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

• Published in: Journal of imaging Vol. 7; no. 2; p. 19
• Main Authors: Magadza, Tirivangani, Viriri, Serestina
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29.01.2021; MDPI
• Subjects: Algorithms; Artificial intelligence; Brain; Brain cancer; Brain research; brain tumor segmentation; Datasets; Deep learning; Image analysis; Image segmentation; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Quantitative analysis; Review; State-of-the-art reviews; survey; Tumors
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging7020019

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.