识别白质纤维束的深度学习方法:最新进展和未来展望。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Nayereh Ghazi, Mohammad Hadi Aarabi, Hamid Soltanian-Zadeh
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引用次数: 2

摘要

从扩散磁共振成像(dMRI)数据中定量分析白质纤维束在健康和疾病方面具有重要意义。例如,术前和治疗计划中需要对与解剖意义纤维束相关的纤维束进行分析,手术结果取决于对所需纤维束的准确分割。目前,这一过程主要是通过神经解剖学专家进行耗时的人工鉴定来完成的。然而,人们对自动化管道有着广泛的兴趣,这样它就可以快速,准确,易于在临床环境中应用,并且还可以消除读取器内的可变性。随着使用深度学习技术的医学图像分析的进步,人们对使用这些技术进行通道识别的兴趣也越来越大。最近关于该应用的报告表明,基于深度学习的通道识别方法优于现有的最先进的方法。本文综述了目前基于深度神经网络的气道识别方法。首先,我们回顾了最近用于通道识别的深度学习方法。接下来,我们比较它们的性能、训练过程和网络属性。最后,我们对未来工作的开放挑战和可能的方向进行了批判性的讨论。
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 Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective.

Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective.

Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly demanded in pre-surgical and treatment planning, and the surgery outcome depends on accurate segmentation of the desired tracts. Currently, this process is mainly done through time-consuming manual identification performed by neuro-anatomical experts. However, there is a broad interest in automating the pipeline such that it is fast, accurate, and easy to apply in clinical settings and also eliminates the intra-reader variabilities. Following the advancements in medical image analysis using deep learning techniques, there has been a growing interest in using these techniques for the task of tract identification as well. Recent reports on this application show that deep learning-based tract identification approaches outperform existing state-of-the-art methods. This paper presents a review of current tract identification approaches based on deep neural networks. First, we review the recent deep learning methods for tract identification. Next, we compare them with respect to their performance, training process, and network properties. Finally, we end with a critical discussion of open challenges and possible directions for future works.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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