PURPOSE Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution that has been used for intraoperative imaging and also for motion estimation, for example, in the context of ophthalmic surgery or cochleostomy. Recently, motion estimation between a template and a moving OCT image has been studied with deep learning methods to overcome the shortcomings of conventional, feature-based methods. METHODS We investigate whether using a temporal stream of OCT image volumes can improve deep learning-based motion estimation performance. For this purpose, we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach. Also, we propose a temporal regularization strategy at the model output. RESULTS Using a tissue dataset without additional markers, our deep learning methods using 4D data outperform previous approaches. The best performing 4D architecture achieves an correlation coefficient (aCC) of 98.58% compared to 85.0% of a previous 3D deep learning method. Also, our temporal regularization strategy at the output further improves 4D model performance to an aCC of 99.06%. In particular, our 4D method works well for larger motion and is robust toward image rotations and motion distortions. CONCLUSIONS We propose 4D spatio-temporal deep learning for OCT-based motion estimation. On a tissue dataset, we find that using 4D information for the model input improves performance while maintaining reasonable inference times. Our regularization strategy demonstrates that additional temporal information is also beneficial at the model output.

中文翻译：

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使用4D OCT图像数据进行运动估计的时空深度学习方法。

目的定位结构和估计特定目标区域的运动是外科手术中导航的常见问题。光学相干断层扫描（OCT）是一种具有高时空分辨率的成像方式，已用于术中成像以及运动估计，例如在眼科手术或耳蜗切开术中。近来，已经利用深度学习方法研究了模板与运动的OCT图像之间的运动估计，以克服传统的基于特征的方法的缺点。方法我们调查使用OCT图像量的时间流是否可以改善基于深度学习的运动估计性能。为此，我们设计和评估了几种3D和4D深度学习方法，并提出了一种新的深度学习方法。也，我们在模型输出处提出了时间正则化策略。结果使用没有附加标记的组织数据集，我们使用4D数据的深度学习方法优于以前的方法。表现最佳的4D架构实现的相关系数（aCC）为98.58％，而之前的3D深度学习方法为85.0％。同样，我们在输出端的时间正则化策略进一步将4D模型的性能提高到了99.06％的aCC。特别是，我们的4D方法适用于较大的运动，并且对图像旋转和运动失真具有鲁棒性。结论我们提出了基于OCT的运动估计的4D时空深度学习。在组织数据集上，我们发现将4D信息用于模型输入可改善性能，同时保持合理的推理时间。