The work is devoted to the development of methods for the automatic isolation and analysis of areas of interest for physician-researchers studying the effect of transplanted mesenchymal stem cells on foci of ischemic stroke in laboratory animals. Areas of interest are represented by ischemic areas on T2 MRI-scans and stem cell clusters on SWI MRI-scans. For segmentation of ischemic zones, the possibilities of identifying spectral and visual informative characteristics using the Fourier transform are considered. The formalization of the task of tracking stem cell clusters is reduced to a transportation problem. The most likely movement of the clusters is visualized using cognitive graphics, which helps the physician-researcher to formulate strategies for possible treatment using cell therapy.
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2. Skuratov A.G., Lyzikov A.N., Petrenev D.R. Metodiki trekinga transplantirovannyh mezenhimal'nyh stvolovyh kletok v organizme recipienta. Problemy zdorov'ya i ekologii. 2014; 4: 84–89. (In Russ. ).
3. Gal'ceva I.V., Mendeleeva L.P., Davydova Y u.O., Solov'ev M.V., Kapranov N.M., Kuz'mina L.A., Gribanova E.O., Gaponova T.V., Savchenko V.G. Issledovanie minimal'noj ostatochnoj bolezni metodom mnogocvetnoj protochnoj citofluorimetrii u bol'nyh mnozhestvennoj mielomoj posle transplantacii autologichnyh gemopoeticheskih stvolovyh kletok. Oncohematology. 2017; 2 : 62-69. (In Russ. ).
4. Gol'dshtejn D.V., Fathudinov T.H. Aktual'nye voprosy kletochnoj terapii miokarda. Vestnik RAMN. 2012 ; 4 : 16–24. (In Russ. ).
5. Tickaya E.V. i dr. Perspektivy primeneniya kletochnyh tekhnologij v reabilitacii bol'nyh serdechno-sosudistymi zabolevaniyami. Sovremennye problemy nauki i obrazovaniya. 2016 ; 6. (In Russ.).
6. Bisaga G.N. i dr. Primenenie mezenhimal'nyh stvolovyh kletok pri atrofii zritel'nyh nervov u bol'nyh rasseyannym sklerozom : pilotnoe issledovanie. Annaly klinicheskoj i eksperimental'noj nevrologii. 2017 ; 11( 2 ): 26-31. (In Russ. ).
7. Lyzikov A.N., Osipov B.B., Skuratov A.G., Prizencov A.A. Stvolovye kletki v regenerativnoj medicine: dostizheniya i perspektivy. Problemy zdorov'ya i ekologii. 2015 ; 3 : 4–8. (In Russ. ).
8. Fralenko V.P., Hachumov M.V., S h ustova M.V. Analiz instrumental'nyh sredstv obrabotki i vizualizacii biomedicinskih dannyh magnitno-rezonansnoj tomografii (obzor literatury). Vestnik novyh medicinskih tekhnologij. 2016; 4: 307–315. (In Russ.).
9. Zou M., Wang D. Texture identification and image segmentation via Fourier transform. Proceedings of the SPIE. 2001; 4550 : 34-39. doi : 10.1117/12.441495.
10. Malkov Y., Ponomarenko A., Krylov V., Logvinov A. Approximate nearest neighbor algorithm based on navigable small world graphs. Inf. Syst. 2014; 45: 61–68.
11. H astie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mini ng, Inference, and Prediction. 2nd ed. New York: Springer-Verlag, 2009. 745 p.
12. Fralenko V.P., S h ustova M.V. Programmnyj kompleks dlya avtomaticheskogo vydeleniya , vizualizacii irascheta informativnyh harakteristik oblastej interesa v biomedicinskih dannyh MRT. Vestnik novyh medicinskih tekhnologij , elektronnyj zhurnal. 2017 ; 4 : 255-262. (In Russ.).
13. Konieva A. A. Vliyanie ekzogennyh mezenhimal'nyh stvolovyh kletok placenty cheloveka na dinamiku nekotoryh patologicheskih processov CNS v eksperimente : diss. kand. medic. nauk. Moskva, 2010. 117 s. (In Russ.).
For citation
Shustova M.V., Fralenko V.P., Khachumov M.V. Isolation and analysis of areas of interest of a physician-researcher on MRI da ta of laboratory animals’ brain. Medical doctor and information technology. 2020; S1: 70-76. (In Russ.). doi : 10.37690/1811-0193-2020-S1-70-76.
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