  Lung tumors, especially those located close to or surrounded by soft tissues
like the mediastinum, are difficult to segment due to the low soft tissue
contrast on computed tomography images. Magnetic resonance images contain
superior soft-tissue contrast information that can be leveraged if both
modalities were available for training. Therefore, we developed a
cross-modality educed learning approach where MR information that is educed
from CT is used to hallucinate MRI and improve CT segmentation. Our approach,
called cross-modality educed deep learning segmentation (CMEDL) combines CT and
pseudo MR produced from CT by aligning their features to obtain segmentation on
CT. Features computed in the last two layers of parallelly trained CT and MR
segmentation networks are aligned. We implemented this approach on U-net and
dense fully convolutional networks (dense-FCN). Our networks were trained on
unrelated cohorts from open-source the Cancer Imaging Archive CT images
(N=377), an internal archive T2-weighted MR (N=81), and evaluated using
separate validation (N=304) and testing (N=333) CT-delineated tumors. Our
approach using both networks were significantly more accurate (U-net $P
<0.001$; denseFCN $P <0.001$) than CT-only networks and achieved an accuracy
(Dice similarity coefficient) of 0.71$\pm$0.15 (U-net), 0.74$\pm$0.12
(denseFCN) on validation and 0.72$\pm$0.14 (U-net), 0.73$\pm$0.12 (denseFCN) on
the testing sets. Our novel approach demonstrated that educing cross-modality
information through learned priors enhances CT segmentation performance
