ArticleIEEE transactions on medical imaging2022
One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy.ArXiv · 2026Article
- Tumor-conditioned inter-patient registration using planning computed tomography for voxel-based analysis to predict radiation pneumonitis in lung cancer patients.Physics and imaging in radiation oncology · 2026Article
- Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer.Medical physics · 2025Article
- Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer.Radiation oncology (London, England) · 2024Article
- Accurate object localization facilitates automatic esophagus segmentation in deep learning.Radiation oncology (London, England) · 2024Article
- Artificial intelligence-based automated segmentation and radiotherapy dose mapping for thoracic normal tissues.Physics and imaging in radiation oncology · 2024Article
- SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023Article
- Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT.Medical physics · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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 ± 0.08, surface DSC [sDSC] of 0.97 ± 0.06, and Hausdorff distance at 95th percentile [HD95] of 3.97±3.02mm) and the esophagus (DSC of 0.78 ± 0.13, sDSC of 0.90±0.14, HD95 of 3.22±2.02) segmentation than multiple methods. Ablation tests and comparative experiments were also done.
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Registered trials
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