Journal
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV
Volume 11073, Issue -, Pages 677-685Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-00937-3_77
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Funding
- NSF CAREER award [IIS-1351049]
- NSF EPSCoR grant [IIA-1355406]
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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.
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