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.
|Title of host publication||Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021|
|Number of pages||4|
|Publication status||Published - 26 May 2021|
|Name||Proceedings - 2021 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2021|
- neural network
- neuro-oncological MRI