Predi\c{c}\~ao da Idade Cerebral a partir de Imagens de Resson\^ancia Magn\'etica utilizando Redes Neurais Convolucionais

TL;DR

This paper investigates deep learning methods, specifically 3D and 2D convolutional neural networks, to predict brain age from MRI images, aiming to identify biomarkers for aging and neurodegenerative processes.

Contribution

It compares 3D and 2D CNN approaches for brain age prediction, finding the 2D model with slice fusion achieves the lowest error, advancing neuroimaging biomarker detection.

Findings

2D CNN with slice fusion achieved MAE of 3.83 years

The 2D approach outperformed the 3D model in accuracy

Deep learning can effectively predict brain age from MRI images

Abstract

In this work, deep learning techniques for brain age prediction from magnetic resonance images are investigated, aiming to assist in the identification of biomarkers of the natural aging process. The identification of biomarkers is useful for detecting an early-stage neurodegenerative process, as well as for predicting age-related or non-age-related cognitive decline. Two techniques are implemented and compared in this work: a 3D Convolutional Neural Network applied to the volumetric image and a 2D Convolutional Neural Network applied to slices from the axial plane, with subsequent fusion of individual predictions. The best result was obtained by the 2D model, which achieved a mean absolute error of 3.83 years. -- Neste trabalho s\~ao investigadas t\'ecnicas de aprendizado profundo para a predi\c{c}\~ao da idade cerebral a partir de imagens de resson\^ancia magn\'etica, visando auxiliar na identifica\c{c}\~ao de biomarcadores do processo natural de envelhecimento. A identifica\c{c}\~ao de biomarcadores \'e \'util para a detec\c{c}\~ao de um processo neurodegenerativo em est\'agio inicial, al\'em de possibilitar prever um decl\'inio cognitivo relacionado ou n\~ao \`a idade. Duas t\'ecnicas s\~ao implementadas e comparadas neste trabalho: uma Rede Neural Convolucional 3D aplicada na imagem volum\'etrica e uma Rede Neural Convolucional 2D aplicada a fatias do plano axial, com posterior fus\~ao das predi\c{c}\~oes individuais. O melhor resultado foi obtido pelo modelo 2D, que alcan\c{c}ou um erro m\'edio absoluto de 3.83 anos.