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SoLiD: Segmentation of Clostridioides Difficile Cells in the Presence of Inhomogeneous Illumination Using a Deep Adversarial Network

  • Ali Memariani
  • Ioannis A.  Kakadiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

Segmentation of cells in scanning electron microscopy images is a challenging problem due to the presence of inhomogeneous illumination. Classical pre-processing methods for illumination normalization destroy the texture and add noise to the image. In this paper, we present a deep cell segmentation method using adversarial training that is robust to inhomogeneous illumination. Specifically, we apply a model based on U-net as the segmenter and a deep ConvNet as the discriminator for the adversarial training called SoLiD: “Segmentation of clostridioides difficile cells in the presence of inhomogeneous iLlumInation using a Deep adversarial network”. We also present an image augmentation algorithm to obtain the training images required for SoLid. The results indicate that SoLiD is robust to inhomogeneous illumination. The segmentation performance is compared to the U-net and the dice score is improved by 44%.

Keywords

Cell segmentation Deep adversarial training Data augmentation U-net 

Notes

Acknowledgements

This work was supported in part by the Hugh Roy and Lillie Cranz Cullen Endowment Fund.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Computational Biomedicine Lab, Department of Computer ScienceUniversity of HoustonHoustonUSA

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