Title

Pyramid-Based Fully Convolutional Networks for Cell Segmentation

Abstract

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.

Meeting Name

21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 (2018: Sep. 16-20, Granada, Spain)

Department(s)

Computer Science

Comments

This project was supported by NSF CAREER award 1351049 and NSF EPSCoR grant IIA-1355406.

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)

978-303000936-6

International Standard Serial Number (ISSN)

0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Springer Verlag, All rights reserved.

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