Real-time Nerve Identification via Birefringence Mapping
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Solution Overview
Problem
Current nerve identification techniques in surgery are time-consuming, invasive, and error-prone, often relying on direct visual observation or electrical nerve stimulation, which can lead to iatrogenic trauma and postoperative complications.
Innovation Solution
The development of a Dual Cross-Modal Transformer Fusion U-Net (DXM-TransFuse U-Net) that utilizes multi-modal imaging inputs, including RGB and birefringence maps, to enhance nerve identification through deep learning-based image segmentation, providing real-time, non-invasive, and accurate nerve detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct visual observation or electrical nerve stimulation is used for nerve identification, then the surgical procedure can be performed with standard equipment, but the identification accuracy is poor and iatrogenic trauma occurs
Solution Approach 1:
The patent replaces direct visual observation and electrical stimulation with optical imaging techniques. Specifically, it uses Mueller polarimetric imaging to capture birefringence patterns of nerves, transforming the identification method from mechanical/electrical to optical domain, thereby improving accuracy and reducing iatrogenic trauma
Solution Approach 2:
The patent introduces an intermediary imaging system between the surgeon and the nerve structure. The Mueller polarimetric imaging system captures optical properties of nerves, and deep learning algorithms process these images to identify nerves automatically, serving as an intermediary that enhances identification accuracy without direct tissue manipulation
2Measurement precision
If human visual observation is used for nerve identification, then no additional equipment is needed, but outcomes are strongly surgeon-dependent and variability is high
Solution Approach 1:
The patent creates an objective copy of the nerve structure through Mueller polarimetric imaging. The system captures optical properties and generates standardized images that can be processed by deep learning algorithms, providing a consistent and reproducible representation of nerve anatomy that is independent of surgeon expertise
Solution Approach 2:
The patent substitutes human visual processing with an automated optical imaging system combined with deep learning algorithms. This replacement eliminates variability in human observation while maintaining the ability to identify nerves accurately, transferring the identification task from biological to computational domain
3Measurement precision
If intraoperative electrical nerve stimulation is used, then nerve localization can be confirmed, but the surgical workflow is interrupted by intermittent stimulations
Solution Approach 1:
The patent enables continuous nerve identification throughout the surgical procedure using Mueller polarimetric imaging. Unlike intermittent electrical stimulation, the imaging system provides ongoing visual information without disrupting the surgical workflow, allowing real-time nerve localization while maintaining surgical continuity
Solution Approach 2:
The patent replaces intermittent electrical stimulation with continuous optical imaging. The Mueller polarimetric imaging system provides continuous visual feedback about nerve structures without the need for repeated electrical interventions, thereby maintaining surgical workflow efficiency while ensuring accurate nerve localization
4Reliability
If standard visual inspection is performed, then the surgical procedure remains simple, but nerve injuries occur due to lack of situational awareness
Solution Approach 1:
The patent introduces an intermediary imaging system that provides situational awareness of nerve structures during surgery. The Mueller polarimetric imaging system captures optical properties of nerves and feeds this information to deep learning algorithms, creating an intermediary layer that enhances surgical safety without requiring direct manipulation of nerves
Solution Approach 2:
The patent implements a feedback mechanism where the imaging system continuously monitors the surgical field and provides real-time information about nerve locations. The deep learning algorithms process the images and provide automated nerve identification feedback to the surgical team, enabling proactive nerve protection throughout the procedure
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed system achieves significant improvements in nerve detection and segmentation accuracy, outperforming existing methods with faster processing times and reduced errors, thereby enhancing surgical safety and efficiency.
Implementation Method 1
our group recently proposed and successfully demonstrated an optical imaging nerve identification using the Mueller polarimetric imaging (Ning et al., 2021), which calculates intrinsic birefringence patterns from fibrous nerve structures
Data Source
AI summary
Precise nerve detection during surgery is crucial for safe operational outcomes, which largely depend on the surgeon's skills. Therefore, automatic real-time identification of anatomical structures is of great importance. However, image processing times must be kept as low as possible to be a viable solution for real-time visual identification. By using a general purpose graphics processing unit (GPGPU) implementation for birefringence mapping, the process can achieve rates of 43 frames per second (FPS); a 118×gain from a CPU implementation. Furthermore, by including a deep learning network, the complete framework can automatically detect nerves close to 12 FPS. By leveraging GPU for the processing task, the framework can run on compact devices with NVIDIA modules, such as Jetson Xavier AGX, while still achieving reasonably fast nerve identification and visualization.