计算机断层扫描图像中主动脉分割的深度学习模型:系统回顾与元分析

IF 1.6 4区 医学 Q4 ENGINEERING, BIOMEDICAL Journal of Medical and Biological Engineering Pub Date : 2024-07-23 DOI:10.1007/s40846-024-00881-9
Ting-Wei Wang, Yun-Hsuan Tzeng, Jia-Sheng Hong, Ho-Ren Liu, Kuan-Ting Wu, Hao-Neng Fu, Yung-Tsai Lee, Wei-Hsian Yin, Yu-Te Wu
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引用次数: 0

摘要

目的 本系统综述和荟萃分析旨在评估深度学习(DL)模型对计算机断层扫描(CT)图像中主动脉分割的有用性。方法 根据 2020 年的 PRISMA 指南,我们系统地检索了 PubMed、Embase 和 Web of Science 上截至 2024 年 3 月 13 日发表的使用 DL 模型对成人胸部 CT 图像中主动脉进行分割的研究。我们排除了未使用 DL 模型、涉及非人类受试者或主动脉疾病(动脉瘤和动脉离断)或缺乏荟萃分析所需的基本数据的研究。分割性能主要根据 Dice 分数进行评估。我们对 16 项研究进行了回顾,结果表明 DL 模型的分割准确率很高,综合 Dice 得分为 96%。结论DL模型有助于CT图像中的主动脉分割,因此可以指导心血管疾病准确、高效、标准化的诊断和治疗计划。未来的研究应解决目前面临的挑战,以提高模型的通用性并评估临床效益,从而扩大 DL 模型在临床实践中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Deep Learning Models for Aorta Segmentation in Computed Tomography Images: A Systematic Review And Meta-Analysis

Purpose

This systematic review and meta-analysis was conducted to evaluate the usefulness of deep learning (DL) models for aorta segmentation in computed tomography (CT) images.

Methods

Adhering to 2020 PRISMA guidelines, we systematically searched PubMed, Embase, and Web of Science for studies published up to March 13, 2024, that used DL models for aorta segmentation in adults’ chest CT images. We excluded studies that did not use DL models, involved nonhuman subjects or aortic diseases (aneurysms and dissections), or lacked essential data for meta-analysis. Segmentation performance was evaluated primarily in terms of Dice scores. Subgroup analyses were performed to identify variations related to geographical location and methodology.

Results

Our review of 16 studies indicated that DL models achieve high segmentation accuracy, with a pooled Dice score of 96%. We further noted geographical variations in model performance but no significant publication bias, according to the Egger test.

Conclusion

DL models facilitate aorta segmentation in CT images, and they can therefore guide accurate, efficient, and standardized diagnosis and treatment planning for cardiovascular diseases. Future studies should address the current challenges to enhance model generalizability and evaluate clinical benefits and thus expand the application of DL models in clinical practice.

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来源期刊
CiteScore
4.30
自引率
5.00%
发文量
81
审稿时长
3 months
期刊介绍: The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.
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