Brain tumor segmentation is a key step in brain cancer diagnosis. Segmentation of brain
tumor sub-regions, including necrotic, enhancing, and edematous regions, can provide …

The recent state-of-the-art deep learning methods have significantly improved brain tumor
segmentation. However, fully supervised training requires a large amount of manually …

Numerous techniques excel in brain tumor segmentation using multi-modal magnetic
resonance imaging (MRI) sequences, delivering exceptional results. However, the prevalent …

Automatic segmentation of brain tumors from multi-modalities magnetic resonance image
data has the potential to enable preoperative planning and intraoperative volume …

Brain tumor segmentation is a fundamental task and existing approaches usually rely on
multi-modality magnetic resonance imaging (MRI) images for accurate segmentation …

In this paper, we devise a novel two-stage cascaded U-Net to segment the substructures of
brain tumors from coarse to fine. The network is trained end-to-end on the Multimodal Brain …

Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain
tumor diagnosis, cancer management and research purposes. With the great success of the …

Accurate segmentation is an essential task when working with medical images. Recently,
deep convolutional neural networks achieved a state-of-the-art performance for many …

Abstract Although Magnetic Resonance Imaging (MRI) is very helpful for brain tumor
segmentation and discovery, it often lacks some modalities in clinical practice. As a result …

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 …