7 research outputs found
Explicit B-spline regularization in diffeomorphic image registration
No full textDiffeomorphic mappings are central to image registration due largely to their topological properties and success in providing biologically plausible solutions to deformation and morphological estimation problems. Popular diffeomorphic image registration algorithms include those characterized by time-varying and constant velocity fields, and symmetrical considerations. Prior information in the form of regularization is used to enforce transform plausibility taking the form of physics-based constraints or through some approximation thereof, e.g. Gaussian smoothing of the vector fields (a la Thirion's Demons citep{thirion1998}). In the context of the original Demons' framework, the so-called {it directly manipulated free-form deformation} citep{tustison2009} can be viewed as a smoothing alternative in which explicit regularization is achieved through fast B-spline approximation. This characterization can be used to provide B-spline ``flavored'' diffeomorphic image registration solutions with several advantages. Implementation is open source and available through the Insight Toolkit and our Advanced Normalization Tools (ANTs) repository. A thorough comparative evaluation with the well-known SyN algorithm citep{avants2008}, implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools
Methodological considerations in longitudinal morphometry of traumatic brain injury
No full textTraumatic brain injury (TBI) has recently been reconceptualized as a chronic, evolving disease process. This new view necessitates quantitative assessment of post-injury changes in brain structure that may allow more accurate monitoring and prediction of recovery. In particular, TBI is known to trigger neurodegenerative processes and therefore quantifying progression of diffuse atrophy over time is currently of utmost interest. However, there are various methodological issues inherent to longitudinal morphometry in TBI that researchers need to be aware of. In this paper, we first overview several of these methodological challenges: lesion evolution, neurosurgical procedures, power, bias, and nonlinearity. We then introduce a sensitive, reliable, and unbiased longitudinal multivariate analysis protocol that combines dimensionality reduction and region of interest approaches. This analysis pipeline is demonstrated using a small dataset consisting of four chronic TBI survivors
Learning from open source software projects to improve scientific review
No full textPeer-reviewed publications are the primary mechanism for sharing scientific results. The current peer-review process is, however, fraught with many problems that undermine the pace, validity, and credibility of science. We highlight five salient problems: (1) reviewers are expected to have comprehensive expertise; (2) reviewers do not have sufficient access to methods and materials to evaluate a study; (3) reviewers are neither identified nor acknowledged; (4) there is no measure of the quality of a review; and (5) reviews take a lot of time, and once submitted cannot evolve. We propose that these problems can be resolved by making the following changes to the review process. Distributing reviews to many reviewers would allow each reviewer to focus on portions of the article that reflect the reviewer's specialty or area of interest and place less of a burden on any one reviewer. Providing reviewers materials and methods to perform comprehensive evaluation would facilitate transparency, greater scrutiny, and replication of results. Acknowledging reviewers makes it possible to quantitatively assess reviewer contributions, which could be used to establish the impact of the reviewer in the scientific community. Quantifying review quality could help establish the importance of individual reviews and reviewers as well as the submitted article. Finally, we recommend expediting post-publication reviews and allowing for the dialog to continue and flourish in a dynamic and interactive manner. We argue that these solutions can be implemented by adapting existing features from open-source software management and social networking technologies. We propose a model of an open, interactive review system that quantifies the significance of articles, the quality of reviews, and the reputation of reviewers.National Institute of Mental Health (U.S.) (R01Grant MH084029
The Insight ToolKit Image Registration Framework
No full textPublicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit ( ITK4 ) seeks to es- tablish new standards in publicly available image registration methodology. ITK4 makes severaladvances in comparison to previous versions of ITK. ITK4 supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field) and low-dimensional (affine) transformations with metrics that are reusable across transform types and with com- posite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available.Furthermore, ITK4 reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations versus translations). A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher tomore easily focus on design/comparison of registration strategies. In total, the ITK4 contribu- tion is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextu- alize this work with a reference registration evaluation study with application to pediatric brainlabeling
Instrumentation bias in the use and evaluation of scientific software: Recommendations for reproducible practices in the computational sciences
No full textRobust automated amygdala segmentation via multi-atlas diffeomorphic registration
No full textHere, we describe a novel method for volumetric segmentation of the amygdala from MRI images collected from 35 human subjects. This approach is adapted from open-source techniques employed previously with the hippocampus (Suh et al., 2011; Wang et al., 2011a; Wang et al., 2011b). Using multi-atlas segmentation and machine learning-based correction, we were able to produce automated amygdala segments with high dice (Mean= 0.918 for the left amygdala; 0.916 for the right amygdala) and Jaccard coefficients (Mean= 0.850 for the left; 0.846 for the right) compared to rigorously hand-traced volumes. This automated routine also produced amygdala segments with high intra-class (consistency=.830, absolute agreement =.819 for the left; consistency=. 786, absolute agreement =. 783 for the right) and bivariate (r =.831 for the left; r =0.797 for the right) correlations compared to hand-drawn amygdala. Our results are discussed in relation to results from other cutting-edge segmentation techniques, as well as commonly- available approaches to amygdala segmentation (e.g., Freesurfer). We believe this new technique has broad application to research with large sample sizes for which amygdala quantification might be needed
