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3D Visualization of Brain Tumors via Artificial Intelligence. / Letyagin, Andrey; Amelina, Evgeniya; Tuchinov, Bair et al.
Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 280-283 9496040 (Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Letyagin, A
, Amelina, E, Tuchinov, B, Groza, V, Tolstokulakov, N
, Amelin, M, Golushko, S & Pavlovskiy, E 2021,
3D Visualization of Brain Tumors via Artificial Intelligence. in
Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021., 9496040, Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021, Institute of Electrical and Electronics Engineers Inc., pp. 280-283, 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021, Novosibirsk, Yekaterinburg, Russian Federation,
26.05.2021.
https://doi.org/10.1109/CSGB53040.2021.9496040APA
Letyagin, A.
, Amelina, E., Tuchinov, B., Groza, V., Tolstokulakov, N.
, Amelin, M., Golushko, S., & Pavlovskiy, E. (2021).
3D Visualization of Brain Tumors via Artificial Intelligence. In
Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021 (pp. 280-283). [9496040] (Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021). Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/CSGB53040.2021.9496040Vancouver
Letyagin A
, Amelina E, Tuchinov B, Groza V, Tolstokulakov N
, Amelin M et al.
3D Visualization of Brain Tumors via Artificial Intelligence. In Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 280-283. 9496040. (Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021). doi: 10.1109/CSGB53040.2021.9496040
Author
Letyagin, Andrey
; Amelina, Evgeniya ; Tuchinov, Bair et al. /
3D Visualization of Brain Tumors via Artificial Intelligence. Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 280-283 (Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021).
BibTeX
@inproceedings{982865af094d40c1928192df44d192d3,
title = "3D Visualization of Brain Tumors via Artificial Intelligence",
abstract = "Neuro-oncological MRI imaging is a complex, expensive procedure that is responsible for all further treatment tactics. The following issues must be unambiguously resolved: (1) to detect a volumetric process in the brain (e.g., tumor); (2) to outline the exact boundaries of the tumor (to delimit the edematous zone and healthy brain tissue); (3) to determine the level of tumor malignancy as accurately as possible. Artificial intelligence technologies make it possible to speed up the process of MRI diagnostics via 3D visualization and increase its accuracy and specificity. This paper presents pipeline and approaches to the creation of a dataset, which can serve as a basis for solving the problems mentioned above. The description of the dataset which is formed in our research project is presented. The methods and algorithms that were used to solve the problem of multiclass segmentation of the tumor are also described. ",
keywords = "dataset, neural network, neuro-oncological MRI, segmentation",
author = "Andrey Letyagin and Evgeniya Amelina and Bair Tuchinov and Vladimir Groza and Nikolay Tolstokulakov and Mikhail Amelin and Sergey Golushko and Evgeniy Pavlovskiy",
note = "Funding Information: The reported study was funded by RFBR according to the research project No 19-29-01103. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021 ; Conference date: 26-05-2021 Through 28-05-2021",
year = "2021",
month = may,
day = "26",
doi = "10.1109/CSGB53040.2021.9496040",
language = "English",
isbn = "9781665431491",
series = "Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "280--283",
booktitle = "Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021",
address = "United States",
}
RIS
TY - GEN
T1 - 3D Visualization of Brain Tumors via Artificial Intelligence
AU - Letyagin, Andrey
AU - Amelina, Evgeniya
AU - Tuchinov, Bair
AU - Groza, Vladimir
AU - Tolstokulakov, Nikolay
AU - Amelin, Mikhail
AU - Golushko, Sergey
AU - Pavlovskiy, Evgeniy
N1 - Funding Information:
The reported study was funded by RFBR according to the research project No 19-29-01103.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/26
Y1 - 2021/5/26
N2 - Neuro-oncological MRI imaging is a complex, expensive procedure that is responsible for all further treatment tactics. The following issues must be unambiguously resolved: (1) to detect a volumetric process in the brain (e.g., tumor); (2) to outline the exact boundaries of the tumor (to delimit the edematous zone and healthy brain tissue); (3) to determine the level of tumor malignancy as accurately as possible. Artificial intelligence technologies make it possible to speed up the process of MRI diagnostics via 3D visualization and increase its accuracy and specificity. This paper presents pipeline and approaches to the creation of a dataset, which can serve as a basis for solving the problems mentioned above. The description of the dataset which is formed in our research project is presented. The methods and algorithms that were used to solve the problem of multiclass segmentation of the tumor are also described.
AB - Neuro-oncological MRI imaging is a complex, expensive procedure that is responsible for all further treatment tactics. The following issues must be unambiguously resolved: (1) to detect a volumetric process in the brain (e.g., tumor); (2) to outline the exact boundaries of the tumor (to delimit the edematous zone and healthy brain tissue); (3) to determine the level of tumor malignancy as accurately as possible. Artificial intelligence technologies make it possible to speed up the process of MRI diagnostics via 3D visualization and increase its accuracy and specificity. This paper presents pipeline and approaches to the creation of a dataset, which can serve as a basis for solving the problems mentioned above. The description of the dataset which is formed in our research project is presented. The methods and algorithms that were used to solve the problem of multiclass segmentation of the tumor are also described.
KW - dataset
KW - neural network
KW - neuro-oncological MRI
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=85112390413&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/eb51b4a6-45ce-33b4-bc74-d12a7a5d39bb/
U2 - 10.1109/CSGB53040.2021.9496040
DO - 10.1109/CSGB53040.2021.9496040
M3 - Conference contribution
AN - SCOPUS:85112390413
SN - 9781665431491
T3 - Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021
SP - 280
EP - 283
BT - Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021
Y2 - 26 May 2021 through 28 May 2021
ER -
