One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs
This work addresses the need for precise segmentation in image-guided adaptive lung radiotherapy, which is crucial for effective cancer treatment, and it is incremental as it builds on existing registration-segmentation methods with patient-specific priors.
The paper tackled the problem of accurately segmenting tumors and organs in thoracic cone-beam CT (CBCT) images for lung radiotherapy, which is challenging due to low contrast and motion artifacts, and achieved significantly more accurate segmentation results, such as a DSC of 0.83 for tumors and 0.78 for the esophagus, compared to other methods.
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.