Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction

Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%. A quick and accurate diagnosis is crucial for increasing the chances of survival. However, in medical analysis, the manual annotation and segmentation of brain tumors are complicated. Multiple MRI modalities are typically analyzed as they provide unique information about the tumor regions. Although using several MRI modalities helps segment brain tumors, they tend to increase overfitting and computation in computer models. This paper proposed a region of interest detection algorithm that was implemented during data preprocessing to locate salient features and remove extraneous MRI data. This decreased the input size of the model, allowing for more aggressive data augmentations and deeper neural networks. Following the preprocessing of the MRI modalities, a fully convolutional autoencoder segmented the different brain MRIs using channel-wise attention and attention gates. Subsequently, test-time augmentations and an energy-based model were used for voxel-based uncertainty predictions. Experimentation was conducted on the BraTS 2019, 2020, and 2021 benchmarks, and the proposed models achieved a state-of-the-art segmentation performance with mean dice scores of 84.55, 88.52, and 90.82 on each respective dataset. Additionally, qualitative results were used to assess the segmentation models and uncertainty predictions. Extensive experimentation showed that the proposed framework with cropping and attention mechanisms achieved state-of-the-art performance when compared to its contemporaries. The code for this work was made available online at https://github.com/WeToTheMoon/BrainTumorSegmentation.