Multi-model data can enhance brain tumor segmentation for the rich information it provides.
However, it also introduces some redundant information that interferes with the …

Automatic segmentation of brain tumors from medical images is important for clinical
assessment and treatment planning of brain tumors. Recent years have seen an increasing …

Despite recent improvements in the accuracy of brain tumor segmentation, the results still
exhibit low levels of confidence and robustness. Uncertainty estimation is one effective way …

Automated segmentation of brain tumor is of great importance for the diagnosis and
treatment. Although manual segmentation could achieve high accuracy, it is time and labor …

Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%. A
quick and accurate diagnosis is crucial to increase the chance of survival. However, in …

Medical image segmentation is a crucial topic in medical image processing. Accurately
segmenting brain tumor regions from multimodal MRI scans is essential for clinical …

Segmentation is a routine and crucial procedure for the treatment of brain tumors. Deep
learning based brain tumor segmentation methods have achieved promising performance in …

Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D
volumes or employ 2D CNNs on 2D image slices. We discovered that while volume-based …

We describe our approach towards the segmentation task of the BRATS 2020 challenge. We
use the Probabilistic UNet to explore the effect of sampling different segmentation maps …

Accurate brain tumor segmentation is crucial for clinical assessment, follow-up, and
subsequent treatment of gliomas. While convolutional neural networks (CNN) have become …