Semantic Scholar Open Access 2017 999 sitasi

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Z. Akkus A. Galimzianova A. Hoogi D. Rubin B. Erickson

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Abstrak

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.

Topik & Kata Kunci

Computer Science Medicine

Penulis (5)

Z. Akkus

A. Galimzianova

A. Hoogi

D. Rubin

B. Erickson

Format Sitasi

APA MLA BibTeX

Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D., Erickson, B. (2017). Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. https://doi.org/10.1007/s10278-017-9983-4

Akses Cepat

Lihat di Sumber doi.org/10.1007/s10278-017-9983-4

Informasi Jurnal

Tahun Terbit: 2017
Bahasa: en
Total Sitasi: 999×
Sumber Database: Semantic Scholar
DOI: 10.1007/s10278-017-9983-4
Akses: Open Access ✓