Brain Tumor Segmentation with Self-supervised Enhance Region Post-processing

Sergey Pnev, Vladimir Groza, Bair Tuchinov, Evgeniya Amelina, Evgeniy N. Pavlovskiy, Nikolay Tolstokulakov, Mihail Amelin, Sergey Golushko, Andrey Letyagin

Abstract: In this paper, we extend the previous research works on the robust multi-sequences segmentation methods which allows to consider all available information from MRI scans by the composition of T1, T1C, T2 and T2-FLAIR sequences. It is based on the clinical radiology hypothesis and presents an efficient approach to combining and matching 3D methods to search for areas of comprised the GD-enhancing tumor in order to significantly improve the model’s performance of the particular applied numerical problem of brain tumor segmentation.