Journal
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Volume -, Issue -, Pages 1555-1559Publisher
IEEE
DOI: 10.1109/icip.2019.8803095
Keywords
Cell segmentation; convolutional neural networks; 3D U-Net; 3D watershed; conditional random field
Categories
Funding
- NSF MCB [1715544]
- Div Of Molecular and Cellular Bioscience
- Direct For Biological Sciences [1715544] Funding Source: National Science Foundation
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We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged cell membranes. Precise identification of cell boundaries, their shapes, and quantifying inter-cellular space leads to a better understanding of cell morphogenesis. Towards this, we outline a cell segmentation method that uses a deep neural network architecture to extract a confidence map of cell boundaries, followed by a 3D watershed algorithm and a final refinement using a conditional random field. In addition to improving the accuracy of segmentation compared to other state-ofthe-art methods, the proposed approach also generalizes well to different datasets without the need to retrain the network for each dataset. Detailed experimental results are provided, and the source code is available on GitHub 01.
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