Deep Learning in Spinal Endoscopy: U-Net Models for Neural Tissue Detection

Abstract

Biportal endoscopic spine surgery (BESS) is minimally invasive and therefore benefits both surgeons and patients. However, concerning complications include dural tears and neural tissue injuries. In this study, we aimed to develop a deep learning model for neural tissue segmentation to enhance the safety and efficacy of endoscopic spinal surgery. We used frames extracted from videos of 28 endoscopic spine surgeries, comprising 2307 images for training and 635 images for validation. A U-Net-like architecture is employed for neural tissue segmentation. Quantitative assessments include the Dice-Sorensen coefficient, Jaccard index, precision, recall, average precision, and image-processing time. Our findings revealed that the best-performing model achieved a Dice-Sorensen coefficient of 0.824 and a Jaccard index of 0.701. The precision and recall values were 0.810 and 0.839, respectively, with an average precision of 0.890. The model processed images at 43 ms per frame, equating to 23.3 frames per second. Qualitative evaluations indicated the effective identification of neural tissue features. Our U-Net-based model robustly performed neural tissue segmentation, indicating its potential to support spine surgeons, especially those with less experience, and improve surgical outcomes in endoscopic procedures. Therefore, further advancements may enhance the clinical applicability of this technique.

Keywords

• endoscopic spine surgery
• neural tissue
• image segmentation
• computer vision
• deep learning