@inproceedings{6dc2cfc28a2f41ce9afaa00258dc49fc,
title = "SoLiD: Segmentation of clostridioides difficile cells in the presence of inhomogeneous illumination using a deep adversarial network",
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, Data augmentation, Deep adversarial training, U-net",
author = "Ali Memariani and Kakadiaris, {Ioannis A.}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 16-09-2018",
year = "2018",
doi = "10.1007/978-3-030-00919-9_33",
language = "English (US)",
isbn = "9783030009182",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "285--293",
editor = "Mingxia Liu and Heung-Il Suk and Yinghuan Shi",
booktitle = "Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings",
}