基于弱监督学习的2D超声波图像的3D膀重建,用于膀体积测量
Zhao Peng1,2, Hongming Shan3,4, Xiaoyu Yang1,2
1Department of Oncology, Xiangya Hospital, Central South University, Changsha, China.
Medical physics
|July 24, 2023
概括
这项研究引入了一种新的3D膀重建方法,使用弱监督学习来准确地从超声波图像中测量膀体积. 与传统方法和商业算法相比,该技术显著提高了准确性.
科学领域:
- 医疗成像医学成像
- 辐射瘤学 辐射瘤学
- 人工智能的人工智能
背景情况:
- 精确的膀体积测量对于在盆腔瘤放射治疗中保持患者解剖学一致至关重要.
- 由于解剖学变异,传统的2D超声波方法会产生不准确的膀体积测量.
研究的目的:
- 为了提高2D超声波图像的膀体积测量的精度,用于接受盆腔瘤辐射治疗的患者.
主要方法:
- 一个基于弱监督学习的3D膀重建神经网络是使用130名患者的回顾性超声数据开发的.
- 设计了一种新的损失函数,包括监督细分和膀紧性.
- 膀体积是通过声素计数来计算的,准确度是通过CT扫描对子相似系数 (DSC) 和相对标准偏差 (RSD) 来评估的.
主要成果:
- 该方法通过12张超声波图像获得了0.94的平均DSC和6.3%的平均绝对RSD.
- 即使只有两个图像,也得到了0.90的平均DSC和9.0%的平均绝对RSD.
- 这与商业膀扫描仪相比显著改善 (平均绝对RSD为13.6%).
结论:
- 提出的3D重建方法大大提高了膀体积测量的准确性.
- 这种技术显示出集成到未来膀体积测量器件的巨大潜力.
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