ArticleMedical image analysis2022
PLOSL: Population learning followed by one shot learning pulmonary image registration using tissue volume preserving and vesselness constraints.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01969344 (Subpopulations and Intermediate Outcome Measures in COPD Study), which is not on this map. Cited by 7 papers.
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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.
Subpopulations and Intermediate Outcome Measures in COPD Study
Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- W-STFNet: A Wavelet Transform-Based Regularized Hybrid Recursive Spatiotemporal Fusion Registration Network.Annals of biomedical engineering · 2026Article
- CARL: A Framework for Equivariant Image Registration.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.Nature communications · 2025Article
- MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration.Medical image analysis · 2025Article
- GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2023Article
- Inverse Consistency by Construction for Multistep Deep Registration.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Robust Measures of Image-Registration-Derived Lung Biomechanics in SPIROMICS.Journal of imaging · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
This paper presents the Population Learning followed by One Shot Learning (PLOSL) pulmonary image registration method. PLOSL is a fast unsupervised learning-based framework for 3D-CT pulmonary image registration algorithm based on combining population learning (PL) and one-shot learning (OSL). The PLOSL image registration has the advantages of the PL and OSL approaches while reducing their respective drawbacks. The advantages of PLOSL include improved performance over PL, substantially reducing OSL training time and reducing the likelihood of OSL getting stuck in local minima. PLOSL pulmonary image registration uses tissue volume preserving and vesselness constraints for registration of inspiration-to-expiration and expiration-to-inspiration pulmonary CT images. A coarse-to-fine convolution encoder-decoder CNN architecture is used to register large and small shape features. During training, the sum of squared tissue volume difference (SSTVD) compensates for intensity differences between inspiration and expiration computed tomography (CT) images and the sum of squared vesselness measure difference (SSVMD) helps match the lung vessel tree. Results show that the PLOSL (SSTVD+SSVMD) algorithm achieved subvoxel landmark error while preserving pulmonary topology on the SPIROMICS data set, the public DIR-LAB COPDGene and 4DCT data sets.
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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.