单视图光X射线姿势估计:替代损失函数和体积场景表示的比较
Chaochao Zhou1, Syed Hasib Akhter Faruqui2, Dayeong An2
1Department of Radiology, Northwestern Medicine, Northwestern University, Chicago, IL, USA. chaochao.zhou@northwestern.edu.
Journal of imaging informatics in medicine
|December 13, 2024
概括
这项研究引入了光镜姿势估计的框架,发现相互信息损失在准确性方面优越. 神经场景表示提供了与CBCT相似的性能,但需要更多的计算资源进行训练.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 计算解剖学的计算解剖学
背景情况:
- 图像引导程序通常依赖于姿势估计来导航3D空间.
- 准确的姿势估计对于手术精度和患者安全至关重要.
研究的目的:
- 开发和评估光镜姿势估计的新框架.
- 在这个框架内,比较不同损失函数和体积场景表示的有效性.
主要方法:
- 开发了一个可微分投影 (DiffProj) 算法,用于生成数字重建放射图 (DRR).
- 介绍并比较了两个神经场景表示:神经调断层扫描 (NeTT) 和掩盖的神经辐射场 (mNeRF).
- 利用代梯度下降与各种损失函数来执行对地面真相光镜图像进行姿势估计.
主要成果:
- 互助信息丢失函数显著超过了其他测试的丢失函数,防止了局部最佳陷.
- 离散 (CBCT) 和神经 (NeTT,mNeRF) 场景表示都实现了可比的姿势估计精度 (平均3D角度误差≤3.2°).
- 与离散方法相比,神经场景表示在训练期间显示了较高的计算成本.
结论:
- 相互信息是用于光镜姿势估计的强大的损失函数.
- 神经场景表示显示出对姿势估计的希望,但需要进一步优化计算效率.
- 开发的框架为评估医学成像中的姿势估计技术提供了一个灵活的平台.
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