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 -