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Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images.

• Authors : Zhong L; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Science, Beijing, 100190, China.

Subjects: Microscopy, Fluorescence*/Microscopy, Fluorescence*/Microscopy, Fluorescence*/methods ; Benchmarking*/Benchmarking*/Benchmarking*/methods ; Neural Networks, Computer*

• Source: BMC bioinformatics [BMC Bioinformatics] 2024 Aug 20; Vol. 25 (1), pp. 269. Date of Electronic Publication: 2024 Aug Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105

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Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images

• Authors : Liqun Zhong; Lingrui Li; Ge Yang

Subjects: Fluorescence microscopy; Semantic segmentation; Deep neural network

• Source: BMC Bioinformatics, Vol 25, Iss 1, Pp 1-28 (2024)

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Image segmentation and separation of spectrally similar dyes in fluorescence microscopy by dynamic mode decomposition of photobleaching kinetics.

• Authors : Wüstner, Daniel¹ (AUTHOR)

• Source: BMC Bioinformatics. 8/12/2022, Vol. 23 Issue 1, p1-23. 23p.

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Application of convolutional neural networks towards nuclei segmentation in localization-based super-resolution fluorescence microscopy images.

• Authors : Mela, Christopher A.^1,2 (AUTHOR); Liu, Yang² (AUTHOR)

• Source: BMC Bioinformatics. 6/15/2021 Supplement 3, Vol. 22, p1-30. 30p.

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CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images.

• Authors : Lee, Michael Y.^1,2,3 (AUTHOR); Bedia, Jacob S.⁴ (AUTHOR); Bhate, Salil S.^1,2,5 (AUTHOR)

• Source: BMC Bioinformatics. 1/18/2022, Vol. 23 Issue 1, p1-17. 17p.

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Image segmentation and separation of spectrally similar dyes in fluorescence microscopy by dynamic mode decomposition of photobleaching kinetics

• Authors : Daniel Wüstner

Subjects: Spatiotemporal modeling; Fluorescence; Photobleaching

• Source: BMC Bioinformatics, Vol 23, Iss 1, Pp 1-23 (2022)

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FocAn: automated 3D analysis of DNA repair foci in image stacks acquired by confocal fluorescence microscopy.

• Authors : Memmel, Simon¹ (AUTHOR); Sisario, Dmitri² (AUTHOR); Zimmermann, Heiko^3,4,5 (AUTHOR)

• Source: BMC Bioinformatics. 1/28/2020, Vol. 21 Issue 1, p1-11. 11p. 1 Color Photograph, 2 Graphs.

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Exploiting geometric similarity for statistical quantification of fluorescence spatial patterns in bacterial colonies.

• Authors : Espeso, David R.¹ (AUTHOR); Algar, Elena¹ (AUTHOR); Martínez-García, Esteban¹ (AUTHOR)

• Source: BMC Bioinformatics. 6/3/2020, Vol. 21 Issue 1, p1-13. 13p. 2 Color Photographs, 2 Diagrams.

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Intelligent fluorescence image analysis of giant unilamellar vesicles using convolutional neural network

• Authors : Il-Hyung Lee; Sam Passaro; Selin Ozturk

Subjects: Image analysis; Fluorescence imaging; Cognitive neural network

• Source: BMC Bioinformatics, Vol 23, Iss 1, Pp 1-22 (2022)

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Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images

• Authors : Wu, Allison Chia-Yi; Rifkin, Scott A

Subjects: Information and Computing Sciences; Biological Sciences; Computer Vision and Multimedia Computation

• Source: BMC Bioinformatics. 16(1)

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