DXM-TransFuse U-net: Dual Cross-Modal Transformer Fusion U-net for Automated Nerve Identification
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2022
- DOI:
- 10.48550/arXiv.2202.13304
- arXiv:
- arXiv:2202.13304
- Bibcode:
- 2022arXiv220213304X
- Keywords:
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- Electrical Engineering and Systems Science - Image and Video Processing;
- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- Computerized Medical Imaging and Graphics, 2022-07-01, Volume 99, Article 102090