Pyramid-Based Fully Convolutional Networks for Cell Segmentation
The low contrast and irregular cell shapes in microscopy images cause difficulties to obtain the accurate cell segmentation. We propose pyramid-based fully convolutional networks (FCN) to segment cells in a cascaded refinement manner. The higher-level FCNs generate coarse cell segmentation masks, attacking the challenge of low contrast between cell inner regions and the background. The lower-level FCNs generate segmentation masks focusing more on cell details, attacking the challenge of irregular cell shapes. The FCNs in the pyramid are trained in a cascaded way such that the residual error between the ground truth and upper-level segmentation is propagated to the lower-level and draws the attention of the lower-level FCNs to find the cell details missed from the upper-levels. The fine cell details from lower-level FCNs are gradually fused into the coarse segmentation from upper-level FCNs so as to obtain a final precise cell segmentation mask. On the ISBI cell segmentation challenge dataset and a newly collected dataset with high-quality ground truth, our method outperforms the state-of-the-art methods.
T. Zhao and Z. Yin, "Pyramid-Based Fully Convolutional Networks for Cell Segmentation," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11073 LNCS, pp. 677-685, Springer Verlag, Sep 2018.
The definitive version is available at https://doi.org/10.1007/978-3-030-00937-3_77
21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 (2018: Sep. 16-20, Granada, Spain)
Intelligent Systems Center
Keywords and Phrases
Cells; Convolution; Cytology; Medical computing; Medical imaging, Accurate cells; Cell segmentation; Coarse segmentation; Convolutional networks; Microscopy images; Residual error; Segmentation masks; State-of-the-art methods, Image segmentation
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2018 Springer Verlag, All rights reserved.