Active deformation fields: Dense deformation field estimation for atlas-based segmentation using the active contour framework

Gorthi, Sai Subrahmanyam; Duay, Valérie; Bresson, Xavier; Cuadra, Meritxell Bach; Castro, F. Javier Sánchez; Pollo, Claudio; Allal, Abdelkarim Said; Thiran, Jean-Philippe

doi:10.1016/j.media.2011.05.008

Cited by 26 publications

(33 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We propose regseg to map anatomical information extracted from Tlw images into the corresponding dMRI of the same subject. Previously, joint segmentation and registration has been applied successfully to other problems such as longitudinal object tracking (Paragios, 2003) and atlas-based segmentation (Gorthi et al, 2011). The most common approach involves optimizing a deformation model (registration) that supports the evolution of the active contours (segmentation), like Paragios (2003); Yezzi et al (2003).…”

Section: Discussionmentioning

confidence: 99%

“…Atlas-based segmentation is typically applied on structural and high-resolution images. A comprehensive review of joint segmentation and registration methods applied in atlasbased segmentation is found in (Gorthi et al, 2011). They also propose a multiphase level-set function initialized from a labeled atlas to implement the active contours that drive the atlas registration.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Surface-driven registration method for the structure-informed segmentation of diffusion MR images

et al. 2016

Self Cite

View full text Add to dashboard Cite

Keywords:Active surfaces Cortical panellation Diffusion MRI Nonlinear registration Segmentation Susceptibility distortion Current methods for processing diffusion MRI (dMRI) to map the connectivity of the human brain require precise delineations of anatomical structures. This requirement has been approached by either segmenting the data in native dMRI space or mapping the structural information from Tl -weighted (Tl w) images. The characteristic features of diffusion data in terms of signal-to-noise ratio, resolution, as well as the geometrical distortions caused by the inhomogeneity of magnetic susceptibility across tissues hinder both solutions. Unifying the two approaches, we propose regseg, a surface-to-volume nonlinear registration method that segments homogeneous regions within multivariate images by mapping a set of nested reference-surfaces. Accurate surfaces are extracted from a Tl w image of the subject, using as target image the bivariate volume comprehending the fractional anisotropy (FA) and the apparent diffusion coefficient (ADC) maps derived from the dMRI dataset. We first verify the accuracy of regseg on a general context using digital phantoms distorted with synthetic and random deformations. Then we establish an evaluation framework using undistorted dMRI data from the Human Connectome Project (HCP) and realistic deformations derived from the inhomogeneity fieldmap corresponding to each subject. We analyze the performance of regseg computing the misregistration error of the surfaces estimated after being mapped with regseg onto 16 datasets from the HCP. The distribution of errors shows a 95% CI of 0.56-0.66 mm, that is below the dMRI resolution (1.25 mm, isotropic). Finally, we cross-compare the proposed tool against a nonlinear i>0-to-T2w registration method, thereby obtaining a significantly lower misregistration error with regseg. The accurate mapping of structural information in dMRI space is fundamental to increase the reliability of network building in connectivity analyses, and to improve the performance of the emerging structure-informed techniques for dMRI data processing.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Surface-driven registration method for the structure-informed segmentation of diffusion MR images

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Regarding the registration, all the 12 atlases are registered to each patient to be segmented. The registration procedure and the corresponding parameter values are same as those used in [11].…”

Section: New Neighborhood Prior Modelmentioning

confidence: 99%

“…1 shows, in one of the axial slices, the ground truth segmentations and the results obtained from the SBA-based fusion methods, both with and without the pairwise terms. The quantitative evaluations are performed over the entire dataset, using "Dice Similarity Metric" (DSM) and sensitivity; DSM is a commonly used metric [11] that measures the percentage of overlap between the ground truth and the automated segmentations; sensitivity is a measure of under-segmentation that computes the "true positive fraction" value. Fig.…”

Section: New Neighborhood Prior Modelmentioning

confidence: 99%

Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation

Gorthi

Cuadra

Tercier³

et al. 2013

IEEE Signal Process. Lett.

Self Cite

View full text Add to dashboard Cite

Abstract-In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: "Global Weighted Shape-Based Averaging" (GWSBA) and "Local Weighted Shape-Based Averaging" (LWSBA). These methods extend the well known Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.Index Terms-Atlas-based segmentation, label fusion, medical imaging, MRF, SBA.

show abstract

“…Atlas-based segmentation is a powerful and popular technique for automatic delineation of structures in volumetric images91112131415161718, especially the hippocampus181920. Atlas-based methods are initiated by registering an atlas with the target image to be segmented.…”

mentioning

confidence: 99%

Hippocampus Segmentation Based on Local Linear Mapping

Pang

Jiang

et al. 2017

Sci Rep

View full text Add to dashboard Cite

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.

show abstract

Active deformation fields: Dense deformation field estimation for atlas-based segmentation using the active contour framework

Cited by 26 publications

References 50 publications

Surface-driven registration method for the structure-informed segmentation of diffusion MR images

Surface-driven registration method for the structure-informed segmentation of diffusion MR images

Weighted Shape-Based Averaging With Neighborhood Prior Model for Multiple Atlas Fusion-Based Medical Image Segmentation

Hippocampus Segmentation Based on Local Linear Mapping

Contact Info

Product

Resources

About