论文标题
互动性深度改进网络用于医学图像细分
Interactive Deep Refinement Network for Medical Image Segmentation
论文作者
论文摘要
深度学习技术已成功地用于包括图像细分在内的许多计算机视觉任务。这些技术还应用于医学图像分割，这是计算机辅助诊断中最关键的任务之一。与自然图像相比，医学图像是具有低对比度的灰度图像（即使有一些看不见的零件）。由于某些器官与相邻器官具有相似的强度和纹理，因此通常需要完善自动分割结果。在本文中，我们提出了一个交互式的深度完善框架，以改善传统的语义分割网络，例如U-NET和完全卷积网络。在拟议的框架中，我们在传统的分割网络中添加了一个完善网络，以完善细分结果。实验性结果显示，公共数据集的实验结果表明，所提出的方法比其他最先进的方法可以实现更高的准确性。
Deep learning techniques have successfully been employed in numerous computer vision tasks including image segmentation. The techniques have also been applied to medical image segmentation, one of the most critical tasks in computer-aided diagnosis. Compared with natural images, the medical image is a gray-scale image with low-contrast (even with some invisible parts). Because some organs have similar intensity and texture with neighboring organs, there is usually a need to refine automatic segmentation results. In this paper, we propose an interactive deep refinement framework to improve the traditional semantic segmentation networks such as U-Net and fully convolutional network. In the proposed framework, we added a refinement network to traditional segmentation network to refine the segmentation results.Experimental results with public dataset revealed that the proposed method could achieve higher accuracy than other state-of-the-art methods.
