One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs

Abstract

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.

arXiv commentThis manuscript is currently under minor revision at IEEE Transactions on Medical Imaging

References in corpus (12)

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• VoxelMorph: A Learning Framework for Deformable Medical Image Registration
• A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
• Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
• Recursive Cascaded Networks for Unsupervised Medical Image Registration
• Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
• A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data
• PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation
• Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting
• Recurrent Registration Neural Networks for Deformable Image Registration
• Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation
• A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy

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