical MRIs to aid in generating a priori hypotheses concerning anatomical connectivity that then can be tested using DTI. Additionally, automation enhances the reliability of ROIs, fiber tracking, and fiber clustering. THE GENERAL RUBRIC of magnetic resonance imaging (MRI) subsumes various modalities of data acquisition (e.g., T1-and T2-weighted anatomical imaging, magnetic resonance spectroscopy, and diffusion tensor (DT) imaging (DTI)), each of which provides unique but complementary information about brain structure and function. Combining data from multiple MRI modalities can provide a more comprehensive view of a subject or group of subjects than can any single MRI modality. For example, anatomical T1-or T2-weighted MR images can help to identify structural boundaries within the gray matter and white matter of the human cerebrum, whereas DTI and its derived measures (e.g., fractional anisotropy [FA], apparent diffusion coefficient [ADC]) provide information about the directional organization of brain tissue that can be used to track nerve-fiber pathways. Moreover, a map of the principal directions for the diffusion of water, generated using DTI, can help to more accurately parcellate the anatomy of the corpus callosum (CC) and other white matter structures (e.g., cingulum, external capsule, and anterior thalamic radiation) (1-4) that appear homogeneous in their contrast and signal intensities in T1-or T2-weighted images.Researchers have long desired to integrate information from both modalities to provide a more comprehensive view of anatomical structure and connectivity in the human brain. The integration of T1-weighted and DT imaging data, however, faces at least two major obstacles: 1) the accurate coregistration of datasets from the two modaliti...

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Adaptive Segmentation of MRI Data

Cited by 230 publications

References 12 publications

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Fast and robust parameter estimation for statistical partial volume models in brain MRI

Unifying the analyses of anatomical and diffusion tensor images using volume‐preserved warping

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