  Adaptive radiotherapy (ART), especially online ART, effectively accounts for
positioning errors and anatomical changes. One key component of online ART is
accurately and efficiently delineating organs at risk (OARs) and targets on
online images, such as CBCT, to meet the online demands of plan evaluation and
adaptation. Deep learning (DL)-based automatic segmentation has gained great
success in segmenting planning CT, but its applications to CBCT yielded
inferior results due to the low image quality and limited available contour
labels for training. To overcome these obstacles to online CBCT segmentation,
we propose a registration-guided DL (RgDL) segmentation framework that
integrates image registration algorithms and DL segmentation models. The
registration algorithm generates initial contours, which were used as guidance
by DL model to obtain accurate final segmentations. We had two implementations
the proposed framework--Rig-RgDL (Rig for rigid body) and Def-RgDL (Def for
deformable)--with rigid body (RB) registration or deformable image registration
(DIR) as the registration algorithm respectively and U-Net as DL model
architecture. The two implementations of RgDL framework were trained and
evaluated on seven OARs in an institutional clinical Head and Neck (HN)
dataset. Compared to the baseline approaches using the registration or the DL
alone, RgDL achieved more accurate segmentation, as measured by higher mean
Dice similarity coefficients (DSC) and other distance-based metrics. Rig-RgDL
achieved a DSC of 84.5% on seven OARs on average, higher than RB or DL alone by
4.5% and 4.7%. The DSC of Def-RgDL is 86.5%, higher than DIR or DL alone by
2.4% and 6.7%. The inference time took by the DL model to generate final
segmentations of seven OARs is less than one second in RgDL. The resulting
segmentation accuracy and efficiency show the promise of applying RgDL
framework for online ART.
