对3D计算机断层扫描的自动细分进行系统审查,用于体积体构分析
Dinh Van Chi Mai1,2, Ioanna Drami1,3, Edward T Pring1,2
1Department of Surgery, St Mark's Academic Institute, St Mark's Hospital, London, UK.
Journal of cachexia, sarcopenia and muscle
|August 10, 2023
概括
使用深度学习 (DL) 进行自动化3DCT扫描细分,可快速分析身体组成. 然而,大多数研究缺乏全身覆盖和标准化验证,需要进一步研究以获得准确的临床应用.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 生物医学数据分析
背景情况:
- 自动计算机断层扫描 (CT) 扫描细分正在推进,使三维 (3D) 分析超出单片.
- 来自CT扫描的体积结构 (BC) 分析对于临床研究和患者评估至关重要.
- 目前的自动化细分方法在准确性,标准化和解剖覆盖方面存在局限性.
研究的目的:
- 系统地审查自动化3DCT扫描细分的可行性和准确性,用于体积BC分析.
- 为研究人员和临床医生确定自动化细分技术的当前局限性和潜在陷.
- 评估深度学习 (DL) 在自动体积BC分析中的作用.
主要方法:
- 在2021年10月之前对OVID Medline,Embase和灰色文献进行系统的文献搜索.
- 包括来自CT扫描的自动化骨肌肉,内脏和皮下脂肪组织 (AT) 分段的原始研究.
- 对细分算法,培训数据,CT覆盖率,性能指标 (迪斯相似系数) 和验证方法的分析.
主要成果:
- 七项研究符合纳入标准,在基础真相细分,患者队列和CT获取参数方面具有显著的异质性.
- 七项研究中的六项报告了优异的细分性能 (Dice > 0.9),与传统方法 (25 分钟) 相比,深度学习 (DL) 算法实现了快速细分 (12-75 秒).
- 只有四种算法经过了内部测试,三种被外部验证;大多数研究没有覆盖整个身体,解剖覆盖的报道是不一致的.
结论:
- 深度学习使得从临床上指示的CT扫描中快速自动化体积BC分析.
- 需要进一步的研究来验证常见的CT扫描区域来估计全身BC,并与单个腰部切片分析进行比较.
- 标准化的准确性和精确度指标,以及关于基准准确性标签和大型多中心数据集的共识,对于可靠的自动化BC分析至关重要.
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