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 -