Review of deep learning-driven MRI brain tumor detection and segmentation methods

Rong Zhang, Hongliang Luo, Weijie Chen, Yongqiang Bai
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Abstract

The application of deep learning in the field of medical imaging has become increasingly widespread, greatly promoting the advancement and development of Magnetic Resonance Imaging (MRI) brain tumor detection and segmentation techniques. Therefore, a comprehensive review of deep learning-based methods for MRI brain tumor detection and segmentation was conducted. This review introduces the basic concepts of brain tumors and MRI brain tumor detection and segmentation, discusses the specific applications and typical methods of deep learning in MRI brain tumor detection and segmentation, and analyzes and compares the performance and advantages and disadvantages of different methods. Additionally, representative brain tu-mor segmentation dataset (BraTS) and its evaluation metrics are introduced, upon which the performance of various deep learning-based brain tumor segmentation methods on the BraTS 2019-2022 dataset is compared. Lastly, the challenges and future development trends in deep learning-based MRI brain tumor detection and segmentation methods are summarized and anticipated.
深度学习驱动的MRI脑肿瘤检测与分割方法综述
深度学习在医学影像领域的应用日益广泛,极大地推动了磁共振成像(MRI)脑肿瘤检测与分割技术的进步与发展。因此,本文对基于深度学习的MRI脑肿瘤检测与分割方法进行了全面综述。本文介绍了脑肿瘤和MRI脑肿瘤检测与分割的基本概念,讨论了深度学习在MRI脑肿瘤检测与分割中的具体应用和典型方法,并对不同方法的性能和优缺点进行了分析比较。此外,介绍了具有代表性的脑肿瘤分割数据集(BraTS)及其评价指标,并在此基础上比较了各种基于深度学习的脑肿瘤分割方法在BraTS 2019-2022数据集上的性能。最后,对基于深度学习的MRI脑肿瘤检测与分割方法面临的挑战和未来发展趋势进行了总结和展望。
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