The magnetic resonance (MR) analysis of brain tumors is widely used for diagnosis and
examination of tumor subregions. The overlapping area among the intensity distribution of …

In this work, we developed multiple 2D and 3D segmentation models with multiresolution
input to segment brain tumor components and then ensembled them to obtain robust …

Abstract 3D brain tumor segmentation is essential for the diagnosis, monitoring, and
treatment planning of brain diseases. In recent studies, the Deep Convolution Neural …

Background Accurate and automated brain tumor segmentation from multi‐modality MR
images plays a significant role in tumor treatment. However, the existing approaches mainly …

Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and
diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task …

Multi-modal three-dimensional (3-D) image segmentation is used in many medical
applications, such as disease diagnosis, treatment planning, and image-guided surgery …

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

Computer-aided diagnosis has emerged as a rapidly evolving field, garnering increased
attention in recent years. At the forefront of this field is the segmentation of lesions in medical …

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 …

Accurate brain tumor segmentation plays a pivotal role in clinical practice and research
settings. In this paper, we propose the multi-level up-sampling network (MU-Net) to learn the …