Abstract
The study of the formation of Bacillus subtilis biofilms in microdroplets has great significance for understanding the biofilms growth in extreme environments. Due to the cell motion, cells observed by microscope have the characteristics of fuzzy edge information, low contrast and noise. it is difficult to segment targets from the background by traditional segmentation methods, but artificial intelligence has a better performance in the field of biological images. In the experiment, a two-stage cross microfluidic tube control system is used to obtain mono-disperse droplets containing Bacillus subtilis, and the image data are captured by a fast camera through a dark field microscope. In this paper, U-Net++ neural network model is used to identify cells. The encoder is used to extract high-level features of images. The decoder restores the features extracted by the encoder. Dense jump connection reduces the semantic gap between encoder and decoder, captures details and improves segmentation performance. Compared with the traditional segmentation methods, the U-Net++ model can be applied even in cases of low contrast and noise, and improves the accuracy and robustness of image segmentation. The U-Net++ method is compared with the traditional threshold segmentation method in terms of a series of metrics (accuracy, recall, F1 score, Intersection over Union). It is demonstrated that this method can extract target information of cells effectively. The U-Net++ method can be further used to analyze the movement of cells and help understanding the biofilm formation in micro-droplets.
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The raw/processed data required to reproduce these findings cannot beshared at this time as the data also forms part of an ongoing study.
References
Ali S, Madabhush A (2012) An Integrated Region-, Boundary-, shape-based active contour for multiple object overlap resolution in histological imagery [J]. IEEE Trans Med Imaging 31(7):1448–1460
Chenyue Wu B, Yi Y, Zhang et al (2018) Retinal vascular image Segmentation based on improved convolutional neural Network[J]. J Opt 38(11):125–131
Fu J, Liu J, Tian H et al (2019) Dual attention network for scene segmentation[C]. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 3146–3154
Kingma D, Ba J, Adam (2014) A method for stochastic optimization [J]. arXiv preprint arXiv:1412.6980
Liu W, Anguelov D, Erhan D et al (2016) SSD: Single Shot MultiBox Detector [C]. European conference on computer vision. Springer, Charm, 21–37
Milletari F, Navab N, Ahmadi SA (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation [J]. 2016 fourth international conference on 3D vision (3DV). IEEE, 565–571
Otsu N (2007) A threshold selection method from Gray-Level Histograms [J]. IEEE Trans Syst Man Cybermetics 9(1):62–66
Peng Wu. Application of image feature extraction and segmentation algorithm in Apple Image[D]. Chongqing University
Ronneberger O, Fischer P (2015) U-Net: Convolutional networks for biomedical image segmentation[J]. Cell 11:648–661
Ssong H, Tice JD, Ismagilov RF (2003) A microfluidic system for controlling reaction networks in time [J]. Angew Chem 115(7):792–796
Schmitt O, Hasse M (2009) Morphological multiscale decomposition of connected regions with emphasis on cell clusters [J]. Comput Vis Image Underst 113(2):188–201
Tingyue Zheng C, Tang ZL (2019) Multiscale Retinal Vascular Segmentation based on full convolutional neural Network[J]. J Opt 39(2):0211002
Verma R, Kumar N, Patil A et al (2020) Multi-organ nuclei segmentation and classification challenge 2020[J]. IEEE Trans Med Imaging 39(1380–1391):8
Xiao Z, Zhang B (2011) Laboratory and application of microfluidic chip based on droplet technology[J]. Chromatography 29(10):949–956
Yi J, Jiang M, WU P et al (2019) Attentive neural cell instance segmentation[J]. Med Image Anal 55:228–240
Zhou Z, Siddiquee M, Tajbakhsh N et al (2018) UNet++: A nested U-Net architecture for medical image segmentation [J], 4 edn. Deep Learning in Medical Image Analysis (DLMIA) Workshop
Zhu S, Chen L, Luo Q et al (2014) An Image segmentation method based on the combination of graph theory and semi-supervised learning, CN103942779A[P]
Zhu J, Li X, Zhang J et al (2019) A hierarchical convolutional network based 3d segmentation of brain tumors[J]. Laser Optoelectron Progress 56(10):67–76
Zunair H, Hamza AB, Sharp (2021) U-Net: depthwise convolutional network for biomedical image segmentation[J]. Comput Biol Med 136:104699
Acknowledgements
The authors would like to thank Professor David A. Weitz of Harvard University and Professor Shmuel Rubinstein of the Hebrew University for their experimental support; and the National Natural Science Foundation of China for funding support (11972074, 11772047 and 11620101001).
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Kong, R., Li, X., Wang, J. et al. Image segmentation based on U-Net++ network method to identify Bacillus Subtilis cells in micro-droplets. Multimed Tools Appl 83, 27747–27759 (2024). https://doi.org/10.1007/s11042-023-16509-0
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DOI: https://doi.org/10.1007/s11042-023-16509-0