Pairwise registration of images with missing correspondences due to resection

Chitphakdithai, Nicha; Duncan, James S.

doi:10.1109/isbi.2010.5490164

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…However, throughout the majority of images, transformations are diffeomorphic, and we have chosen to work in the computational anatomy random orbit model to preserve properties that are useful for morphometry in addition to registration, such as the embedding of human anatomy into a metric space. Metamorphosis-based models (Miller et al, 2002;Li et al, 2011;Nithiananthan et al, 2012) allow image intensity to vary in certain regions to match anomalies, while maskbased models (Periaswamy and Farid, 2006;Sdika and Pelletier, 2009;Vidal et al, 2009;Chitphakdithai and Duncan, 2010b) or 9 manually or automatically ignore these anomalies. Our method leverages the strengths of these last two, modeling both nonmonotonic image intensity variation and masking in a generative statistical framework.…”

Section: Resultsmentioning

confidence: 99%

Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease

et al. 2020

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This paper examines the problem of diffeomorphic image registration in the presence of differing image intensity profiles and sparsely sampled, missing, or damaged tissue. Our motivation comes from the problem of aligning 3D brain MRI with 100-micron isotropic resolution to histology sections at 1 × 1 × 1,000-micron resolution with multiple varying stains. We pose registration as a penalized Bayesian estimation, exploiting statistical models of image formation where the target images are modeled as sparse and noisy observations of the atlas. In this injective setting, there is no assumption of symmetry between atlas and target. Cross-modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity. Missing data is accommodated via a multiple atlas selection procedure where several atlas images may be of homogeneous intensity and correspond to "background" or "artifact." The two concepts are combined within an Expectation-Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively and polynomial coefficients are computed in closed form. We validate our method with simulated images, examples from neuropathology, and a standard benchmarking dataset. Finally, we apply it to reconstructing digital pathology and MRI in standard atlas coordinates. By using a standard convolutional neural network to detect tau tangles in histology slices, this registration method enabled us to quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer's disease postmortem specimen.

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Section: Resultsmentioning

confidence: 99%

Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease

et al. 2020

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“…Several works are developed to register brain images with resections by matching subvolumes [27,28], landmarks [29] or segmentation surfaces [30]. In addition, the ExpectationMaximization (EM) framework has been used for a joint estimation of resection region and registration [31,32]. These works rely on local features, and in most cases, their registration results have limited dimensionality rather than a dense deformation field, due to the computational complexity of EM.…”

Section: Deformable Registration Algorithms For Images With Topologicmentioning

confidence: 99%

Registration of Images With Varying Topology Using Embedded Maps

Long

Laurienti

et al. 2012

IEEE Trans. Med. Imaging

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In medical images, intensity changes caused by certain pathology can change the topology of image level-sets and are thus commonly referred to as topological changes. Topological changes cause false deformation in existing deformable registration algorithms, which in turn leads to unreliable observations in the clinical study that relies on the deformation fields, such as deformation based morphometry (DBM). In this work, we develop a new deformable registration algorithm for images with topological changes. In our proposed algorithm, 3D images are embedded as 4D surfaces in a Riemannian space. The registration is therefore conducted as a surface evolution, which is modeled by a diffusion process. Our algorithm differs from existing methods in the sense that it takes an a-priori estimation of areas with topological change as an additional input and generates dense deformation vector fields which are free of false deformation. In particular, the output of our algorithm is composed of a diffeomorphic deformation field and an intensity displacement which corrects intensity difference caused by topological changes. By conducting multiple sets of experiments, we demonstrate that our proposed algorithm is capable of accurately registering images with considerable topological changes. More importantly, the resulting deformation field is not impacted by topological changes, i.e., there is no false deformation.

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“…Several methods to register pre- and post-operative MRI images are already available. The authors in Chitphakdithai and Duncan ( 2010 ) propose a solution to register corresponding healthy tissues of longitudinal images. Furthermore, the same authors (Chitphakdithai et al, 2011 ) develop a method to register pre-operative MRI data with any stage of images acquired after tumor resection.…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment

Canalini

Klein

Waldmannstetter

et al. 2022

Front. Neuroimaging

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Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. This work evaluates the influence of multiple sequences, namely T1-weighted (T1), T2-weighted (T2), contrast enhanced T1-weighted (T1-CE), and T2 Fluid Attenuated Inversion Recovery (FLAIR), and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1-CE achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1-CE obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1-CE. Both approaches utilizing the T1-CE sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.

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Pairwise registration of images with missing correspondences due to resection

Cited by 8 publications

References 11 publications

Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease

Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease

Registration of Images With Varying Topology Using Embedded Maps

Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment

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