Abstract：
This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL-based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo-progression. Furthermore, the review examines the evolution of DL-based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL-driven healthcare technologies. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 2.
摘要：
这篇综合综述探讨了深度学习（DL）在使用多参数磁共振成像（MRI）数据进行神经胶质瘤分割中的作用。该研究调查了诸如多参数MRI之类的先进技术，以捕获神经胶质瘤的复杂性质。它深入研究了DL与MRI的整合，专注于卷积神经网络(CNN)及其在肿瘤分割方面的卓越能力。重点介绍了基于DL的分割的临床应用，包括治疗计划，监测治疗反应，并区分肿瘤进展和假性进展。此外，这篇综述考察了基于DL的分割研究的演变，从早期的CNN模型到最近的进步，如注意力机制和变换器模型。数据质量方面的挑战，渐变消失，并对模型的可解释性进行了讨论。这篇综述总结了对未来研究方向的见解，强调解决肿瘤异质性的重要性，整合基因组数据，并确保负责任地部署DL驱动的医疗技术。证据级别：不适用技术效率：第二阶段。
