Advanced Normalization Tools Research Articles

A supervised framework for lesion segmentation and automated VLSM analyses in left hemispheric stroke

A supervised framework for lesion segmentation and automated VLSM analyses in left hemispheric stroke

Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR.

Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning intervention, and for tracking tumor changes over time. However, this task is complicated by the lack of prior knowledge concerning tumor location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and tumor segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package--a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal Brain Tumor Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade glioma tumor four-modality MRI from five different institutions. Average Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for "complete", "core", and "enhanced" tumor components, respectively.

Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements

Many studies of the human brain have explored the relationship between cortical thickness and cognition, phenotype, or disease. Due to the subjectivity and time requirements in manual measurement of cortical thickness, scientists have relied on robust software tools for automation which facilitate the testing and refinement of neuroscientific hypotheses. The most widely used tool for cortical thickness studies is the publicly available, surface-based FreeSurfer package. Critical to the adoption of such tools is a demonstration of their reproducibility, validity, and the documentation of specific implementations that are robust across large, diverse imaging datasets. To this end, we have developed the automated, volume-based Advanced Normalization Tools (ANTs) cortical thickness pipeline comprising well-vetted components such as SyGN (multivariate template construction), SyN (image registration), N4 (bias correction), Atropos (n-tissue segmentation), and DiReCT (cortical thickness estimation). In this work, we have conducted the largest evaluation of automated cortical thickness measures in publicly available data, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets (IXI, MMRR, NKI, and OASIS), with parcellation based on the recently proposed Desikan–Killiany–Tourville (DKT) cortical labeling protocol. We found good scan–rescan repeatability with both FreeSurfer and ANTs measures. Given that such assessments of precision do not necessarily reflect accuracy or an ability to make statistical inferences, we further tested the neurobiological validity of these approaches by evaluating thickness-based prediction of age and gender. ANTs is shown to have a higher predictive performance than FreeSurfer for both of these measures. In promotion of open science, we make all of our scripts, data, and results publicly available which complements the use of open image data sets and the open source availability of the proposed ANTs cortical thickness pipeline.

SU-D-WAB-05: Image-Based Proton Range Verification Using Intensity-Corrected CBCT

Purpose: Using CBCT to detect variations in proton beam path prior to treatment is an important step in the quality assurance of proton therapy. However, the Hounsfield Units (HUs) from a CBCT differs slightly from a simulation CT, thereby introducing additional uncertainties in the conversion from HUs to proton stopping power. The aim of this work is to demonstrate the feasibility of using deformable image registration to map simulation CT HUs onto a treatment CBCT for range verification during proton therapy. Methods: A simulation CT and two simulated proton CBCT of a head and neck patient were used in this study. We used a diffeomorphic deformable image registration algorithm with symmetric normalization in conjunction with cross correlation similarity metrics (ANTs: Advanced Normalization Tools) to deform the simulation CT onto the CBCTs. Polar plots of proton beam water equivalent thickness (WET) were generated using a ray tracing algorithm at different gantry angles in the unmodified CBCT, corrected CBCT and simulation CT for analysis. Results: With no anatomical variation, corrected CBCT and simulation CT image exhibited excellent agreement in WETs (mean difference of 1.26 mm, interquartile range 0.42∼1.79mm). In the unmodified CBCT image, larger difference in WETs (mean value 3.38 mm, interquartile range 1.90∼5.03mm) attributed to errors in HU to stopping power conversion were observed. With tumor shrinkage the differences in the WET polar plots between planning CT and corrected CBCT correlated with the location of anatomical change in the nasal/oral cavity, indicating potential deviations in proton dose deposition. Conclusion: It is feasible to use deformable image registration to correct CBCT HUs for image based proton range verification. Polar plots of deviations in WET between CBCT and simulation CT is potentially an efficient and practical quality assurance tool in the use of CBCT for proton therapy applications.

Explicit B-spline regularization in diffeomorphic image registration

Diffeomorphic 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 (Thirion, 1998)]. In the context of the original Demons' framework, the so-called directly manipulated free-form deformation (DMFFD) (Tustison et al., 2009) 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 (Avants et al., 2008), implemented within the same framework, and its B-spline analog is performed using open labeled brain data and open source evaluation tools.

Instrumentation bias in the use and evaluation of scientific software: recommendations for reproducible practices in the computational sciences.

OPINION article Front. Neurosci., 09 September 2013Sec. Brain Imaging Methods Volume 7 - 2013 | https://doi.org/10.3389/fnins.2013.00162

Age-Specific MRI Templates for Pediatric Neuroimaging

This study created a database of pediatric age-specific magnetic resonance imaging (MRI) brain templates for normalization and segmentation. Participants included children from 4.5 through 19.5 years, totaling 823 scans from 494 subjects. Open-source processing programs (FMRIB Software Library, Statistical Parametric Mapping, Advanced Normalization Tools [ANTS]) constructed head, brain, and segmentation templates in 6-month intervals. The tissue classification (white matter [WM], gray matter [GM], cerebrospinal fluid) showed changes over age similar to previous reports. A volumetric analysis of age-related changes in WM and GM based on these templates showed expected increase/decrease pattern in GM and an increase in WM over the sampled ages. This database is available for use for neuroimaging studies (http://jerlab.psych.sc.edu/neurodevelopmentalmridatabase).

A computational framework to quantify tissue microstructural integrity using conventional MRI macrostructural volumetry

In this work, we present a framework to fuse and quantify tissue microstructural attributes (e.g. diffusion, functional, relaxation and perfusion MRI) using high spatial resolution T1-weighted volumetric measurements. The proposed computational procedures can be implemented by utilizing advanced, image segmentation, registration and diffusion tensor image processing software. Feasibility of the framework is also demonstrated using in-house developed software and freely available software such as FreeSurfer for regional tissue segmentation, Advanced Normalization Tools ( ANTs) for registration and DTIStudio for fiber tracking and diffusion tensor data processing. We also demonstrate the application of these methods in the provision of quality control measures to assess the fidelity of tissue segmentation and multi-modal MRI registration.

The optimal template effect in hippocampus studies of diseased populations

We evaluate the impact of template choice on template-based segmentation of the hippocampus in epilepsy. Four dataset-specific strategies are quantitatively contrasted: the “closest to average” individual template, the average shape version of the closest to average template, a best appearance template and the best appearance and shape template proposed here and implemented in the open source toolkit Advanced Normalization Tools (ANTS). The cross-correlation similarity metric drives the correspondence model and is used consistently to determine the optimal appearance. Minimum shape distance in the diffeomorphic space determines optimal shape. Our evaluation results show that, with respect to gold-standard manual labeling of hippocampi in epilepsy, optimal shape and appearance template construction outperforms the other strategies for gaining data-derived templates. Our results also show the improvement is most significant on the diseased side and insignificant on the healthy side. Thus, the importance of the template increases when used to study pathology and may be less critical for normal control studies. Furthermore, explicit geometric optimization of the shape component of the unbiased template positively impacts the study of diseased hippocampi.

Advanced Normalization Tools: V1.0

We provide examples and highlights of Advanced Normalization Tools (ANTS) that address practical problems in real data. A variety of image and point similarity metrics and elastic, diffeomorphic, affine and other variations of transformation models are available.