Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation

Abstract

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration‐based cortical thickness (DiReCT) is a known technique to derive such measures from non‐surface‐based volumetric tissue maps. ANTs provides an open‐source method for estimating cortical thickness, derived by applying DiReCT to an atlas‐based segmentation. In this paper, we propose DL+DiReCT, a method using high‐quality deep learning‐based neuroanatomy segmentations followed by DiReCT, yielding accurate and reliable cortical thickness measures in a short time. We evaluate the methods on two independent datasets and compare the results against surface‐based measures from FreeSurfer. Good correlation of DL+DiReCT with FreeSurfer was observed (r = .887) for global mean cortical thickness compared to ANTs versus FreeSurfer (r = .608). Experiments suggest that both DiReCT‐based methods had higher sensitivity to changes in cortical thickness than Freesurfer. However, while ANTs showed low scan‐rescan robustness, DL+DiReCT showed similar robustness to Freesurfer. Effect‐sizes for group‐wise differences of healthy controls compared to individuals with dementia were highest with the deep learning‐based segmentation. DL+DiReCT is a promising combination of a deep learning‐based method with a traditional registration technique to detect subtle changes in cortical thickness.

The ANTsX ecosystem for quantitative biological and medical imaging

“…This structural processing pipeline is currently available as open-source within the ANTsXNet libraries which underwent a thorough evaluation using both cross-sectional and longitudinal data and discussed within the context of our previous evaluation of our classical ANTs pipelines 20,33 . Note that related work has been recently reported by external groups 34,35 . Fortunately, these overlapping contributions provide a context for comparison to simultaneously motivate the utility of the ANTsX ecosystem and to editorialize with respect to best practices in the field.…”

“…As we have explained previously, 66 we find these evaluation measures to be much more useful than other commonly applied criteria as they are closer to assessing the actual utility of these thickness measurements as biomarkers for disease 21 or growth. In recent work 39 the authors employ correlation with FreeSurfer thickness values as the primary evaluation for assessing relative performance with ANTs cortical thickness. 66 This evaluation, unfortunately, is fundamentally flawed in that it is a prime example of a type of circularity analysis 29 whereby data selection is driven by the same criteria used to evaluate performance.…”

“…It is the comprehensiveness of ANTsX that provides significant advantages over much of the deep learning work that is currently taking place in medical imaging and related fields. For example, related work 34 also built a similar pipeline and assessed performance. However, due to the lack of a complete processing and analysis framework, training data was generated using the FreeSurfer stream, deep learning-based brain segmentation employed DeepSCAN 50 (in-house software), and cortical thickness estimation 19 used the ANTs toolkit.…”

“…In other words, various steps in the deep learning training processing (e.g., data augmentation, preprocessing) can all be performed within the same ecosystem where such important details as header information for image geometry are treated the same. In contrast, related work 39 described and evaluated a similar thickness measurement pipeline. However, due to the lack of a complete processing and analysis framework, training data was generated using the FreeSurfer stream, deep learning-based brain segmentation employed DeepSCAN 27 (in-house software), and cortical thickness estimation 57 was generated using the ANTs toolkit.…”

“…Evaluations using both cross-sectional and longitudinal data are described in subsequent sections and couched within the context of our previous publications. 51,66 Related work has been recently reported by external groups 38,39 and provides a context for comparison to motivate the utility of the ANTsX ecosystem.…”