Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features

TL;DR

This paper introduces PDLF-Net, a deep learning model that improves segmentation of weakly visible environmental microorganism images by focusing on foreground features through pairwise deep learning features, achieving high accuracy.

Contribution

The study proposes a novel Pairwise Deep Learning Feature Network (PDLF-Net) that enhances segmentation of transparent, noisy EM images by leveraging pairwise features based on Shi and Tomas descriptors.

Findings

Achieved 89.24% accuracy in segmentation

Attained 63.20% IoU score

Demonstrated superior performance over existing methods

Abstract

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.