Organ-based Chronological Age Estimation based on 3D MRI Scans
This work addresses the need for more accurate health state assessments by providing organ-specific age estimates, which is incremental as it builds on existing MRI-based regression networks with architectural improvements.
The authors tackled the problem of estimating chronological age from 3D MRI scans by developing a deep learning architecture for organ-based age estimation, achieving superior performance compared to existing methods in brain and knee age estimation tasks.
Individuals age differently depending on a multitude of different factors such as lifestyle, medical history and genetics. Often, the global chronological age is not indicative of the true ageing process. An organ-based age estimation would yield a more accurate health state assessment. In this work, we propose a new deep learning architecture for organ-based age estimation based on magnetic resonance images (MRI). The proposed network is a 3D convolutional neural network (CNN) with increased depth and width made possible by the hybrid utilization of inception and fire modules. We apply the proposed framework for the tasks of brain and knee age estimation. Quantitative comparisons against concurrent MR-based regression networks and different 2D and 3D data feeding strategies illustrated the superior performance of the proposed work.