Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for
automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While …

Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate
brain tumors by providing rich complementary information. Previous multi-modal MRI …

Automated brain tumor segmentation is crucial for aiding brain disease diagnosis and
evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely …

Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-
region analysis of brain tumors. Plenty of methods have been proposed for automatic brain …

Most existing brain tumor segmentation methods usually exploit multi-modal magnetic
resonance imaging (MRI) images to achieve high segmentation performance. However, the …

In this work, we propose a novel cascaded V-Nets method to segment brain tumor
substructures in multimodal brain magnetic resonance imaging. Although V-Net has been …

Brain tumor segmentation, which aims at segmenting the whole tumor area, enhancing
tumor core area, and tumor core area from each input multi-modality bio-imaging data, has …

Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is clinically
relevant in diagnoses, prognoses and surgery treatment, which requires multiple modalities …

Automatic segmentation of brain tumor regions from multimodal MRI scans is of great clinical
significance. In this letter, we propose a “Segmentation-Fusion” multi-task model named SF …

Accurate medical image segmentation commonly requires effective learning of the
complementary information from multimodal data. However, in clinical practice, we often …