Table1_Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.xlsx

IntroductionImage segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT).MethodThe literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review.ResultsThe majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85–0.9.DiscussionDeep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.

引言：图像分割是量化恶性骨病变特征的重要过程，但对于放射科医生而言，这一任务既具挑战性又极为繁重。深度学习在自动化放射学图像分割方面展现出巨大潜力，包括对恶性骨病变的分割。本综述旨在探讨基于深度学习的图像分割方法，应用于计算机断层扫描（CT）、磁共振成像（MRI）以及正电子发射断层扫描/计算机断层扫描（PET/CT）中的恶性骨病变。方法：遵循系统评价和荟萃分析优先报告项目（PRISMA）指南，在PubMed、Embase、Web of Science和Scopus电子数据库中进行了基于深度学习的CT和MRI上恶性骨病变图像分割的文献检索。共纳入2017年2月至2023年3月间发表的41篇原创文章。结果：大多数研究集中在MRI，其次是CT、PET/CT和PET/MRI。研究原发性与继发性恶性肿瘤，以及利用三维与二维数据的论文分布相对均衡。许多论文使用自定义构建的模型，作为U-Net的修改或变体。最常见的评估指标是dice相似系数（DSC）。大多数模型实现的DSC超过0.6，所有成像模态的中位数介于0.85至0.9之间。讨论：深度学习方法在分割CT、MRI和PET/CT上的恶性骨病变方面展现出巨大的潜力。一些常用的策略包括数据增强、利用大型公开数据集、预处理（包括去噪和裁剪）以及U-Net架构的改进。未来的研究方向包括克服数据集和注释的同质性，以及实现临床应用的可推广性。