  Dendritic microstructures are ubiquitous in nature and are the primary
solidification morphologies in metallic materials. Techniques such as x-ray
computed tomography (XCT) have provided new insights into dendritic phase
transformation phenomena. However, manual identification of dendritic
morphologies in microscopy data can be both labor intensive and potentially
ambiguous. The analysis of 3D datasets is particularly challenging due to their
large sizes (terabytes) and the presence of artifacts scattered within the
imaged volumes. In this study, we trained 3D convolutional neural networks
(CNNs) to segment 3D datasets. Three CNN architectures were investigated,
including a new 3D version of FCDense. We show that using hyperparameter
optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures
can be trained to outperform the previous state of the art. The 3D U-Net
architecture trained in this study produced the best segmentations according to
quantitative metrics (pixel-wise accuracy of 99.84% and a boundary displacement
error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and
best segmentations according to visual inspection. The trained 3D CNNs are able
to segment entire 852 x 852 x 250 voxel 3D volumes in only ~60 seconds, thus
hastening the progress towards a deeper understanding of phase transformation
phenomena such as dendritic solidification.
