  Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate
auto-segmentation to reduce the time cost for physicians to edit contours.
However, deep learning (DL)-based direct segmentation of CBCT images is a
challenging task, mainly due to the poor image quality and lack of
well-labelled large training datasets. Deformable image registration (DIR) is
often used to propagate the manual contours on the planning CT (pCT) of the
same patient to CBCT. In this work, we undertake solving the problems mentioned
above with the assistance of DIR. Our method consists of three main components.
First, we use deformed pCT contours derived from multiple DIR methods between
pCT and CBCT as pseudo labels for initial training of the DL-based direct
segmentation model. Second, we use deformed pCT contours from another DIR
algorithm as influencer volumes to define the region of interest for DL-based
direct segmentation. Third, the initially trained DL model is further
fine-tuned using a smaller set of true labels. We found that DL-based direct
segmentation on CBCT trained with pseudo labels and without influencer volumes
shows poor performance compared to DIR-based segmentation. However, adding
deformed pCT contours as influencer volumes in the direct segmentation network
dramatically improves segmentation performance, reaching the accuracy level of
DIR-based segmentation. The DL model with influencer volumes can be further
improved through fine-tuning using a smaller set of true labels. Experiments
showed that 7 out of 19 structures have an at least 0.2 Dice similarity
coefficient increase compared to DIR-based segmentation. A DL-based direct CBCT
segmentation model can be improved to outperform DIR-based segmentation models
by using deformed pCT contours as pseudo labels and influencer volumes for
initial training, and by using a smaller set of true labels for model fine
tuning.

Medical Physics;Computer Vision and Pattern Recognition