IEEE J Biomed Health Inform. 2019 Nov;23(6):2211-2219. doi: 10.1109/JBHI.2018.2853987. Epub 2018 Jul 9.
Robotic endoscopic systems offer a minimally invasive approach to the examination of internal body structures, and their application is rapidly extending to cover the increasing needs for accurate therapeutic interventions. In this context, it is essential for such systems to be able to perform measurements, such as measuring the distance traveled by a wireless capsule endoscope, so as to determine the location of a lesion in the gastrointestinal tract, or to measure the size of lesions for diagnostic purposes. In this paper, we investigate the feasibility of performing contactless measurements using a computer vision approach based on neural networks. The proposed system integrates a deep convolutional image registration approach and a multilayer feed-forward neural network into a novel architecture. The main advantage of this system, with respect to the state-of-the-art ones, is that it is more generic in the sense that it is 1) unconstrained by specific models, 2) more robust to nonrigid deformations, and 3) adaptable to most of the endoscopic systems and environment, while enabling measurements of enhanced accuracy. The performance of this system is evaluated under ex vivo conditions using a phantom experimental model and a robotically assisted test bench. The results obtained promise a wider applicability and impact in endoscopy in the era of big data.
机器人内窥镜系统提供了一种微创的方法来检查内部身体结构，其应用正在迅速扩展，以满足对精确治疗干预的日益增长的需求。在这种情况下，对于这些系统来说，能够进行测量是至关重要的，例如测量无线胶囊内窥镜的行驶距离，以确定胃肠道中病变的位置，或者为了诊断目的测量病变的大小。在本文中，我们研究了使用基于神经网络的计算机视觉方法进行非接触式测量的可行性。所提出的系统将深度卷积图像配准方法和多层前馈神经网络集成到一个新的架构中。与现有技术相比，该系统的主要优势在于它更通用，因为它 1）不受特定模型的限制，2）对非刚体变形更鲁棒，3）适用于大多数内窥镜系统和环境，同时能够实现更高精度的测量。该系统的性能在使用机器人辅助测试台的离体实验模型下进行了评估。所获得的结果有望在大数据时代为内窥镜带来更广泛的适用性和影响。
