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Review

Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa
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Author to whom correspondence should be addressed.
Academic Editor: Leonardo Rundo
Received: 23 November 2020 / Revised: 7 January 2021 / Accepted: 11 January 2021 / Published: 29 January 2021
(This article belongs to the Special Issue Advanced Computational Methods for Oncological Image Analysis)
Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis. View Full-Text
Keywords: brain tumor segmentation; deep learning; magnetic resonance imaging; survey brain tumor segmentation; deep learning; magnetic resonance imaging; survey
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MDPI and ACS Style

Magadza, T.; Viriri, S. Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art. J. Imaging 2021, 7, 19. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020019

AMA Style

Magadza T, Viriri S. Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art. Journal of Imaging. 2021; 7(2):19. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020019

Chicago/Turabian Style

Magadza, Tirivangani; Viriri, Serestina. 2021. "Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art" J. Imaging 7, no. 2: 19. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7020019

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