Papers
Topics
Authors
Recent
AI Research Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 85 tok/s
Gemini 2.5 Pro 46 tok/s Pro
GPT-5 Medium 16 tok/s Pro
GPT-5 High 10 tok/s Pro
GPT-4o 108 tok/s Pro
Kimi K2 192 tok/s Pro
GPT OSS 120B 455 tok/s Pro
Claude Sonnet 4 31 tok/s Pro
2000 character limit reached

Cellpose+, a morphological analysis tool for feature extraction of stained cell images (2410.18738v1)

Published 24 Oct 2024 in cs.CV and cs.AI

Abstract: Advanced image segmentation and processing tools present an opportunity to study cell processes and their dynamics. However, image analysis is often routine and time-consuming. Nowadays, alternative data-driven approaches using deep learning are potentially offering automatized, accurate, and fast image analysis. In this paper, we extend the applications of Cellpose, a state-of-the-art cell segmentation framework, with feature extraction capabilities to assess morphological characteristics. We also introduce a dataset of DAPI and FITC stained cells to which our new method is applied.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (40)
  1. Mey J, Thanos S. Development of the visual system of the chick: I. Cell differentiation and histogenesis. Brain research reviews. 2000;32(2-3):343-79.
  2. Deep learning neural networks highly predict very early onset of pluripotent stem cell differentiation. Stem cell reports. 2019;12(4):845-59.
  3. Deep learning in microscopy image analysis: A survey. IEEE transactions on neural networks and learning systems. 2017;29(10):4550-68.
  4. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nature methods. 2019;16(1):103-10.
  5. Deep learning massively accelerates super-resolution localization microscopy. Nature biotechnology. 2018;36(5):460-8.
  6. Analysis of live cell images: Methods, tools and opportunities. Methods. 2017;115:65-79.
  7. Data-analysis strategies for image-based cell profiling. Nature methods. 2017;14(9):849-63.
  8. Workflow and metrics for image quality control in large-scale high-content screens. Journal of biomolecular screening. 2012;17(2):266-74.
  9. Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS computational biology. 2016;12(11):e1005177.
  10. Deep learning for cellular image analysis. Nature methods. 2019;16(12):1233-46.
  11. CellProfiler 4: improvements in speed, utility and usability. BMC bioinformatics. 2021;22:1-11.
  12. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nature protocols. 2016;11(9):1757-74.
  13. A simple ImageJ macro tool for analyzing mitochondrial network morphology in mammalian cell culture. Acta histochemica. 2017;119(3):315-26.
  14. SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells. Molecular microbiology. 2016;102(4):690-700.
  15. Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 2961-9.
  16. Cell detection with star-convex polygons. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11. Springer; 2018. p. 265-73.
  17. Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities. Elife. 2021;10:e65151.
  18. Pachitariu M, Stringer C. Cellpose 2.0: how to train your own model. Nature methods. 2022;19(12):1634-41.
  19. Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation. Nature methods. 2022;19(11):1438-48.
  20. Microscopy cell counting and detection with fully convolutional regression networks. Computer methods in biomechanics and biomedical engineering: Imaging & Visualization. 2018;6(3):283-92.
  21. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE transactions on medical imaging. 2015;35(1):119-30.
  22. Biosynthesis of multi-component polyhydroxyalkanoates by the bacterium Wautersia eutropha. Journal of Syberian Federal University Biology. 2008;1(1):91-101.
  23. Properties of degradable polyhydroxyalkanoates with different monomer compositions. International Journal of Biological Macromolecules. 2021;182:98-114.
  24. Kiselev E. Technical and technological bases of biosynthesis of reserve polyhydroxyalkanoates by hydrogen bacteria. Summary of the Candidate of Sciences Thesis. 2012:12-5.
  25. A glucose-utilizing strain, Cupriavidus euthrophus B-10646: growth kinetics, characterization and synthesis of multicomponent PHAs. PLoS One. 2014;9(2):e87551.
  26. Insight on relationship between crystallinity and band gap energies of polyhydroxyalkanoates polymers. Materials Today Communications. 2024;39:108886.
  27. Resorbable nanomatrices from microbial polyhydroxyalkanoates: design strategy and characterization. Nanomaterials. 2022;12(21):3843.
  28. Weaver BA, Cleveland DW. Decoding the links between mitosis, cancer, and chemotherapy: The mitotic checkpoint, adaptation, and cell death. Cancer cell. 2005;8(1):7-12.
  29. H&E multi-laboratory staining variance exploration with machine learning. Applied Sciences. 2022;12(15):7511.
  30. Couture HD. Deep learning-based prediction of molecular tumor biomarkers from H&E: a practical review. Journal of Personalized Medicine. 2022;12(12):2022.
  31. Multi-pass adaptive voting for nuclei detection in histopathological images. Scientific reports. 2016;6(1):33985.
  32. Harnessing nanotopography and integrin–matrix interactions to influence stem cell fate. Nature materials. 2014;13(6):558-69.
  33. Cell–material interactions. In: Tissue Engineering. Elsevier; 2023. p. 261-92.
  34. LIVE/DEAD® BacLight™: application of a new rapid staining method for direct enumeration of viable and total bacteria in drinking water. Journal of microbiological Methods. 1999;37(1):77-86.
  35. Azithromycin protects retinal glia against oxidative stress-induced morphological changes, inflammation, and cell death. ACS bio & med Chem Au. 2022;2(5):499-508.
  36. Cell shape and cartilage differentiation of early chick limb bud cells in culture. Cell differentiation. 1982;11(4):245-51.
  37. Geometric cues for directing the differentiation of mesenchymal stem cells. Proceedings of the National Academy of Sciences. 2010;107(11):4872-7.
  38. Image-based discrimination of the early stages of mesenchymal stem cell differentiation. Molecular Biology of the Cell. 2024;35(8):ar103.
  39. Epigenetic and transcriptional regulations prime cell fate before division during human pluripotent stem cell differentiation. Nature Communications. 2023;14(1):405.
  40. Ultrasonically Produced Porous Sponge Layer on Titanium to Guide Cell Behavior. Advanced Engineering Materials. 2016;18(4).

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 1 post and received 0 likes.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up to View