DXM-TransFuse U-net: Dual Cross-Modal Transformer Fusion U-net for Automated Nerve Identification
This addresses the critical need to prevent nerve injuries in surgical procedures, which can cause long-term patient harm and financial burdens, though it appears incremental as it builds on existing U-Net and Transformer methods.
The study tackled the problem of accurate nerve identification during surgery by developing a deep-learning network that combines U-Net with a Transformer-based fusion module for multi-modal optical imaging, achieving improved effectiveness for noninvasive intraoperative identification.
Accurate nerve identification is critical during surgical procedures for preventing any damages to nerve tissues. Nerve injuries can lead to long-term detrimental effects for patients as well as financial overburdens. In this study, we develop a deep-learning network framework using the U-Net architecture with a Transformer block based fusion module at the bottleneck to identify nerve tissues from a multi-modal optical imaging system. By leveraging and extracting the feature maps of each modality independently and using each modalities information for cross-modal interactions, we aim to provide a solution that would further increase the effectiveness of the imaging systems for enabling the noninvasive intraoperative nerve identification.