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 …

Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …

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

Abstract Using multimodal Magnetic Resonance Imaging (MRI) is necessary for accurate
brain tumor segmentation. The main problem is that not all types of MRIs are always …

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

For medical imaging tasks, it is a prevalent practice to have a multi-modality image dataset,
as experts prefer using multiple medical devices to diagnose a disease. Each device can …

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 …

Medical practitioners generally rely on multimodal brain images, for example based on the
information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis …

Accurate medical image segmentation of brain tumors is necessary for the diagnosing,
monitoring, and treating disease. In recent years, with the gradual emergence of multi …

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 …