Age-Net: An MRI-Based Iterative Framework for Brain Biological Age
Estimation
- URL: http://arxiv.org/abs/2009.10765v2
- Date: Mon, 15 Mar 2021 15:48:23 GMT
- Title: Age-Net: An MRI-Based Iterative Framework for Brain Biological Age
Estimation
- Authors: Karim Armanious, Sherif Abdulatif, Wenbin Shi, Shashank Salian, Thomas
K\"ustner, Daniel Weiskopf, Tobias Hepp, Sergios Gatidis, Bin Yang
- Abstract summary: The concept of biological age (BA) is hard to grasp mainly due to the lack of a clearly defined reference standard.
We propose a new imaging-based framework for organ-specific BA estimation.
- Score: 18.503467872057424
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The concept of biological age (BA), although important in clinical practice,
is hard to grasp mainly due to the lack of a clearly defined reference
standard. For specific applications, especially in pediatrics, medical image
data are used for BA estimation in a routine clinical context. Beyond this
young age group, BA estimation is mostly restricted to whole-body assessment
using non-imaging indicators such as blood biomarkers, genetic and cellular
data. However, various organ systems may exhibit different aging
characteristics due to lifestyle and genetic factors. Thus, a whole-body
assessment of the BA does not reflect the deviations of aging behavior between
organs. To this end, we propose a new imaging-based framework for
organ-specific BA estimation. In this initial study, we focus mainly on brain
MRI. As a first step, we introduce a chronological age (CA) estimation
framework using deep convolutional neural networks (Age-Net). We quantitatively
assess the performance of this framework in comparison to existing
state-of-the-art CA estimation approaches. Furthermore, we expand upon Age-Net
with a novel iterative data-cleaning algorithm to segregate atypical-aging
patients (BA $\not \approx$ CA) from the given population. We hypothesize that
the remaining population should approximate the true BA behavior. We apply the
proposed methodology on a brain magnetic resonance image (MRI) dataset
containing healthy individuals as well as Alzheimer's patients with different
dementia ratings. We demonstrate the correlation between the predicted BAs and
the expected cognitive deterioration in Alzheimer's patients. A statistical and
visualization-based analysis has provided evidence regarding the potential and
current challenges of the proposed methodology.
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