Deep learning in structural and functional lung image analysis.
Deep learning in structural and functional lung image analysis.
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在结构和功能性肺图像分析中深入学习。
DOI：
10.1259/bjr.20201107
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发表时间：
2022-04-01
期刊：
影响因子：
--
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文献类型：
--
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最近深度学习（DL）的复兴极大地影响了医学成像领域。医学图像分析应用一直处于DL研究工作的最前沿，应用于多种疾病和器官，包括肺部。本综述的目的是双重的：（i）简要概述DL理论，因为它涉及到肺图像分析;（ii）系统地回顾DL研究文献有关的肺图像分析应用的分割，重建，配准和合成。该综述按照系统性综述和荟萃分析的首选报告项目指南进行。从文献检索中初步识别出479项研究，其中82项研究符合合格标准。分割是最常见的肺部图像分析DL应用（65.9%的综述论文）。DL在应用于整个肺部和其他肺部结构的分割时显示出令人印象深刻的结果。DL在图像配准、重建和合成方面也显示出巨大的应用潜力。然而，大多数已发表的研究仅限于结构性肺成像，只有12.9%的综述研究采用功能性肺成像模式，因此突出了该领域进一步研究的重要机会。尽管DL在肺部影像分析中的应用领域正在迅速扩大，但在临床肺部成像工作流程中广泛采用DL之前，需要解决对不一致的验证和评价策略、研究中心间的普遍性、方法细节的透明度和可解释性的担忧。
The recent resurgence of deep learning (DL) has dramatically influenced the medical imaging field. Medical image analysis applications have been at the forefront of DL research efforts applied to multiple diseases and organs, including those of the lungs. The aims of this review are twofold: (i) to briefly overview DL theory as it relates to lung image analysis; (ii) to systematically review the DL research literature relating to the lung image analysis applications of segmentation, reconstruction, registration and synthesis. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. 479 studies were initially identified from the literature search with 82 studies meeting the eligibility criteria. Segmentation was the most common lung image analysis DL application (65.9% of papers reviewed). DL has shown impressive results when applied to segmentation of the whole lung and other pulmonary structures. DL has also shown great potential for applications in image registration, reconstruction and synthesis. However, the majority of published studies have been limited to structural lung imaging with only 12.9% of reviewed studies employing functional lung imaging modalities, thus highlighting significant opportunities for further research in this field. Although the field of DL in lung image analysis is rapidly expanding, concerns over inconsistent validation and evaluation strategies, intersite generalisability, transparency of methodological detail and interpretability need to be addressed before widespread adoption in clinical lung imaging workflow.
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