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Title: A Morphological Post-Processing Approach for Overlapped Segmentation of Bacterial Cell Images
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.  more » « less
Award ID(s):
1920954
PAR ID:
10537340
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
MDPI
Date Published:
Journal Name:
Machine Learning and Knowledge Extraction
Volume:
4
Issue:
4
ISSN:
2504-4990
Page Range / eLocation ID:
1024 to 1041
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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