Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany.
Department of Orthopedics, Trauma and Plastic Surgery, University Hospital Leipzig, Leipzig, Germany.
World Neurosurg. 2022 Oct;166:60-70. doi: 10.1016/j.wneu.2022.07.041. Epub 2022 Jul 19.
Convolutional neural networks (CNNs) are being increasingly used in the medical field, especially for image recognition in high-resolution, large-volume data sets. The study represents the current state of research on the application of CNNs in image segmentation and pathology detection in spine magnetic resonance imaging.
For this systematic literature review, the authors performed a systematic initial search of the PubMed/Medline and Web of Science (Core collection) databases for eligible investigations. The authors limited the search to observational studies. Outcome parameters were analyzed according to the inclusion criteria and assigned to 3 groups: 1) segmentation of anatomical structures, 2) segmentation and evaluation of pathologic structures, and 3) specific implementation of CNNs.
Twenty-four retrospectively designed articles met the inclusion criteria. Publication dates ranged from 2017 to 2021. In total, 14,065 patients with 113,110 analyzed images were included. Most authors trained their network with a training-to-testing ratio of 80/20, while all but 2 articles used 5- to 10-fold cross-validation. Nine articles compared their performance results with other neural networks and algorithms, and all 24 articles described outcomes as positive.
State-of-the-art CNNs can detect and segment-specific anatomical landmarks and pathologies across a wide range, comparable to the skills of radiologists and experienced clinicians. With rapidly evolving network architectures and growing medical image databases, the future is likely to show growth in the development and refinement of these capable networks. However, the aid of automated segmentation and classification by neural networks cannot and should not be expected to replace clinical experts.
卷积神经网络（CNNs）在医学领域的应用越来越广泛，特别是在高分辨率、大容量数据集的图像识别方面。本研究代表了卷积神经网络在脊柱磁共振成像中图像分割和病理学检测应用的研究现状。
为了进行这项系统综述，作者对 PubMed/Medline 和 Web of Science（核心合集）数据库进行了系统的初步检索，以查找符合条件的研究。作者将搜索范围限定为观察性研究。根据纳入标准分析结果参数，并将其分为 3 组：1）解剖结构的分割，2）病理结构的分割和评估，3）CNN 的具体实施。
符合纳入标准的回顾性设计文章有 24 篇。发表日期范围为 2017 年至 2021 年。总共纳入了 14065 名患者的 113110 张分析图像。大多数作者将其网络的训练-测试比例设定为 80/20，而除了 2 篇文章之外，所有文章都使用了 5-10 倍的交叉验证。有 9 篇文章将其性能结果与其他神经网络和算法进行了比较，所有 24 篇文章都描述了阳性结果。
最先进的 CNN 可以检测和分割广泛范围内的特定解剖标志和病变，与放射科医生和经验丰富的临床医生的技能相当。随着网络架构的快速发展和医学图像数据库的不断增长，这些功能强大的网络的开发和改进可能会在未来得到进一步发展。然而，神经网络的自动分割和分类辅助不应也不能被期望替代临床专家。
