Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni- / multi- modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.Comment: Add new discussion

Fast Predictive Multimodal Image Registration

We introduce a deep encoder-decoder architecture for image deformation prediction from multimodal images. Specifically, we design an image-patch-based deep network that jointly (i) learns an image similarity measure and (ii) the relationship between image patches and deformation parameters. While our method can be applied to general image registration formulations, we focus on the Large Deformation Diffeomorphic Metric Mapping (LDDMM) registration model. By predicting the initial momentum of the shooting formulation of LDDMM, we preserve its mathematical properties and drastically reduce the computation time, compared to optimization-based approaches. Furthermore, we create a Bayesian probabilistic version of the network that allows evaluation of registration uncertainty via sampling of the network at test time. We evaluate our method on a 3D brain MRI dataset using both T1- and T2-weighted images. Our experiments show that our method generates accurate predictions and that learning the similarity measure leads to more consistent registrations than relying on generic multimodal image similarity measures, such as mutual information. Our approach is an order of magnitude faster than optimization-based LDDMM.Comment: Accepted as a conference paper for ISBI 201

Closed and open source neuroimage analysis tools and libraries at UNC

pre-printThe emergence of open-source libraries and development tools in the last decade has changed the process of academic software development in many ways. In medical image processing and visualization this change is especially evident, also because open source projects are actively furthered by grant funding institutions. This manuscript presents the use of such development tools and libraries at the UNC Neuro-Image Analysis Laboratory for open source applications and tools. We have also experienced in our research that the development of open source in academics raises the issue of access to unpublished methodology. The strategy at our laboratory is to combine all in-house libraries and applications into a single repository that consists of two parts: a fully open source part that is distributed under a Berkley-style license and a private, closed source part with unpublished tools and methods. Access to the open source part is unrestricted, whereas the private parts can only be downloaded via cvs user login. This setup solved our issues regarding unpublished methodology, as migration from the private to the open source part is very simple. Overall our experience with this development environment within the academic setting is very positive

Minimum description length with local geometry

pre-printEstablishing optimal correspondence across object populations is essential to statistical shape analysis. Minimizing the description length (MDL) is a popular method for finding correspondence. In this work, we extend the MDL method by incorporating various local curvature metrics. Using local curvature can improve performance by ensuring that corresponding points exhibit similar local geometric characteristics that can't always be captured by mere point locations. We illustrate results on a variety of anatomical structures. The MDL method with a combination of point locations and curvature outperforms all the other methods we analyzed, including traditional MDL and spherical harmonics (SPHARM) correspondence, when the analyzed object population exhibits complex structure. When the objects are of simple nature, however, there's no added benefit to using the local curvature. In our experiments, we did not observe a significant difference in the correspondence quality when different curvature metrics (e.g. principal curvatures, mean curvature, Gaussian curvature) were used

Statistical group differences in anatomical shape analysis using Hotelling T2 Metric

journal articleShape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology. The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T2 two sample metric. Statistical pvalues, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives. Prior versions of this shape analysis framework have been applied already to clinical studies on hippocampus and lateral ventricle shape in adult schizophrenics. The novelty of this submission is the use of the Hotelling T2 two-sample group difference metric for the computation of a template free statistical shape analysis. Template free group testing allowed this framework to become independent of any template choice, as well as it improved the sensitivity of our method considerably. In addition to our existing correction methodology for the multiple comparison problem using non-parametric permutation tests, we have extended the testing framework to include False Discovery Rate (FDR). FDR provides a significance correction with higher sensitivity while allowing a expected minimal amount of false-positives compared to our prior testing scheme

Correspondence evaluation in local shape analysis and structural subdivision

journal articleRegional volumetric and local shape analysis has become of increasing interest to the neuroimaging community due to the potential to locate morphological changes. In this paper we compare three common correspondence methods applied to two studies of hippocampal shape in schizophrenia: correspondence via deformable registration, spherical harmonics (SPHARM) and Minimum Description Length (MDL) optimization. These correspondence methods are evaluated in respect to local statistical shape analysis and structural subdivision analysis. Results show a non-negligible influence of the choice of correspondence especially in studies with low numbers of subjects. The differences are especially striking in the structural subdivision analysis and hints at a possible source for the diverging findings in many subdivision studies. Our comparative study is not meant to be exhaustive, but rather raises awareness of the issue and shows that assessing the validity of the correspondence is an important step

Synergy of Image Analysis for Animal and Human Neuroimaging Supports Translational Research on Drug Abuse

The use of structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) in animal models of neuropathology is of increasing interest to the neuroscience community. In this work, we present our approach to create optimal translational studies that include both animal and human neuroimaging data within the frameworks of a study of post-natal neuro-development in intra-uterine cocaine-exposure. We propose the use of non-invasive neuroimaging to study developmental brain structural and white matter pathway abnormalities via sMRI and DTI, as advanced MR imaging technology is readily available and automated image analysis methodology have recently been transferred from the human to animal imaging setting. For this purpose, we developed a synergistic, parallel approach to imaging and image analysis for the human and the rodent branch of our study. We propose an equivalent design in both the selection of the developmental assessment stage and the neuroimaging setup. This approach brings significant advantages to study neurobiological features of early brain development that are common to animals and humans but also preserve analysis capabilities only possible in animal research. This paper presents the main framework and individual methods for the proposed cross-species study design, as well as preliminary DTI cross-species comparative results in the intra-uterine cocaine-exposure study