Abstract
During surgery of delicate regions, differentiation between nerve and surrounding tissue is crucial. Hyperspectral imaging (HSI) techniques can enhance the contrast between types of tissue beyond what the human eye can differentiate. Whereas an RGB image captures 3 bands within the visible light range (e.g., 400 nm to 700 nm), HSI can acquire many bands in wavelength increments that highlight regions of an image across a wavelength spectrum. We developed a workflow to identify nerve tissues from other similar tissues such as fat, bone, and muscle. Our workflow uses spectral angle mapper (SAM) and endmember selection. The method is robust for different types of environment and lighting conditions. We validated our workflow on two samples of human tissues. We used a compact HSI system that can image from 400 to 1700 nm to produce HSI of the samples. On these two samples, we achieved an intersection-over-union (IoU) segmentation score of 84.15% and 76.73%, respectively. We showed that our workflow identifies nerve segments that are not easily seen in RGB images. This method is fast, does not rely on special hardware, and can be applied in real time. The hyperspectral imaging and nerve detection approach may provide a powerful tool for image-guided surgery.