ArticleMedical image analysis2021
Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- A feasibility study on multimodal CT-MRI registration using segmentation aid and CoLlAGe feature extraction approach.Abdominal radiology (New York) · 2026Article
- Assessing the suitability of automated registration and segmentation for dosimetry calculations in SIRT treatment planning.EJNMMI physics · 2026Article
- Imaging-Based Artificial Intelligence in Vascular and Interventional Radiology: A Narrative Review.Journal of imaging informatics in medicine · 2026Review
- PhysMorph: A biomechanical and image-guided deep learning framework for real-time multi-modal liver image registration.Physics and imaging in radiation oncology · 2026Article
- MICAFlow: Fast and robust MRI preprocessing bridging research neuroimaging and clinical practice.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Finite Element Method-Based Hybrid MRI/CBCT Generation to Improve Liver Stereotactic Body Radiation Therapy Targets Localization Accuracy.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Unsupervised deep learning registration model for multimodal brain images.Journal of applied clinical medical physics · 2023Article
- Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT.Medical physics · 2023Article
- A Paradigm Shift in Primary Liver Cancer Therapy Utilizing Genomics, Molecular Biomarkers, and Artificial Intelligence.Cancers · 2023Review
- The Liver Tumor Segmentation Benchmark (LiTS).Medical image analysis · 2023Article
- Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx.Quantitative imaging in medicine and surgery · 2023Article
- ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction.JHEP reports : innovation in hepatology · 2022Review
- One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs.IEEE transactions on medical imaging · 2022Article
- DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography.Medical image analysis · 2022Article
- Review of Generative Adversarial Networks in mono- and cross-modal biomedical image registration.Frontiers in neuroinformatics · 2022Review
- MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET.IEEE transactions on medical imaging · 2021Article
- Harmonized neonatal brain MR image segmentation model for cross-site datasets.Biomedical signal processing and control · 2021Article
- Co-optimization Learning Network for MRI Segmentation of Ischemic Penumbra Tissues.Frontiers in neuroinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Multimodal image registration has many applications in diagnostic medical imaging and image-guided interventions, such as Transcatheter Arterial Chemoembolization (TACE) of liver cancer guided by intraprocedural CBCT and pre-operative MR. The ability to register peri-procedurally acquired diagnostic images into the intraprocedural environment can potentially improve the intra-procedural tumor targeting, which will significantly improve therapeutic outcomes. However, the intra-procedural CBCT often suffers from suboptimal image quality due to lack of signal calibration for Hounsfield unit, limited FOV, and motion/metal artifacts. These non-ideal conditions make standard intensity-based multimodal registration methods infeasible to generate correct transformation across modalities. While registration based on anatomic structures, such as segmentation or landmarks, provides an efficient alternative, such anatomic structure information is not always available. One can train a deep learning-based anatomy extractor, but it requires large-scale manual annotations on specific modalities, which are often extremely time-consuming to obtain and require expert radiological readers. To tackle these issues, we leverage annotated datasets already existing in a source modality and propose an anatomy-preserving domain adaptation to segmentation network (APA2Seg-Net) for learning segmentation without target modality ground truth. The segmenters are then integrated into our anatomy-guided multimodal registration based on the robust point matching machine. Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.