ArticleFrontiers in artificial intelligence2025
Three-stage registration pipeline for dynamic lung field of chest X-ray images based on convolutional neural networks.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Dual-level weighted cross-entropy loss function and multi-object region segmentation network evaluation for dynamic knee joint X-ray radiography based on a novel scoring criterion.Frontiers in medicine · 2026Article
- Pulmonary Biomechanics in COPD: Imaging Techniques and Clinical Applications.Journal of biomechanical engineering · 2025Review
- An optimization method for hemi-diaphragm measurement of dynamic chest X-ray radiography during respiration based on graphics and diaphragm motion consistency criterion.Frontiers in physiology · 2025Article
- BLoss-DDNet: bending loss and dual-task decoding network for overlapping cell nucleus segmentation of cervical clinical LBC images.Frontiers in artificial intelligence · 2025Article
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11 authors.
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Abstract
Background: The anatomically constrained registration network (AC-RegNet), which yields anatomically plausible results, has emerged as the state-of-the-art registration architecture for chest X-ray (CXR) images. Nevertheless, accurate lung field registration results may be more favored and exciting than the registration results of the entire CXR images and hold promise for dynamic lung field analysis in clinical practice. Objective: Based on the above, a registration model of the dynamic lung field of CXR images based on AC-RegNet and static CXR images is urgently developed to register these dynamic lung fields for clinical quantitative analysis. Methods: This paper proposes a fully automatic three-stage registration pipeline for the dynamic lung field of CXR images. First, the dynamic lung field mask images are generated from a pre-trained standard lung field segmentation model with the dynamic CXR images. Then, a lung field abstraction model is designed to generate the dynamic lung field images based on the dynamic lung field mask images and their corresponding CXR images. Finally, we propose a three-step registration training method to train the AC-RegNet, obtaining the registration network of the dynamic lung field images (AC-RegNet_V3). Results: The proposed AC-RegNet_V3 with the four basic segmentation networks achieve the mean dice similarity coefficient (DSC) of 0.991, 0.993, 0.993, and 0.993, mean Hausdorff distance (HD) of 12.512, 12.813, 12.449, and 13.661, mean average symmetric surface distance (ASSD) of 0.654, 0.550, 0.572, and 0.564, and mean squared distance (MSD) of 559.098, 577.797, 548.189, and 559.652, respectively. Besides, compared to the dynamic CXR images, the mean DSC of these four basic segmentation networks with AC-RegNet has been significantly improved by 7.2, 7.4, 7.4, and 7.4% ( Conclusion: Our proposed three-stage registration pipeline has demonstrated its effectiveness in dynamic lung field registration. Therefore, it could become a powerful tool for dynamic lung field analysis in clinical practice, such as pulmonary airflow detection and air trapping location.
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