3D Visualization of Brain Tumors via Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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.
Original languageEnglish
Title of host publicationProceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021
Pages280-283
Number of pages4
ISBN (Electronic)978-1-6654-3149-1
DOIs
Publication statusPublished - 26 May 2021

Publication series

NameProceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021

Keywords

  • dataset
  • neural network
  • neuro-oncological MRI
  • segmentation

OECD FOS+WOS

  • 1.06 BIOLOGICAL SCIENCES

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