  Image-guided adaptive lung radiotherapy requires accurate tumor and organs
segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs
are hard to segment because of low soft-tissue contrast, imaging artifacts,
respiratory motion, and large treatment induced intra-thoracic anatomic
changes. Hence, we developed a novel Patient-specific Anatomic Context and
Shape prior or PACS-aware 3D recurrent registration-segmentation network for
longitudinal thoracic CBCT segmentation. Segmentation and registration networks
were concurrently trained in an end-to-end framework and implemented with
convolutional long-short term memory models. The registration network was
trained in an unsupervised manner using pairs of planning CT (pCT) and CBCT
images and produced a progressively deformed sequence of images. The
segmentation network was optimized in a one-shot setting by combining
progressively deformed pCT (anatomic context) and pCT delineations (shape
context) with CBCT images. Our method, one-shot PACS was significantly more
accurate (p$<$0.001) for tumor (DSC of 0.83 $\pm$ 0.08, surface DSC [sDSC] of
0.97 $\pm$ 0.06, and Hausdorff distance at $95^{th}$ percentile [HD95] of
3.97$\pm$3.02mm) and the esophagus (DSC of 0.78 $\pm$ 0.13, sDSC of
0.90$\pm$0.14, HD95 of 3.22$\pm$2.02) segmentation than multiple methods.
Ablation tests and comparative experiments were also done.
