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Journal of Digital Imaging

pp 1–11

First Online: 02 June 2017DOI: 10.1007-s10278-017-9983-4

Cite this article as: Akkus, Z., Galimzianova, A., Hoogi, A. et al. J Digit Imaging 2017. doi:10.1007-s10278-017-9983-4

Abstract

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

KeywordsDeep learning Quantitative brain MRI Convolutional neural network Brain lesion segmentation Zeynettin Akkus and Alfiia Galimzianova have contributed equally to this work.





Autor: Zeynettin Akkus - Alfiia Galimzianova - Assaf Hoogi - Daniel L. Rubin - Bradley J. Erickson

Fuente: https://link.springer.com/



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