论文标题
使用4D OCT数据进行运动预测的深度学习方法
A Deep Learning Approach for Motion Forecasting Using 4D OCT Data
论文作者
论文摘要
特定目标对象的预测运动是手术干预的常见问题，例如对于目标区域的定位，手术干预指导或运动补偿。光学相干断层扫描（OCT）是具有高空间和时间分辨率的成像方式。最近，深度学习方法显示了基于两个体积图像的基于OCT的运动估计的有希望的性能。我们扩展了这种方法，并研究使用时间序列是否可以实现运动预测。我们提出了使用OCT量流的端到端运动预测和估算的4D时空深度学习。我们使用组织数据集设计和评估五种不同的3D和4D深度学习方法。我们最佳性能的4D方法可实现运动预测，总体平均相关系数为97.41％，同时，与以前的3D方法相比，运动估计的性能也提高了2.5倍。
Forecasting motion of a specific target object is a common problem for surgical interventions, e.g. for localization of a target region, guidance for surgical interventions, or motion compensation. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution. Recently, deep learning methods have shown promising performance for OCT-based motion estimation based on two volumetric images. We extend this approach and investigate whether using a time series of volumes enables motion forecasting. We propose 4D spatio-temporal deep learning for end-to-end motion forecasting and estimation using a stream of OCT volumes. We design and evaluate five different 3D and 4D deep learning methods using a tissue data set. Our best performing 4D method achieves motion forecasting with an overall average correlation coefficient of 97.41%, while also improving motion estimation performance by a factor of 2.5 compared to a previous 3D approach.
