目的针对目前基于深度学习的脑肿瘤分割算法参数量大, 计算复杂和快速性差的问题,
提出了一种超轻量级快速语义分割网络LRUNet (lightweight rapid UNet) …

Semantic segmentation plays an essential role in brain tumor diagnosis and treatment
planning. Yet, manual segmentation is a time-consuming task. That fact leads to hire the …

目的脑肿瘤是一种严重威胁人类健康的疾病. 利用计算机辅助诊断进行脑肿瘤分割对于患者的
预后和治疗具有重要的临床意义. 3D 卷积神经网络因具有空间特征提取充分 …

Semantic segmentation plays an important role in the prevention, diagnosis and treatment of
brain glioma. In this paper, we propose a dense channels 2D U-net segmentation model …

Semantic segmentation technology based on deep learning has played an important role for
doctors in identifying brain tumor regions and formulating treatment plans. Popular …

目的磁共振成像(magnetic resonance imaging, MRI) 作为一种非侵入性的软组织对比成像方式,
可以提供有关脑肿瘤的形状, 大小和位置等有价值的信息, 是用于脑肿瘤患者检查的主要方法 …

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 …

Abstract Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art
performance in medical image segmentation tasks. A common feature in most top …

Although the U-shaped architecture, represented by UNet, has become a major network
model for brain tumor segmentation, the repeated convolution and sampling operations can …

目的脑肿瘤核磁共振(magnetic resonance, MR) 图像分割对评估病情和治疗患者具有重要意义.
虽然深度卷积网络在医学图像分割中取得了良好表现, 但由于脑胶质瘤的恶性程度与外观表现有 …