Pyramidal Deep Neural Networks for the Accurate Segmentation and Counting of Cells in Microscopy Data |
Vununu, Caleb
(Dept. of IT Convergence and Application Engineering, Pukyong National University)
Kang, Kyung-Won (Dept. of Information & Communication Eng., Tongmyong University) Lee, Suk-Hwan (Dept. of Information Security, Tongmyong University) Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University) |
1 | O. Sliusarenko, J. Heinritz, T. Emonet, and C. Jacobs-Wagner, “High-Throughput, Subpixel Precision Analysis of Bacterial Morphogenesis and Intracellular Spatio-Temporal Dynamics,” Molecular Microbiology, Vol. 80, No. 3, pp. 621-627, 2011. |
2 | J.W. Young, J.C.W. Locke, A. Altinok, N. Rosenfeld, T. Bacarian, P.S. Swain, 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 |
3 | S. Tay, J.J. Hughey, T.K. Lee, T. Lipniacki, S.R. Quake, M.W. Covert, et al., "Single-Cell Dynamics Reveal Digital Activation and Analogue Information Processing," Nature, Vol. 466, No. 7303, pp. 267-271, 2010. DOI |
4 | 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 |
5 | N. Madusanka, Y.Y. Choi, K.Y. Choi, K.H. Lee, and H.-K. Choi, "Hippocampus Segmentation and Classification in Alzheimer's Disease and Mild Cognitive Impairment Applied on MR Images," Journal of Korea Multimedia Society, Vol. 20, No. 2, pp. 205-215, 2017. DOI |
6 | A.E. Carpenter, T.R. Jones, M.R. Lamprecht, C. Clarke, I.H. Kang, and O. Friman, et al., "CellProfiler: Image Analysis Software for Identifying and Quantifying Cell Phenotypes," Genome Biology, Vol. 7, No. 10, pp. r100-r100, 2006. DOI |
7 | Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, Vol. 521, pp. 436-444, 2015. DOI |
8 | O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," Proceeding of Medical Image Computing and Computer Assisted Intervention 2015, pp. 234-241, 2015. |
9 | C.X. Hernandez and M.M. Sultan, "Using Deep Learning for Segmentation and Counting Within Microscopy Data," arXiv e-prints arXiv:1802.10548, 2018. |
10 | G. Ghiasi and C.C. Fowlkes, "Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation," Proceeding of European Conference on Computer Vision, pp. 519-534, 2016. |
11 | G.E. Hinton and R.R. Salakhutdinov, “Reducing the Dimensionality of the Data with Neural Networks,” Science, Vol. 313, No. 5786, pp. 504-507, 2006. DOI |
12 | D.E. Rumelhart, G.E. Hinton, and R.J. Williams, "Learning Representations by Back-Propagating Errors," Nature, Vol. 323, pp. 533-536, 1986. DOI |
13 | V. Ljosa, K.L. Sokolnicki, and A.E. Carpenter, “Annotated High-Throughput Microscopy Image Sets for Validation,” Nature Methods, Vol. 9, No. 7, pp. 637-637, 2012. DOI |
14 | 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 |
15 | L. Putzu, G. Caocci, and C.D. Ruberto, "Leucocytes Classification for Leukemia Detection Using Image Processing Techniques," Artificial Intelligence in Medicine, Vol. 62, No. 3, pp. 179-191, 2014. DOI |