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Scanning electron microscopy (SEM) techniques have been extensively performed to image and study bacterial cells with high-resolution images. Bacterial image segmentation in SEM images is an essential task to distinguish an object of interest and its specific region. These segmentation results can then be used to retrieve quantitative measures (e.g., cell length, area, cell density) for the accurate decision-making process of obtaining cellular objects. However, the complexity of the bacterial segmentation task is a barrier, as the intensity and texture of foreground and background are similar, and also, most clustered bacterial cells in images are partially overlapping with each other. The traditional approaches for identifying cell regions in microscopy images are labor intensive and heavily dependent on the professional knowledge of researchers. To mitigate the aforementioned challenges, in this study, we tested a U-Net-based semantic segmentation architecture followed by a post-processing step of morphological over-segmentation resolution to achieve accurate cell segmentation of SEM-acquired images of bacterial cells grown in a rotary culture system. The approach showed an 89.52% Dice similarity score on bacterial cell segmentation with lower segmentation error rates, validated over several cell overlapping object segmentation approaches with significant performance improvement.
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Cell segmentation using stable extremal regions in multi-exposure microscopy images
We propose a novel cell segmentation approach by extracting Multi-exposure Maximally Stable Extremal Regions (MMSER) in phase contrast microscopy images on the same cell dish. Using our method, cell regions can be well identified by considering the maximally stable regions with response to different camera exposure times. Meanwhile, halo artifacts with regard to cells at different stages are leveraged to identify cells' stages. The experimental results validate that high quality cell segmentation and cell stage classification can be achieved by our approach.
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- Award ID(s):
- 1355406
- PAR ID:
- 10023446
- Date Published:
- Journal Name:
- Proceedings (International Symposium on Biomedical Imaging)
- Page Range / eLocation ID:
- 526 to 530
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISBI.2016.7493323
