Datasheet1_Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.docx

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.

图像分割是量化恶性骨病变特征的重要步骤，然而，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，包括恶性骨病变的分割。本综述旨在探讨基于深度学习的恶性骨病变在计算机断层扫描（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）中的图像分割方法。研究方法：在PubMed、Embase、Web of Science和Scopus电子数据库中，遵循系统评价和Meta分析优先报告项目（PRISMA）指南，对基于深度学习的CT和MRI上恶性骨病变的图像分割文献进行了检索。共纳入2017年2月至2023年3月间发表的41篇原始研究论文。研究结果：大多数论文研究的是MRI，其次是CT、PET/CT和PET/MRI。关于原发性与继发性恶性肿瘤的研究论文分布相对均衡，同样，在3维与2维数据方面的研究也呈现相对均匀的分布。许多论文采用定制的模型，作为U-Net的修改或变体。评价中最常用的指标是dice相似系数（DSC）。大多数模型达到了DSC值高于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CT、MRI和PET/CT上分割恶性骨病变方面展现出巨大的潜力。一些常用的策略以帮助提升性能包括数据增强、利用大型公开数据集、预处理（包括降噪和裁剪）以及U-Net架构的修改。未来的研究方向包括克服数据集和标注的同质性，以及实现临床应用的可推广性。