Brain Tumor Segmentation Using UNet-Context Encoding Network

Abstract

Glioblastoma is an aggressive type of cancer that can develop in the brain or spinal cord. Magnetic Resonance Imaging (MRI) is key to diagnosing and tracking brain tumors in clinical settings. Brain tumor segmentation in MRI is required for disease diagnosis, surgical planning, and prognosis. As these tumors are heterogeneous in shape and appearance, their segmentation becomes a challenging task. The performance of automated medical image segmentation has considerably improved because of recent advances in deep learning. Introducing context encoding with deep CNN models has shown promise for semantic segmentation of brain tumors. In this work, we use a 3D UNet-Context Encoding (UNCE) deep learning network for improved brain tumor segmentation. Further, we introduce epistemic and aleatoric Uncertainty Quantification (UQ) using Monte Carlo Dropout (MCDO) and Test Time Augmentation (TTA) with the UNCE deep learning model to ascertain confidence in tumor segmentation performance. We build our model using the training MRI image sets of RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021. We evaluate the model performance using the validation and test images from the BraTS challenge dataset. Online evaluation of validation data shows dice score coefficients (DSC) of 0.7787, 0.8499, and 0.9159 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The dice score coefficients of the test datasets are 0.6684 for ET, 0.7056 for TC, and 0.7551 for WT, respectively.