基于深度学习的多参数磁共振成像胶质瘤脑肿瘤分割技术:临床应用综述与未来展望》。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Delaram J Ghadimi, Amir M Vahdani, Hanie Karimi, Pouya Ebrahimi, Mobina Fathi, Farzan Moodi, Adrina Habibzadeh, Fereshteh Khodadadi Shoushtari, Gelareh Valizadeh, Hanieh Mobarak Salari, Hamidreza Saligheh Rad
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引用次数: 0

摘要

这篇综述探讨了深度学习(DL)在使用多参数磁共振成像(MRI)数据进行胶质瘤分割中的作用。研究调查了多参数磁共振成像等先进技术,以捕捉神经胶质瘤的复杂性质。研究深入探讨了 DL 与 MRI 的整合,重点关注卷积神经网络 (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、治疗反应监测以及区分肿瘤进展和假性进展。此外,综述还探讨了基于 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。文中还讨论了数据质量、梯度消失和模型可解释性方面的挑战。综述最后提出了对未来研究方向的见解,强调了解决肿瘤异质性、整合基因组数据以及确保负责任地部署 DL 驱动的医疗保健技术的重要性。证据等级:不适用 技术效率:第 2 阶段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical Applications and Future Outlooks.

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.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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