Computer‐aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks

Abstract

During laparoscopic surgery, the Veress needle is commonly used in pneumoperitoneum establishment. Precise placement of the Veress needle is still a challenge for the surgeon. In this study, a computer‐aided endoscopic optical coherence tomography (OCT) system was developed to effectively and safely guide Veress needle insertion. This endoscopic system was tested by imaging subcutaneous fat, muscle, abdominal space, and the small intestine from swine samples to simulate the surgical process, including the situation with small intestine injury. Each tissue layer was visualized in OCT images with unique features and subsequently used to develop a system for automatic localization of the Veress needle tip by identifying tissue layers (or spaces) and estimating the needle‐to‐tissue distance. We used convolutional neural networks (CNNs) in automatic tissue classification and distance estimation. The average testing accuracy in tissue classification was 98.53 ± 0.39%, and the average testing relative error in distance estimation reached 4.42 ± 0.56% (36.09 ± 4.92 μm).

Percutaneous Nephrostomy Guidance by a Convolutional Neural Network Based Endoscopic Optical Coherence Tomography System

“…It has been clinically applied in the diagnosis of different diseases, intraoperative tissue assessment, and blood vessel segmentation. To assist endoscopic OCT imaging, our previous studies have illustrated the potential of using CNN methods in different applications, such as pig kidney tissue classification [60], epidural needle navigation [61], and Veress needle location [62]. Therefore, we continue to use CNN methods for human kidney tissue recognition in this research.…”