The Conditional Random Field as a Recurrent Neural Network layer is a recently proposed algorithm meant to be placed on top of an existing Fully-Convolutional Neural Network to improve the quality of semantic segmentation. In this paper, we test whether this algorithm, which was shown to improve semantic segmentation for 2D RGB images, is able to improve segmentation quality for 3D multi-modal medical images. We developed an implementation of the algorithm which works for any number of spatial dimensions, input/output image channels, and reference image channels. As far as we know this is the first publicly available implementation of this sort. We tested the algorithm with two distinct 3D medical imaging datasets, we concluded that the performance differences observed were not statistically significant. Finally, in the discussion section of the paper, we go into the reasons as to why this technique transfers poorly from natural images to medical images.
翻译:条件随机字段是一个经常性神经网络层,这是最近提出的一种算法,旨在置于现有的全进神经网络之上,以提高语义分离的质量。在本文中,我们测试这一算法是否能够改善3D多式医学图像的语义分离质量。我们开发了一个算法,该算法适用于任何空间维度、输入/输出图像频道和参考图像频道。据我们所知,这是首次可公开得到的这种类型的实施。我们用两套不同的3D医学成像数据集测试了算法,我们的结论是,观察到的性能差异在统计上没有多大意义。最后,在论文的讨论部分,我们探讨了为什么这种技术从自然图像向医学图像传播不力的原因。