  Many strides have been made in semantic segmentation of multiple classes
within an image. This has been largely due to advancements in deep learning and
convolutional neural networks (CNNs). Features within a CNN are automatically
learned during training, which allows for the abstraction of semantic
information within the images. These deep learning models are powerful enough
to handle the segmentation of multiple classes without the need for multiple
networks. Despite these advancements, few attempts have been made to
automatically segment multiple anatomical features within medical imaging
datasets obtained from CT or MRI scans. This offers a unique challenge because
of the three dimensional nature of medical imaging data. In order to alleviate
the 3D modality problem, we propose a multi-axis ensemble method, applied to a
dataset of 4-cardiac-chamber segmented CT scans. Inspired by the typical
three-axis view used by humans, this technique aims to maximize the 3D spatial
information afforded to the model, while remaining efficient for consumer grade
inference hardware. Multi-axis ensembling along with pragmatic voxel
preprocessing have shown in our experiments to greatly increase the mean
intersection over union of our predictions over the complete DICOM dataset.
