  Accurate and robust segmentation of lung cancers from CT, even those located
close to mediastinum, is needed to more accurately plan and deliver
radiotherapy and to measure treatment response. Therefore, we developed a new
cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI
scans, whereby an informative teacher MRI network guides a student CT network
to extract features that signal the difference between foreground and
background. Our contribution eliminates two requirements of distillation
methods: (i) paired image sets by using an image to image (I2I) translation and
(ii) pre-training of the teacher network with a large training set by using
concurrent training of all networks. Our framework uses an end-to-end trained
unpaired I2I translation, teacher, and student segmentation networks.
Architectural flexibility of our framework is demonstrated using 3 segmentation
and 2 I2I networks. Networks were trained with 377 CT and 82 T2w MRI from
different sets of patients, with independent validation (N=209 tumors) and
testing (N=609 tumors) datasets. Network design, methods to combine MRI with CT
information, distillation learning under informative (MRI to CT), weak (CT to
MRI) and equal teacher (MRI to MRI), and ablation tests were performed.
Accuracy was measured using Dice similarity (DSC), surface Dice (sDSC), and
Hausdorff distance at the 95$^{th}$ percentile (HD95). The CMEDL approach was
significantly (p $<$ 0.001) more accurate (DSC of 0.77 vs. 0.73) than non-CMEDL
methods with an informative teacher for CT lung tumor, with a weak teacher (DSC
of 0.84 vs. 0.81) for MRI lung tumor, and with equal teacher (DSC of 0.90 vs.
0.88) for MRI multi-organ segmentation. CMEDL also reduced inter-rater lung
tumor segmentation variabilities..
