Brain tumor segmentation is the task of segmenting tumors from other brain artefacts in MRI image of the brain.
( Image credit: Brain Tumor Segmentation with Deep Neural Networks )
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Multimodal MR images can provide complementary information for accurate brain tumor segmentation.
The brain is a complex organ controlling cognitive process and physical functions.
Segmentation of brain tumors and their subregions remains a challenging task due to their weak features and deformable shapes.
We tackle this challenge and propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities.
Intracranial tumors are groups of cells that usually grow uncontrollably.
Multimodal brain tumor segmentation challenge (BraTS) brings together researchers to improve automated methods for 3D MRI brain tumor segmentation.
The overall accura-cy of the proposed model for the survival prediction task is %52 for the valida-tion and %49 for the test dataset.
Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to obtain.
In this paper, we use only two kinds of weak labels, i. e., scribbles on whole tumor and healthy brain tissue, and global labels for the presence of each substructure, to train a deep learning model to segment all the sub-regions.