Abstract
Glioblastoma is the most common and lethal primary brain tumor in adults. Magnetic resonance imaging (MRI) is a critical diagnostic tool for glioblastoma. Besides MRI, histopathology features and molecular subtypes like MGMT methylation, IDH mutation, 1p19q co-deletion, etc. are used for prognosis. Accurate tumor segmentation is a step towards fully utilizing the MRI data for radiogenomics that will allow use of MRI to predict genomic features of glioblastoma. With accurate tumor segmentation, we can get precise quantitative information about the 3D tumor volumetric features. We have developed an inference model for brain tumor segmentation using neural network algorithm with Resnet50 as an encoding layer. Major feature of our algorithm is the use of composite image generated from T1, T2, T1ce and FLAIR series. We report average Dice scores of 0.88716 for the whole tumor, 0.79052 for the necrotic core, and 0.72760 for the contrast-enhancing tumor on the validation set of BraTS 2021 Task1 challenge. For the final unseen test data, we report average Dice scores of 0.89656 for the whole tumor, 0.83734 for the necrotic core, and 0.81162 for the contrast-enhancing tumor.
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Shah, D., Biswas, A., Sonpatki, P., Chakravarty, S., Shah, N. (2022). Neural Network Based Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_29
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