  Segmentation of 3D micro-Computed Tomographic uCT) images of rock samples is
essential for further Digital Rock Physics (DRP) analysis, however,
conventional methods such as thresholding, watershed segmentation, and
converging active contours are susceptible to user-bias. Deep Convolutional
Neural Networks (CNNs) have produced accurate pixelwise semantic segmentation
results with natural images and $\mu$CT rock images, however, physical accuracy
is not well documented. The performance of 4 CNN architectures is tested for 2D
and 3D cases in 10 configurations. Manually segmented uCT images of Mt. Simon
Sandstone are treated as ground truth and used as training and validation data,
with a high voxelwise accuracy (over 99%) achieved. Downstream analysis is then
used to validate physical accuracy. The topology of each segmented phase is
calculated, and the absolute permeability and multiphase flow is modelled with
direct simulation in single and mixed wetting cases. These physical measures of
connectivity, and flow characteristics show high variance and uncertainty, with
models that achieve 95\%+ in voxelwise accuracy possessing permeabilities and
connectivities orders of magnitude off. A new network architecture is also
introduced as a hybrid fusion of U-net and ResNet, combining short and long
skip connections in a Network-in-Network configuration. The 3D implementation
outperforms all other tested models in voxelwise and physical accuracy
measures. The network architecture and the volume fraction in the dataset (and
associated weighting), are factors that not only influence the accuracy
trade-off in the voxelwise case, but is especially important in training a
physically accurate model for segmentation.
