Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
This work addresses segmentation quality for 3D medical imaging, but it is incremental as it applies an existing method to new data without achieving gains.
The paper tested whether a Conditional Random Field as a Recurrent Neural Network layer, previously effective for 2D RGB image segmentation, improves segmentation for 3D multi-modal medical images, finding no statistically significant performance differences in two datasets.
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.