MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures
This work provides an improved method for automated brain tumor segmentation and uncertainty estimation, which is critical for clinicians in medical diagnosis and treatment planning. It is an incremental improvement over existing methods.
This paper addresses the challenge of automated brain tumor segmentation in 3D MRI scans, which is crucial for diagnosis and treatment assessment. The authors developed an ensemble of 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and mitigate data imbalance, achieving improved segmentation performance. They also incorporated voxel-wise epistemic and aleatoric uncertainty estimation using test-time dropout and data augmentation, respectively.
Automation of brain tumor segmentation in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is especially critical in medical diagnosis. This work studies 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and decrease the effect of unbalanced data. The different trained models are then used to create an ensemble that leverages the properties of each model, thus increasing the performance. We also introduce voxel-wise uncertainty information, both epistemic and aleatoric using test-time dropout (TTD) and data-augmentation (TTA) respectively. In addition, a hybrid approach is proposed that helps increase the accuracy of the segmentation. The model and uncertainty estimation measurements proposed in this work have been used in the BraTS'20 Challenge for task 1 and 3 regarding tumor segmentation and uncertainty estimation.