基于CT图像的腹腔大动脉动脉瘤中内血栓的自动细分:基于深度学习的方法的全面审查
Jia Guo1,2, Fabien Lareyre3,4, Sébastien Goffart1
1Clinical Chemistry Laboratory, University Hospital of Nice, 06107 Nice, France.
Journal of clinical medicine
|December 11, 2025
概括
深度学习 (DL) 技术在分析腹腔大动脉瘤 (AAA) 图像方面表现有前途,特别是在细分内血栓 (ILT) 方面. 先进的DL模型为手术规划提供了更高的准确性,尽管更广泛的临床使用需要标准化数据.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心血管外科心血管外科
背景情况:
- 腹腔大动脉瘤 (AAA) 影像分析对于诊断和治疗规划至关重要.
- 内血栓 (ILT) 细分是AAA成像中的一个关键挑战.
- 深度学习 (DL) 为自动化和改进ILT细分提供了潜力.
研究的目的:
- 审查深度学习 (DL) 技术在腹腔大动脉瘤 (AAA) 影像分析中的应用.
- 专注于使用DL对内血栓 (ILT) 的细分.
- 评估基于DL的ILT细分的临床使用性能和适用性.
主要方法:
- 关于PUBMED和Web of Science的综合文献审查,截至2025年9月.
- 在AAA患者的计算机断层扫描血管学 (CTA) 中,包括使用DL进行ILT细分的英语研究.
- 选了664篇文章,其中22篇研究符合分析资格标准.
主要成果:
- 对于ILT细分,DL网络实现了高的Dice相似系数 (2D:0.81-0.93;3D:0.804-0.9868).
- 2D多视图融合模型的性能优于其他2DDL方法; 3D U-Net作为一个强大的基线.
- 手术前的DL细分显示了手术规划的实用性,而手术后的细分由于支架器件的缺陷而面临挑战.
结论:
- 基于DL的ILT细分方法正在推进AAA成像分析和手术前规划.
- DL提供了改进术后监测的潜力,但成像文物存在挑战.
- 标准化数据集和临床工作流集成对于未来的DL开发和AAA管理中的验证至关重要.
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