  Lack of large expert annotated MR datasets makes training deep learning
models difficult. Therefore, a cross-modality (MR-CT) deep learning
segmentation approach that augments training data using pseudo MR images
produced by transforming expert-segmented CT images was developed. Eighty-One
T2-weighted MRI scans from 28 patients with non-small cell lung cancers were
analyzed. Cross-modality prior encoding the transformation of CT to pseudo MR
images resembling T2w MRI was learned as a generative adversarial deep learning
model. This model augmented training data arising from 6 expert-segmented T2w
MR patient scans with 377 pseudo MRI from non-small cell lung cancer CT patient
scans with obtained from the Cancer Imaging Archive. A two-dimensional Unet
implemented with batch normalization was trained to segment the tumors from T2w
MRI. This method was benchmarked against (a) standard data augmentation and two
state-of-the art cross-modality pseudo MR-based augmentation and (b) two
segmentation networks. Segmentation accuracy was computed using Dice similarity
coefficient (DSC), Hausdroff distance metrics, and volume ratio. The proposed
approach produced the lowest statistical variability in the intensity
distribution between pseudo and T2w MR images measured as Kullback-Leibler
divergence of 0.069. This method produced the highest segmentation accuracy
with a DSC of 0.75 and the lowest Hausdroff distance on the test dataset. This
approach produced highly similar estimations of tumor growth as an expert (P =
0.37). A novel deep learning MR segmentation was developed that overcomes the
limitation of learning robust models from small datasets by leveraging learned
cross-modality priors to augment training. The results show the feasibility of
the approach and the corresponding improvement over the state-of-the-art
methods.
