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Lung tumors, especially those located close to or surrounded by soft tissueslike the mediastinum, are difficult to segment due to the low soft tissuecontrast on computed tomography images. Magnetic resonance images containsuperior soft-tissue contrast information that can be leveraged if bothmodalities were available for training. Therefore, we developed across-modality educed learning approach where MR information that is educedfrom CT is used to hallucinate MRI and improve CT segmentation. Our approach,called cross-modality educed deep learning segmentation (CMEDL) combines CT andpseudo MR produced from CT by aligning their features to obtain segmentation onCT. Features computed in the last two layers of parallelly trained CT and MRsegmentation networks are aligned. We implemented this approach on U-net anddense fully convolutional networks (dense-FCN). Our networks were trained onunrelated cohorts from open-source the Cancer Imaging Archive CT images(N=377), an internal archive T2-weighted MR (N=81), and evaluated usingseparate validation (N=304) and testing (N=333) CT-delineated tumors. Ourapproach 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) onthe testing sets. Our novel approach demonstrated that educing cross-modalityinformation through learned priors enhances CT segmentation performance

Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation