Machine learning and deep learning for brain tumor MRI image segmentation.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2023-11-01 Epub Date: 2023-12-16 DOI:10.1177/15353702231214259
Md Kamrul Hasan Khan, Wenjing Guo, Jie Liu, Fan Dong, Zoe Li, Tucker A Patterson, Huixiao Hong
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Abstract

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.

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用于脑肿瘤 MRI 图像分割的机器学习和深度学习。
脑肿瘤通常是致命的。因此,准确的脑肿瘤图像分割对于这些肿瘤患者的诊断、治疗和监测至关重要。磁共振成像(MRI)是捕捉脑部图像的常用成像技术。机器学习和深度学习技术都是分析核磁共振成像图像的常用方法。本文回顾了一些用于脑肿瘤 MRI 图像分割的常用机器学习和深度学习技术。文章讨论了所综述的机器学习和深度学习方法的局限性和优势。尽管这些方法中的每一种在各自的领域都有公认的地位,但目前将两种或多种技术结合起来是一种新兴趋势。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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