Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images

Chitphakdithai, Nicha; Duncan, James S.

doi:10.1007/978-3-642-15705-9_45

Cited by 41 publications

(31 citation statements)

References 11 publications

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“…In Niethammer et al (2011), the authors formulated a geometric metamorphosis model to address the challenges arising in the registration of images from TBI, tumour or stroke patients. Other approaches iteratively register and segment the images simultaneously to identify missing correspondences (Periaswamy and Farid, 2006;Chitphakdithai and Duncan, 2010). Based on a seed, Fig.…”

Section: Introductionmentioning

confidence: 99%

Robust whole-brain segmentation: Application to traumatic brain injury

Ledig

Heckemann

Hammers

et al. 2015

Medical Image Analysis

162

155

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We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.

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Section: Introductionmentioning

confidence: 99%

Robust whole-brain segmentation: Application to traumatic brain injury

Ledig

Heckemann

Hammers

et al. 2015

Medical Image Analysis

162

155

View full text Add to dashboard Cite

show abstract

“…A second class of methods is concerned with registration across time points (Chitphakdithai and Duncan, 2010; Niethammer et al, 2011a; Ou et al, 2011; Lou et al, 2013). Chitphakdithai and Duncan (2010) proposed a registration method accommodating the missing correspondences for pre-operative and post-resection brain images.…”

Section: Related Work / Previous Workmentioning

confidence: 99%

“…Chitphakdithai and Duncan (2010) proposed a registration method accommodating the missing correspondences for pre-operative and post-resection brain images. Niethammer et al (2011a) presented a registration framework for TBI images using geometric metamorphosis that maps TBI over time using known, presegmented lesion boundaries defined manually.…”

Section: Related Work / Previous Workmentioning

confidence: 99%

Modeling 4D pathological changes by leveraging normative models

Wang

Prastawa

Irimia

et al. 2016

Computer Vision and Image Understanding

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With the increasing use of efficient multimodal 3D imaging, clinicians are able to access longitudinal imaging to stage pathological diseases, to monitor the efficacy of therapeutic interventions, or to assess and quantify rehabilitation efforts. Analysis of such four-dimensional (4D) image data presenting pathologies, including disappearing and newly appearing lesions, represents a significant challenge due to the presence of complex spatio-temporal changes. Image analysis methods for such 4D image data have to include not only a concept for joint segmentation of 3D datasets to account for inherent correlations of subject-specific repeated scans but also a mechanism to account for large deformations and the destruction and formation of lesions (e.g., edema, bleeding) due to underlying physiological processes associated with damage, intervention, and recovery. In this paper, we propose a novel framework that provides a joint segmentation-registration framework to tackle the inherent problem of image registration in the presence of objects not present in all images of the time series. Our methodology models 4D changes in pathological anatomy across time and and also provides an explicit mapping of a healthy normative template to a subject’s image data with pathologies. Since atlas-moderated segmentation methods cannot explain appearance and locality pathological structures that are not represented in the template atlas, the new framework provides different options for initialization via a supervised learning approach, iterative semisupervised active learning, and also transfer learning, which results in a fully automatic 4D segmentation method. We demonstrate the effectiveness of our novel approach with synthetic experiments and a 4D multimodal MRI dataset of severe traumatic brain injury (TBI), including validation via comparison to expert segmentations. However, the proposed methodology is generic in regard to different clinical applications requiring quantitative analysis of 4D imaging representing spatio-temporal changes of pathologies.

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“…Most methods alternatively estimate the registration and segmentation maps. Chitphakdithai and Duncan (2010) proposed an EM Bayesian framework in the context of a surgical tumor resection. The resection area was detected by statistical learning on a training set based on the intensity values and deformed by interpolation (constant registration cost in the resection area).…”

Section: Introductionmentioning

confidence: 99%

Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs

Parisot¹,

Wells²,

Chemouny³

et al. 2014

Medical Image Analysis

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In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model.

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Non-rigid Registration with Missing Correspondences in Preoperative and Postresection Brain Images

Cited by 41 publications

References 11 publications

Robust whole-brain segmentation: Application to traumatic brain injury

Robust whole-brain segmentation: Application to traumatic brain injury

Modeling 4D pathological changes by leveraging normative models

Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs

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