论文标题
使用多个图像量表通过级联的深神经网络进行脑瘤分割
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales
论文作者
论文摘要
颅内肿瘤是通常无法控制的细胞组。四分之一的癌症死亡是由于脑肿瘤。脑肿瘤的早期检测和评估是通过磁共振成像（MRI）执行的基本预防性医学步骤。为此，存在许多分割技术。低分割精度是现有方法的主要缺点。在本文中，我们使用一种深度学习方法来提高MR图像中肿瘤分割的准确性。级联方法与多个图像尺度一起使用，以诱导本地和全球视图，并帮助网络达到更高的精度。我们的实验结果表明，使用多个量表和两个级联网络的利用是有利的。
Intracranial tumors are groups of cells that usually grow uncontrollably. One out of four cancer deaths is due to brain tumors. Early detection and evaluation of brain tumors is an essential preventive medical step that is performed by magnetic resonance imaging (MRI). Many segmentation techniques exist for this purpose. Low segmentation accuracy is the main drawback of existing methods. In this paper, we use a deep learning method to boost the accuracy of tumor segmentation in MR images. Cascade approach is used with multiple scales of images to induce both local and global views and help the network to reach higher accuracies. Our experimental results show that using multiple scales and the utilization of two cascade networks is advantageous.
