  Visual discrimination of clinical tissue types remains challenging, with
traditional RGB imaging providing limited contrast for such tasks.
Hyperspectral imaging (HSI) is a promising technology providing rich spectral
information that can extend far beyond three-channel RGB imaging. Moreover,
recently developed snapshot HSI cameras enable real-time imaging with
significant potential for clinical applications. Despite this, the
investigation into the relative performance of HSI over RGB imaging for
semantic segmentation purposes has been limited, particularly in the context of
medical imaging. Here we compare the performance of state-of-the-art deep
learning image segmentation methods when trained on hyperspectral images, RGB
images, hyperspectral pixels (minus spatial context), and RGB pixels
(disregarding spatial context). To achieve this, we employ the recently
released Oral and Dental Spectral Image Database (ODSI-DB), which consists of
215 manually segmented dental reflectance spectral images with 35 different
classes across 30 human subjects. The recent development of snapshot HSI
cameras has made real-time clinical HSI a distinct possibility, though
successful application requires a comprehensive understanding of the additional
information HSI offers. Our work highlights the relative importance of spectral
resolution, spectral range, and spatial information to both guide the
development of HSI cameras and inform future clinical HSI applications.
