  Information about the structure and composition of biopsy specimens can
assist in disease monitoring and diagnosis. In principle, this can be acquired
from Raman and infrared (IR) hyperspectral images (HSIs) that encode
information about how a sample's constituent molecules are arranged in space.
Each tissue section/component is defined by a unique combination of spatial and
spectral features, but given the high dimensionality of HSI datasets,
extracting and utilising them to segment images is non-trivial. Here, we show
how networks based on deep convolutional autoencoders (CAEs) can perform this
task in an end-to-end fashion by first detecting and compressing relevant
features from patches of the HSI into low-dimensional latent vectors, and then
performing a clustering step that groups patches containing similar
spatio-spectral features together. We showcase the advantages of using this
end-to-end spatio-spectral segmentation approach compared to i) the same
spatio-spectral technique not trained in an end-to-end manner, and ii) a method
that only utilises spectral features (spectral k-means) using simulated HSIs of
porcine tissue as test examples. Secondly, we describe the potential
advantages/limitations of using three different CAE architectures: a generic 2D
CAE, a generic 3D CAE, and a 2D CNN architecture inspired by the recently
proposed UwU-net that is specialised for extracting features from HSI data. We
assess their performance on IR HSIs of real colon samples. We find that all
architectures are capable of producing segmentations that show good
correspondence with HE stained adjacent tissue slices used as approximate
ground truths, indicating the robustness of the CAE-driven approach for
segmenting biomedical HSI data. Additionally, we stress the need for more
accurate ground truth information to rigorously compare the advantages offered
by each architecture.
