  The use of Environmental Microorganisms (EMs) offers a highly efficient, low
cost and harmless remedy to environmental pollution, by monitoring and
decomposing of pollutants. This relies on how the EMs are correctly segmented
and identified. With the aim of enhancing the segmentation of weakly visible EM
images which are transparent, noisy and have low contrast, a Pairwise Deep
Learning Feature Network (PDLF-Net) is proposed in this study. The use of PDLFs
enables the network to focus more on the foreground (EMs) by concatenating the
pairwise deep learning features of each image to different blocks of the base
model SegNet. Leveraging the Shi and Tomas descriptors, we extract each image's
deep features on the patches, which are centered at each descriptor using the
VGG-16 model. Then, to learn the intermediate characteristics between the
descriptors, pairing of the features is performed based on the Delaunay
triangulation theorem to form pairwise deep learning features. In this
experiment, the PDLF-Net achieves outstanding segmentation results of 89.24%,
63.20%, 77.27%, 35.15%, 89.72%, 91.44% and 89.30% on the accuracy, IoU, Dice,
VOE, sensitivity, precision and specificity, respectively.
