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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 &#x003C; 0.001) for tumor (DSC of 0.83 &#x00B1; 0.08, surface DSC [sDSC] of 0.97 &#x00B1; 0.06, and Hausdorff distance at 95<sup><italic>th</italic></sup> percentile [HD95] of 3.97&#x00B1;3.02mm) and the esophagus (DSC of 0.78 &#x00B1; 0.13, sDSC of 0.90&#x00B1;0.14, HD95 of 3.22&#x00B1;2.02) segmentation than multiple methods. Ablation tests and comparative experiments were also done.

ieee transactions on medical imaging

One Shot PACS: <underline>P</underline>atient Specific <underline>A</underline>natomic <underline>C</underline>ontext and <underline>S</underline>hape Prior Aware Recurrent Registration-Segmentation of Longitudinal Thoracic Cone Beam CTs

One Shot PACS: Patient Specific Anatomic Context and Shape Prior Aware Recurrent Registration-Segmentation of Longitudinal Thoracic Cone Beam CTs