DXM-TransFuse U-net: Dual Cross-Modal Transformer Fusion U-net for Automated Nerve Identification
- Resource Type
- Working Paper
- Authors
- Xie, Baijun; Milam, Gary; Ning, Bo; Cha, Jaepyeong; Park, Chung Hyuk
- Source
- Computerized Medical Imaging and Graphics, 2022-07-01, Volume 99, Article 102090
- Subject
- Electrical Engineering and Systems Science - Image and Video Processing
Computer Science - Computer Vision and Pattern Recognition
- Language
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.