Bionic model of blood cell segmentation based on impulse image transformation

1Kuzomin 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.Search in Google Scholar

2Abu-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.Search in Google Scholar

3Markushevska АV, Savchenko МО. Mathematical simulation of blood movement in vessels. Bull Stud Sci Soc. 2021;2(13):316-319.Search in Google Scholar

4Morozova 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/cryo30.03.290Search in Google Scholar

5Batyuk 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/10.17721/1812-5409.2022/1.4Search in Google Scholar

6Novytskyy 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/10.17721/1812-5409.2021/4.8Search in Google Scholar

7Batyuk 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.Search in Google Scholar

8Pertsov 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/10.26565/2311-0872-2021-34-08Search in Google Scholar

9Cao 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.v34i07.6619Search in Google Scholar

10Ko 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.2011.03.009Search in Google Scholar

11Shah 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.2021.3114059Search in Google Scholar

12Navya 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-zSearch in Google Scholar

13Basu 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/s12530-023-09491-3Search in Google Scholar

14World 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/Search in Google Scholar

15Mohapatra 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.2010.5706196Search in Google Scholar

16Li 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/10.1155/2016/9514707Search in Google Scholar

17Wang Y, Cao Y. Quick leukocyte nucleus segmentation in leukocyte counting. Comput Math Methods Med. 2019;3072498. https://doi.org/10.1155/2019/3072498Search in Google Scholar

18Yang 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/S1793545814500072Search in Google Scholar

19Rabotiahov 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.2018.8336289Search in Google Scholar

20Lyashenko 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.2018.8632156Search in Google Scholar

21Wang 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.2021.119890Search in Google Scholar

22Li 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.0c17465Search in Google Scholar

23Li 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.37322Search in Google Scholar

24Chen 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.2022.05.075Search in Google Scholar

25Zhao 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.2021.10.154Search in Google Scholar

26Li 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.35640Search in Google Scholar

27Li 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.9b20264Search in Google Scholar

28Huang 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.2018.11.002Search in Google Scholar

29Li 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.36604Search in Google Scholar

30Wang 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.2020.04.025Search in Google Scholar

31Zheng 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.2022.112895Search in Google Scholar

32Zhu 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/jev2.12255Search in Google Scholar

33Miao 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.2c00893Search in Google Scholar

34Meng 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.2021.108096Search in Google Scholar

35Huang 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.202202790Search in Google Scholar

36Zhao 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.202208824Search in Google Scholar

37Liu 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/D1TB02706ASearch in Google Scholar

38Chatterjee 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.2021.03.061Search in Google Scholar

39Bello 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.2018.03.028Search in Google Scholar

40Gilchrist 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/zyaa013Search in Google Scholar

41Zhang 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.2023.03.005Search in Google Scholar

42Mousavi 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/ICCKE57176.2022.9960108Search in Google Scholar

43Matern 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.2019.2935913Search in Google Scholar

44Liao 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.v34i07.6812Search in Google Scholar

45Su 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/10.1155/2022/1267080Search in Google Scholar

46Patil 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.2020.08.005Search in Google Scholar