Browse > Article
http://dx.doi.org/10.9717/kmms.2020.23.8.965

Segmentation of Bacterial Cells Based on a Hybrid Feature Generation and Deep Learning  

Lim, Seon-Ja (Dept. of Computer Engineering, Pukyong National University)
Vununu, Caleb (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Youn, Sung-Dae (Dept. of Computer Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.8, 2020 , pp. 965-976 More about this Journal
Abstract
We present in this work a segmentation method of E. coli bacterial images generated via phase contrast microscopy using a deep learning based hybrid feature generation. Unlike conventional machine learning methods that use the hand-crafted features, we adopt the denoising autoencoder in order to generate a precise and accurate representation of the pixels. We first construct a hybrid vector that combines original image, difference of Gaussians and image gradients. The created hybrid features are then given to a deep autoencoder that learns the pixels' internal dependencies and the cells' shape and boundary information. The latent representations learned by the autoencoder are used as the inputs of a softmax classification layer and the direct outputs from the classifier represent the coarse segmentation mask. Finally, the classifier's outputs are used as prior information for a graph partitioning based fine segmentation. We demonstrate that the proposed hybrid vector representation manages to preserve the global shape and boundary information of the cells, allowing to retrieve the majority of the cellular patterns without the need of any post-processing.
Keywords
Bio-cell Informatics; Bacterial Cell Segmentation; Autoencoder; Hybrid Feature; Artificial Neural Network; Deep Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning Representations by Back-Propagating Errors,” Nature, Vol. 323, No. 1, pp. 533-536, 1986.   DOI
2 N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979.   DOI
3 R. Ali, M. Gooding, M. Christlieb, and M. Brady, "Phase-based Segmentation of Cells from Brightfield Microscopy," Proceeding of the 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 57-60, 2007.
4 O. Ronneberger, P. Fischer, and T. Brox, "UNet: Convolutional Networks for Biomedical Image Segmentation," Proceeding of MICCAI 2015, pp. 234-241, 2015.
5 Q. Wang, J. Niemi, C.M. Tan, L. You, and M. West, “Image Segmentation and Dynamic Lineage Analysis in Single-cell Fluorescence Microscopy,” Cytometry Part A, Vol. 77, No. 1, pp. 101-110, 2010.
6 J.W. Young, J.C.W. Locke, A. Altinok, N. Rosenfeld, T. Bacarian, et al., “Measuring Single- Cell Gene Expression Dynamics in Bacteria using Fluorescence Time-Lapse Microscopy,” Nature Protocols, Vol. 7, No. 1, pp. 80-88, 2012.   DOI
7 Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, Vol. 521, No. 1, pp. 436-444, 2015.   DOI
8 D.R. Martin, C.C. Fowlkes, and J. Malik, “Learning to Detect Natural Image Boundaries using Local Brightness, Color, and Texture Cues,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 5, pp. 530-549, 2004.   DOI
9 O.H. Kwon, M.G. Song, H.J. Song, and K.R. Kwon, “Layer Segmentation of Retinal OCT Images using Deep Convolutional Encoder-Decoder Network,” Journal of Korea Multimedia Society, Vol. 22, No. 11, pp. 1269-1279, 2019.   DOI
10 I. Belevich, M. Joensuu, D. Kumar, H. Vihinen, and E. Jokitalo, "Microscopy Image Browser: A Platform for Segmentation and Analysis of Multidimensional Datasets," PLoS Biology, Vol. 14, No. 1, e1002340, 2016.   DOI
11 D.A. Van Valen, T. Kudo, K.M. Lane, D.N. Macklin, N.T. Quach, M.M. DeFelice, et. al., "Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments," PLoS Computational Biology, Vol. 12, No. 11, e10051, 2016.
12 P. Moeskops, M.A. Viergever, A.M. Mendrik, L.S. de Vries, M.J.L. Benders, and I. Isgum, "Automatic Segmentation of MR Brain Images with a Convolutional Neural Network," IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp.1252-1262, 2016.   DOI
13 Y. Song, L. Zhang, S. Chen, D. Ni, B. Lei, and T. Wang, “Accurate Segmentation of Cervical Cytoplasm and Nuclei Based on Multiscale Convolutional Network and Graph Partitioning,” IEEE Transactions on Biomedical Engineering, Vol. 62, No. 10, pp. 2421-2433, 2015.   DOI
14 Y. Boykov and M.P. Jolly, "Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images," Proceeding Eighth IEEE International Conference on Computer Vision ICCV 2001, pp. 105-112, 2001.
15 G.E. Hinton and R.R. Salakhutdinov, "Reducing the Dimensionality of the Data with Neural Networks," Science, Vol. 313, Issue 5786, pp. 504-507, 2006.   DOI