Spinal tissue identification using a Forward-oriented endoscopic ultrasound technique.

The limited imaging depth of optical endoscope restrains the identification of tissues under surface during the minimally invasive spine surgery (MISS), thus increasing the risk of critical tissue damage. This study is proposed to improve the accuracy and effectiveness of automatic spinal soft tissue identification using a forward-oriented ultrasound endoscopic system. Total 758 ex-vivo soft tissue samples were collected from ovine spines to create a dataset with four categories including spinal cord, nucleus pulposus, adipose tissue, and nerve root. Three conventional methods including Gray-level co-occurrence matrix (GLCM), Empirical Wavelet Transform (EWT), Variational Mode Decomposition (VMD) and two deep-learning based methods including Densely Connected Neural Network (DenseNet) model, one-dimensional Vision Transformer (ViT) model, were applied to identify the spinal tissues. The two deep learning methods outperformed the conventional methods with both accuracy over 95%. Especially the signal-based method (ViT) achieved an accuracy of 98.31% and a specificity of 99.2%, and the inference latency was only 0.0025 s. It illustrated the feasibility of applying the forward-oriented ultrasound endoscopic system for real-time intraoperative recognition of critical spinal tissues to enhance the precision and safety of minimally invasive spine surgery.