ArticleScientific reports2022
Automatic lung segmentation in chest X-ray images using improved U-Net.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 115 citations in OpenAlex.
- AI applications in lumbar and lumbosacral pedicle screw placement: a systematic review of limited evidence and future directions.Neurosurgical review · 2026Pooled it
- Lung Disease Classification with Deep Learning Enhanced CNN Architecture in Chest X-Ray Imaging.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Fine-Tuned Segment Anything Model with Low-Rank Adaptation for Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing generative networks for chest anomaly localization through automatic registration-based unpaired-to-pseudo-paired training data translation.Scientific reports · 2026Article
- Deep Learning on Misaligned Dual-Energy Chest X-ray Images Using Paired Cycle-Consistent Generative Adversarial Networks.Journal of imaging informatics in medicine · 2026Article
- Comparison of Deep Learning Architectures for Cardiac Contour Segmentation in Catheterization Radiographs.Cureus · 2026Article
- A generative model uses healthy and diseased image pairs for pixel-level chest X-ray pathology localization.Nature biomedical engineering · 2026Article
- DANEELpath open source digital analysis tools for histopathological research in neuroblastoma models.Scientific reports · 2026Article
- Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using Edge-Assisted Multiscale Feature Fusion.International journal of biomedical imaging · 2026Article
- Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach.Sensors (Basel, Switzerland) · 2025Article
- A Novel Deep Learning Framework for Liver Fibrosis Staging and Etiology Diagnosis Using Integrated Liver-Spleen Elastography.Diagnostics (Basel, Switzerland) · 2025Article
- Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module.Journal of imaging · 2025Article
- Segmentation of lungs from chest X-ray images based on Deep Atrous Attention UNet (DAA-UNet) model.Medical & biological engineering & computing · 2025Article
- Segmentation-Assisted Fusion-Based Classification for Automated CXR Image Analysis.Sensors (Basel, Switzerland) · 2025Article
- Computer vision to predict cell seeding coverage in re-endothelialized mouse lungs.Scientific reports · 2025Article
- Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology.Scientific reports · 2025Article
- Lung Segmentation with Lightweight Convolutional Attention Residual U-Net.Diagnostics (Basel, Switzerland) · 2025Article
- Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging.Physical and engineering sciences in medicine · 2025Article
- CXR-Seg: A Novel Deep Learning Network for Lung Segmentation from Chest X-Ray Images.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The automatic segmentation of the lung region for chest X-ray (CXR) can help doctors diagnose many lung diseases. However, extreme lung shape changes and fuzzy lung regions caused by serious lung diseases may incorrectly make the automatic lung segmentation model. We improved the U-Net network by using the pre-training Efficientnet-b4 as the encoder and the Residual block and the LeakyReLU activation function in the decoder. The network can extract Lung field features efficiently and avoid the gradient instability caused by the multiplication effect in gradient backpropagation. Compared with the traditional U-Net model, our method improves about 2.5% dice coefficient and 6% Jaccard Index for the two benchmark lung segmentation datasets. Our model improves about 5% dice coefficient and 9% Jaccard Index for the private lung segmentation datasets compared with the traditional U-Net model. Comparative experiments show that our method can improve the accuracy of lung segmentation of CXR images and it has a lower standard deviation and good robustness.
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