Dr. Stone and Dr. Tustison Work on ANTs and FreeSurfer Cortical Thickness Measurements Project
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 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 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.
James R. Stone, MD, PhD, is an Associate Professor of Radiology and Medical Imaging at the University of Virginia. Dr. Stone’s laboratory currently explores questions related to improving the clinical diagnosis of this disease process. Ongoing work includes the design and application of novel molecular imaging probes for the detection of acute and chronic effects of TBI. He is also involved with exploring novel ultrasound based approaches for the detection of TBI in a far forward environment.
Nick Tustison, DSc, is an Assistant Professor of Radiology and Medical Imaging at the University of Virginia. As a member of the UVA Medical Imaging Research Department, Nick develops computational techniques for facilitating quantitation of anatomical and/or functional variability in different populations or over time as a consequence of disease or injury. Areas of active research include various projects with neuroradiology faculty, collaborative pulmonary research using hyperpolarized gases and the open source dissemination of useful image analysis algorithms.