Indoor Localization of Hand-Held OCT Probe Using Visual Odometry and Real-Time Segmentation Using Deep Learning.
Indoor Localization of Hand-Held OCT Probe Using Visual Odometry and Real-Time Segmentation Using Deep Learning.
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DOI：
10.1109/tbme.2021.3116514
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发表时间：
2022-04
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光学相干层析成像(OCT)是一种成熟的医学成像手段，由于其能够以高分辨率可视化组织结构而得到广泛应用。目前，OCT手持成像探头缺乏位置信息，使得很难甚至不可能将特定图像与最初获得的位置联系起来。在这项研究中，我们提出了一种基于摄像机的定位方法来实时跟踪和记录扫描仪的位置，并提供了一种基于深度学习的分割方法。我们使用基于相机的视觉里程计(VO)和同步测绘和定位(SLAM)来计算和可视化手持OCT成像探头的位置。采用深度卷积神经网络(CNN)对肾小管腔进行分割。平均绝对误差(MAE)为0.15 mm，标准偏差(STD)为0.26 mm。对于2D平移，MAE和STD分别为0.85 mm和0.50 mm。分割方法的骰子系数为0.7。预测平均密度与实际平均密度、预测平均直径与实际平均直径之间的t检验分别为7.7547e-13和2.2288e-15。我们还利用我们的自动分割定位方法在保存的肾脏上进行了实验。比较3D综合扫描和VO系统扫描的平均密度图和平均直径图。结果表明，VO能够高精度地跟踪探头的位置，并为在3D空间中查看OCT 2D图像提供了一个用户友好的可视化工具。这也表明深度学习可以为分割提供高精度和高速度。该方法可用于预测肾移植术后移植肾功能延迟恢复情况。
Optical coherence tomography (OCT) is an established medical imaging modality that has found widespread use due to its ability to visualize tissue structures at a high resolution. Currently, OCT hand-held imaging probes lack positional information, making it difficult or even impossible to link a specific image to the location it was originally obtained. In this study, we propose a camera-based localization method to track and record the scanner position in real-time, as well as providing a deep learning-based segmentation method. We used camera-based visual odometry (VO) and simultaneous mapping and localization (SLAM) to compute and visualize the location of a hand-held OCT imaging probe. A deep convolutional neural network (CNN) was used for kidney tubule lumens segmentation. The mean absolute error (MAE) and the standard deviation (STD) for 1D translation were found to be 0.15 mm and 0.26mm respectively. For 2D translation, the MAE and STD were found to be 0.85 mm and 0.50 mm, respectively. The dice coefficient of the segmentation method was 0.7. The t-statistic of the t-test between predicted and actual average densities and predicted and actual average diameters were 7.7547e-13 and 2.2288e-15 respectively. We also experimented on a preserved kidney utilizing our localization method with automatic segmentation. Comparisons of the average density maps and average diameter maps were made between the 3D comprehensive scan and VO system scan. Our results demonstrate that VO can track the probe location at high accuracy, and provides a user-friendly visualization tool to review OCT 2D images in 3D space. It also indicates that deep learning can provide high accuracy and high speed for segmentation. The proposed methods can be potentially used to predict delayed graft function (DGF) in kidney transplantation.