Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and provide feedback for the ablation laser to avoid critical tissues such as bone marrow and nerve. Furthermore, in the implementation, the tissue classifier’s accuracy is dependent on the quality of the OCT image. Therefore, image denoising plays an important role in having an accurate feedback system. A common OCT image denoising technique is the frame-averaging method. Inherent to this method is the need for multiple images, i.e., the more images used, the better the resulting image quality. However, this approach comes at the price of increased acquisition time and sensitivity to motion artifacts. To overcome these limitations, we applied a deep-learning denoising method capable of imitating the frame-averaging method. The resulting image had a similar image quality to the frame-averaging and was better than the classical digital filtering methods. We also evaluated if this method affects the tissue classifier model’s accuracy that will provide feedback to the ablation laser. We found that image denoising significantly increased the accuracy of the tissue classifier. Furthermore, we observed that the classifier trained using the deep learning denoised images achieved similar accuracy to the classifier trained using frame-averaged images. The results suggest the possibility of using the deep learning method as a pre-processing step for real-time tissue classification in smart laser osteotomy.

中文翻译：

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用于智能激光截骨术的基于深度学习的快速光学相干断层扫描 (OCT) 图像去噪


激光截骨术可以实现精确切割并减少骨组织损伤。我们提出了光学相干断层扫描 (OCT) 来监测智能激光截骨方法的消融过程。 OCT图像有助于识别组织类型，并为消融激光提供反馈，以避免骨髓和神经等关键组织。此外，在实现中，组织分类器的准确性取决于OCT图像的质量。因此，图像去噪对于拥有准确的反馈系统起着重要作用。常见的 OCT 图像去噪技术是帧平均法。该方法的本质是需要多个图像，即使用的图像越多，得到的图像质量就越好。然而，这种方法的代价是增加采集时间和对运动伪影的敏感性。为了克服这些限制，我们应用了一种能够模仿帧平均方法的深度学习去噪方法。所得图像具有与帧平均相似的图像质量，并且优于经典的数字滤波方法。我们还评估了这种方法是否会影响组织分类器模型的准确性，该模型将为消融激光提供反馈。我们发现图像去噪显着提高了组织分类器的准确性。此外，我们观察到使用深度学习去噪图像训练的分类器达到了与使用帧平均图像训练的分类器相似的精度。结果表明，可以使用深度学习方法作为智能激光截骨术中实时组织分类的预处理步骤。