Organ-based Chronological Age Estimation based on 3D MRI Scans

TL;DR

This paper introduces a novel 3D CNN architecture utilizing inception and fire modules for organ-specific age estimation from MRI scans, aiming to improve health assessment accuracy over traditional methods.

Contribution

It presents a new deep learning model specifically designed for organ-based age estimation using MRI, demonstrating superior performance over existing methods.

Findings

Outperforms existing MR-based regression networks

Effective for brain and knee age estimation

Utilizes hybrid 3D CNN architecture

Abstract

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.