  Despite the widespread availability of in-treatment room cone beam computed
tomography (CBCT) imaging, due to the lack of reliable segmentation methods,
CBCT is only used for gross set up corrections in lung radiotherapies. Accurate
and reliable auto-segmentation tools could potentiate volumetric response
assessment and geometry-guided adaptive radiation therapies. Therefore, we
developed a new deep learning CBCT lung tumor segmentation method. Methods: The
key idea of our approach called cross modality educed distillation (CMEDL) is
to use magnetic resonance imaging (MRI) to guide a CBCT segmentation network
training to extract more informative features during training. We accomplish
this by training an end-to-end network comprised of unpaired domain adaptation
(UDA) and cross-domain segmentation distillation networks (SDN) using unpaired
CBCT and MRI datasets. Feature distillation regularizes the student network to
extract CBCT features that match the statistical distribution of MRI features
extracted by the teacher network and obtain better differentiation of tumor
from background.} We also compared against an alternative framework that used
UDA with MR segmentation network, whereby segmentation was done on the
synthesized pseudo MRI representation. All networks were trained with 216
weekly CBCTs and 82 T2-weighted turbo spin echo MRI acquired from different
patient cohorts. Validation was done on 20 weekly CBCTs from patients not used
in training. Independent testing was done on 38 weekly CBCTs from patients not
used in training or validation. Segmentation accuracy was measured using
surface Dice similarity coefficient (SDSC) and Hausdroff distance at 95th
percentile (HD95) metrics.
