Have a personal or library account? Click to login

Bionic model of blood cell segmentation based on impulse image transformation

Open Access
|Dec 2024

References

  1. Kuzomin O, Abu-Jassar AT, Lyashenko V. Forecasting and decision making in the context of COVID. Int J Acad Inf Syst Res. 2023;7(6):89-94.
  2. Abu-Jassar AT, Sotnik S, Sinelnikova T, Lyashenko V. Binarization methods in multimedia systems when recognizing license plates of cars. Int J Acad Eng Res. 2023;7(2):1-9.
  3. Markushevska АV, Savchenko МО. Mathematical simulation of blood movement in vessels. Bull Stud Sci Soc. 2021;2(13):316-319.
  4. Morozova ОМ, Batyuk LV, Muraveinik ОА. Mathematical modeling of red blood cell shape change in early neuroprotection with moderate therapeutic effect of hypothermia. Probl Cryobiol Cryomed. 2020;30(3):290. https://doi.org/10.15407/cryo30.03.290
  5. Batyuk LV, Kizilova NМ. Modeling of blood cell surface oscillations as fluid-filled multilayer viscoelastic shells. Bull Taras Shevchenko Natl Univ Kyiv Ser: Phys Math. 2022;1:40-43. https://doi.org/10.17721/1812-5409.2022/1.4
  6. Novytskyy VV, Novytskyy Jr VV. Mathematical model of erythrocyte in the capillary motion. Bull Taras Shevchenko Natl Univ Kyiv Ser Phys Math. 2021;4:56-61. https://doi.org/10.17721/1812-5409.2021/4.8
  7. Batyuk LV, Kizilova NМ. Modeling of laminar flows of erythrocyte suspensions as Binhgam microfluids. Bull Taras Shevchenko Natl Univ Kyiv Ser: Phys Math. 2017;4:23-28.
  8. Pertsov ОV, Berest VP. Analysis of kinetics of light scattering by cell suspection during aggregation: Mathematical modeling of platelet disaggregation. Visnyk of VN Karazin Kharkiv Natl Univ, Ser “Radio Phys Electron.”. 2021;34:70-77. https://doi.org/10.26565/2311-0872-2021-34-08
  9. Cao B, Zhang H, Wang N, Gao X, Shen D. Auto-GAN: Self-supervised collaborative learning for medical image synthesis. Proceed of the AAAI Conf AI. 2020;34(7):10486-10493. https://doi.org/10.1609/aaai.v34i07.6619
  10. Ko BC, Gim JW, Nam JY. Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron. 2011;42(7):695-705. https://doi.org/10.1016/j.micron.2011.03.009
  11. Shah A, Naqvi S, Naveed K, Salem N, Khan M, Alimgir K. Automated Diagnosis of Leukemia: A Comprehensive Review. IEEE Access. 2021;9:132097-132124. https://doi.org/10.1109/ACCESS.2021.3114059
  12. Navya KT, Prasad K, Singh BMK. Analysis of red blood cells from peripheral blood smear images for anemia detection: A methodological review. Med Biol Eng Comput. 2022;60(9):2445-2462. https://doi.org/10.1007/s11517-022-02614-z
  13. Basu A, Senapati P, Deb M, Rai R, Dhal KG. A survey on recent trends in deep learning for nucleus segmentation from histopathology images. Evolving Systems. 2024;15:203-248. https://doi.org/10.1007/s12530-023-09491-3
  14. World Medical Association. 2022. WMA Declaration Helsinki – Ethical Princ. Med. Research Involv. Human Subj. https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/
  15. Mohapatra S, Patra D. Automated leukemia detection using hausdorff dimension in blood microscopic images. In: Proceed Int Conf IEEE Robotics and Commun Technol (INTERACT-2010). 2010:64-68. https://doi.org/10.1109/INTERACT.2010.5706196
  16. Li Y, Zhu R, Mi L, Cao Y, Yao D. Segmentation of white blood cell from acute lymphoblastic leukemia images using dual-threshold method. Comput. Math. Methods Med. 2016;9514707. https://doi.org/10.1155/2016/9514707
  17. Wang Y, Cao Y. Quick leukocyte nucleus segmentation in leukocyte counting. Comput Math Methods Med. 2019;3072498. https://doi.org/10.1155/2019/3072498
  18. Yang Y, Cao Y, Shi W. A method of leukocyte segmentation based on S component and B component images. J Innovative Opt Health Sci. 2014;7(1):1450007. https://doi.org/10.1142/S1793545814500072
  19. Rabotiahov A, Kobylin O, Dudar Z, Lyashenko V. Bionic image segmentation of cytology samples method. In: 2018 14th Int Conf Adv Trends Radioelecrtron, Telecommun Comp Eng (TCSET). 2018;665-670. https://doi.org/10.1109/TCSET.2018.8336289
  20. Lyashenko V, Rabotiahov A, Kobylin О, Kolesnykov D. Analysis of human speech as a protection tool in infocommunication systems. In: 2018 Int Sci-Pract Conf “Probl Infocommun Sci Tech”. 2018;79-83. https://doi.org/10.1109/INFOCOMMST.2018.8632156
  21. Wang H, Ma H, Fang P, et al. Dynamic confocal Raman spectroscopy of flowing blood in bionic blood vessel. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2021;259:119890. https://doi.org./10.1016/j.saa.2021.119890
  22. Li J, Ye W, Fan Z, Cao L. A Novel Stereocomplex Poly(lactic acid) with Shish-Kebab Crystals and Bionic Surface Structures as Bioimplant Materials for Tissue Engineering Applications. ACS Appl Mater Interfaces. 2021;13(4):5469-5477. https://doi.org/10.1021/acsami.0c17465
  23. Li Z, Wu T, Chen Y, Gao X, Ye J, Jin Y, Chen B. Oriented homo-epitaxial crystallization of polylactic acid displaying a biomimetic structure and improved blood compatibility. J Biomed Mater Res: Part A. 2022;110(3):684-695. https://doi.org/10.1002/jbm.a.37322
  24. Chen Y, Yang W, Hu Z, et al. Preparation and properties of oriented microcellular Poly(l-lactic acid) foaming material. International Journal of Biological Macromolecules. 2022;211:460-469. https://doi.org/10.1016/j.ijbiomac.2022.05.075
  25. Zhao X, Li J, Liu J, Zhou W, Peng S. Recent progress of preparation of branched poly(lactic acid) and its application in the modification of polylactic acid materials. Int J Biol Macromol. 2021;193(Part A):874-892. https://doi.org/10.1016/j.ijbiomac.2021.10.154
  26. Li Z, Ye L, Zhao X, Coates P, Caton-Rose F, Martyn M. High orientation of long chain branched poly (lactic acid) with enhanced blood compatibility and bionic structure. J Biomed Mater Res: Part A. 2016;104(5):1082-1089. https://doi.org/10.1002/jbm.a.35640
  27. Li J, Chen Q, Zhang Q, Fan T, Gong L, Ye W, Fan Z, Cao L. Improving mechanical properties and biocompatibilities by highly oriented long chain branching poly(lactic acid) with bionic surface structures. ACS Appl Materials & Interfaces. 2020;12(12):14365-14375. https://doi.org/10.1021/acsami.9b20264
  28. Huang L, Tan J, Li W, Zhou L, Liu Z, Luo B, Lu L, Zhou, C. Functional polyhedral oligomeric silsesquioxane reinforced poly(lactic acid) nanocomposites for biomedical applications. J Mech Behav Biomed Mater. 2019;90:604-614. https://doi.org/10.1016/j.jmbbm.2018.11.002
  29. Li J, Zhao X, Ye L, Coates P, Caton-Rose F. Multiple shape memory behavior of highly oriented long-chain-branched poly(lactic acid) and its recovery mechanism. J Biomed Mater Res: Part A. 2019;107(4):872-883. https://onlinelibrary.wiley.com/doi/10.1002/jbm.a.36604
  30. Wang K, Lu J, Tusiime R, Yang Y, Fan F, Zhang H, Ma B. Properties of poly (L-lactic acid) reinforced by L-lactic acid grafted nanocellulose crystal. Int J Biol Macromol. 2020;156:314-320. https://doi.org/10.1016/j.ijbiomac.2020.04.025
  31. Zheng BD, Xiao MT. Red blood cell membrane nanoparticles for tumor phototherapy. Colloids Surfaces B: Biointerfaces. 2022;220:112895. https://doi.org/10.1016/j.colsurfb.2022.112895
  32. Zhu Z, Zhai Y, Hao Y, Wang Q, Han F, Zheng W, Hong J, Cui L, Jin W, Ma S, Yang L, Cheng G. Specific anti-glioma targeted-delivery strategy of engineered small extracellular vesicles dual-functionalised by Angiopep-2 and TAT peptides. J Extracell Vesicles. 2022;11(8):e12255. https://doi.org/10.1002/jev2.12255
  33. Miao Y, Yang Y, Guo L, Chen M, Zhou X, Zhao Y, Nie D, Gan Y, Zhang X. Cell membrane-camouflaged nanocarriers with biomimetic deformability of erythrocytes for ultralong circulation and enhanced cancer therapy. ACS Nano. 2022;16(4):6527-6540. https://doi.org/10.1021/acsnano.2c00893
  34. Meng Q, Pu L, Lu Q, Wang B, Li S, Liu B, Li F. Morin hydrate inhibits atherosclerosis and LPS-induced endothelial cells inflammatory responses by modulating the NFκB signaling-mediated autophagy. Int Immunopharmacol. 2022;100:108096. https://doi.org/10.1016/j.intimp.2021.108096
  35. Huang Y, Wu H, Xie N, Zhang X, Zou Z, Deng M, Cheng W, Guo X, Ding S, Guo B. Conductive antifouling sensing coating: A bionic design inspired by natural cell membrane. Adv Healthcare Mater. 2023;12(13):2202790. https://doi.org/10.1002/adhm.202202790
  36. Zhao Z, Pan M, Qiao C, Xiang L, Liu X, Yang W, Chen XZ, Zeng H. Bionic engineered protein coating boosting anti-biofouling in complex biological fluids. Adv Mater. 2023;35(6):2208824. https://doi.org/10.1002/adma.202208824
  37. Liu B, Tao C, Wu Z, Yao H, Wang DA. Engineering strategies to achieve efficient in vitro expansion of haematopoietic stem cells: Development and improvement. J Mater Chem B. 2022;10(11):1734-1753. https://doi.org/10.1039/D1TB02706A
  38. Chatterjee C, Schertl P, Frommer M, et al. Rebuilding the hematopoietic stem cell niche: Recent developments and future prospects. Acta Biomaterialia. 2021;132:129-148. https://doi.org/10.1016/j.actbio.2021.03.061
  39. Bello AB, Park H, Lee SH. Current approaches in biomaterial-based hematopoietic stem cell niches. Acta Biomaterialia. 2018;72:1-15. https://doi.org/10.1016/j.actbio.2018.03.028
  40. Gilchrist AE, Harley BAC. Connecting secretome to hematopoietic stem cell phenotype shifts in an engineered bone marrow niche. Integr Biol. 2020;12(7):175-187. https://doi.org/10.1093/intbio/zyaa013
  41. Zhang X, Cao D, Xu L, et al. Harnessing matrix stiffness to engineer a bone marrow niche for hematopoietic stem cell rejuvenation. Cell Stem Cell. 2023;30(4):378-395. https://doi.org/10.1016/j.stem.2023.03.005
  42. Mousavi SMH, Lyashenko VV, Ilanloo A, Mirinezhad SY. Fatty liver level recognition using Particle Swarm optimization (PSO) image segmentation and analysis. In: 2022 12th Int Conf Comput Knowl Eng (ICCKE). 2022;237-245. https://doi.org/10.1109/ICCKE57176.2022.9960108
  43. Matern F, Riess C, Stamminger, M. Gradient-based illumination description for image forgery detection. IEEE Transactions Inf Forensics Security. 2019;15:1303-1317. https://doi.org/10.1109/TIFS.2019.2935913
  44. Liao M, Wan Z, Yao C, Chen K, Bai X. Real-time scene text detection with differentiable binarization. Proceed AAAI Conf AI. 2020;34(7):11474-11481. https://doi.org/10.1609/aaai.v34i07.6812
  45. Su Y, Zang Y, Su Q, Peng L. A method for expanding the training set of white blood cell images. J Healthcare Eng. 2022;1267080. https://doi.org/10.1155/2022/1267080
  46. Patil AM, Patil MD, Birajdar GK. White blood cells image classification using deep learning with canonical correlation analysis. IRBM. 2021;42(5):378-389. https://doi.org/10.1016/j.irbm.2020.08.005
DOI: https://doi.org/10.2478/pjmpe-2024-0027 | Journal eISSN: 1898-0309 | Journal ISSN: 1425-4689
Language: English
Submitted on: Mar 20, 2024
Accepted on: Sep 28, 2024
Published on: Dec 23, 2024
Published by: Polish Society of Medical Physics
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Roman Yu. Bukhtiiarov, Anatoliy V. Tarasov, Andriy V. Rabotiahov, Victor M. Cheverda, Alexander Gigolaev, published by Polish Society of Medical Physics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.