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

Autor:	Armanious, Karim, Abdulatif, Sherif, Bhaktharaguttu, Anish Rao, Küstner, Thomas, Hepp, Tobias, Gatidis, Sergios, Yang, Bin
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.23919/Eusipco47968.2020.9287398
Popis:	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. Comment: Submitted to IEEE EUSIPCO 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.06271 Zobrazit plný text záznamu View this record from Arxiv