CT肝脏结构的语义细分:对最近趋势的系统审查和文献分析:基于神经网络的肝脏语义细分方法
Jessica C Delmoral1, João Manuel R S Tavares2
1Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
Journal of medical systems
|October 14, 2024
概括
医疗图像中细分肝脏结构的人工智能 (AI) 正在迅速发展. 全自动混合网络在肝脏细分方面表现出色,而生成人工智能在瘤和血管系统方面表现有前途,尽管在小型结构方面需要改进.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 医疗图像细分的AI是一个不断增长的研究领域.
- 以前的研究表明,人工智能工具在肝脏细分中的性能各不相同.
- 缺乏对这个领域的系统概述.
研究的目的:
- 在CT图像中对肝脏结构细分进行AI,特别是深度学习的系统审查和文献分析.
- 根据算法特征概述研究方向.
- 评估算法建模目标,性能基准和模型复杂性.
主要方法:
- 对相关出版物的系统和文献计量审查.
- 专注于CT扫描中肝脏结构的全自动语义细分.
- 对算法特征,性能和复杂性的分析.
主要成果:
- 全自动混合2D和3D网络是一般肝脏细分的最佳表现.
- 全自动生成方法在肝脏瘤和血管细分方面表现最好.
- 目前的基准指标表明,在高分辨率CT扫描中细分小结构方面还有改进的余地.
结论:
- 混合2D/3D网络在肝脏细分方面处于领先地位.
- 生成性人工智能对复杂的肝脏结构,如瘤和血管系统有希望.
- 需要进一步的研究来提高人工智能的准确性,用于腹部CT成像中的小,详细的结构.
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