Spatio-temporal deep learning models for tip force estimation during needle insertion
Spatio-temporal deep learning models for tip force estimation during needle insertion
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DOI：
10.1007/s11548-019-02006-z
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发表时间：
2019-09-01
影响因子：
3
通讯作者：
Schlaefer, Alexander
中科院分区：
文献类型：
--
作者：
Gessert, Nils;Priegnitz, Torben;Schlaefer, Alexander
在许多临床应用中，如近距离放射治疗或活检，针头的精确放置是一个挑战。作用在针头上的力会导致组织变形和针头偏转，从而可能导致针头错位或受伤。因此，提出了许多估计针上的力的方法。然而，将传感器集成到针尖是具有挑战性的，需要仔细校准才能获得良好的力估计。方法设计了一种使用单根OCT光纤进行测量的光纤针尖力传感器。光纤成像放置在针尖下方的环氧树脂层的变形，从而产生一维深度轮廓流。我们研究了不同的深度学习方法来促进时空图像数据和相关力之间的校准。特别是，我们提出了一种新的卷积神经网络- cnn架构，用于同时处理时空数据。结果通过改变环氧树脂层的硬度，该针可以适应不同的操作范围。同样，可以通过训练深度学习模型来调整校准。我们的新卷积神经网络- cnn架构的平均绝对误差最低，为1.59 +/- 1.3mN，相关系数为0.9997，明显优于其他方法。人体前列腺组织的离体实验证明了该针的应用。结论基于oct的光纤传感器提供了一种可行的针尖力估计方法。结果表明，图像流中包含的丰富的时空信息显示了整个环氧层的变形，可以有效地用于深度学习模型。特别是，我们证明了卷积神经网络- cnn架构表现良好，使其成为其他时空学习问题的有前途的方法。
Purpose Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and needle deflection which in turn may lead to misplacement or injury. Hence, a number of approaches to estimate the forces at the needle have been proposed. Yet, integrating sensors into the needle tip is challenging and a careful calibration is required to obtain good force estimates. Methods We describe a fiber-optic needle tip force sensor design using a single OCT fiber for measurement. The fiber images the deformation of an epoxy layer placed below the needle tip which results in a stream of 1D depth profiles. We study different deep learning approaches to facilitate calibration between this spatio-temporal image data and the related forces. In particular, we propose a novel convGRU-CNN architecture for simultaneous spatial and temporal data processing. Results The needle can be adapted to different operating ranges by changing the stiffness of the epoxy layer. Likewise, calibration can be adapted by training the deep learning models. Our novel convGRU-CNN architecture results in the lowest mean absolute error of 1.59 +/- 1.3mN and a cross-correlation coefficient of 0.9997 and clearly outperforms the other methods. Ex vivo experiments in human prostate tissue demonstrate the needle's application. Conclusions Our OCT-based fiber-optic sensor presents a viable alternative for needle tip force estimation. The results indicate that the rich spatio-temporal information included in the stream of images showing the deformation throughout the epoxy layer can be effectively used by deep learning models. Particularly, we demonstrate that the convGRU-CNN architecture performs favorably, making it a promising approach for other spatio-temporal learning problems.