In-vivo markerless motion detection from volumetric optical coherence tomography data using CNNs
In-vivo markerless motion detection from volumetric optical coherence tomography data using CNNs
复制标题
使用 CNN 根据体积光学相干断层扫描数据进行体内无标记运动检测
DOI：
10.1117/12.2581023
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Schlaefer A
中科院分区：
文献类型：
--
作者：
Sprenger J;Neidhardt M;Schlüter M;Latus S;Gosau T;Kemmling J;Feldhaus S;Schumacher U;Schlaefer A
精确导航是机器人辅助微创手术中的一项重要任务。对光学标记的需求以及皮肤或器官上缺乏明显的解剖特征使得商业跟踪系统的组织跟踪变得复杂。先前的工作已经证明了基于 3D 光学相干断层扫描的系统用于此目的的可行性。此外，卷积神经网络已被证明可以精确检测体积之间的变化。然而，大多数实验都是用模型或离体组织进行的。我们介绍了一种实验装置，并对体内异种移植肿瘤的灌注和非灌注（死亡）组织进行测量。我们训练 3D 连体深度学习模型并评估运动预测的精度。比较了网络预测不同运动幅度的变化的能力以及不同体积轴的性能。灌注和非灌注肿瘤组织的均方根误差分别为 0:12mm 和 0:08mm。
Precise navigation is an important task in robot-assisted and minimally invasive surgery. The need for optical markers and a lack of distinct anatomical features on skin or organs complicate tissue tracking with commercial tracking systems. Previous work has shown the feasibility of a 3D optical coherence tomography based system for this purpose. Furthermore, convolutional neural networks have been proven to precisely detect shifts between volumes. However, most experiments have been performed with phantoms or ex-vivo tissue. We introduce an experimental setup and perform measurements on perfused and non-perfused (dead) tissue of in-vivo xenograft tumors. We train 3D siamese deep learning models and evaluate the precision of the motion prediction. The network's ability to predict shifts for different motion magnitudes and also the performance for the different volume axes are compared. The root-mean-square errors are 0:12mm and 0:08mm on perfused and non-perfused tumor tissue, respectively.