ArticleSN computer science2023
Vision Transformers for Lung Segmentation on CXR Images.
Article in SN computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 30 citations in OpenAlex.
- Fine-Tuned Segment Anything Model with Low-Rank Adaptation for Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 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
- CoviSwin: A Deep Vision Transformer for Automatic Segmentation of COVID-19 CT Scans.Bioengineering (Basel, Switzerland) · 2025Article
- Three-dimensional reconstruction of lung tumors from computed tomography scans using adversarial and transductive learning.Scientific reports · 2025Article
- Trans RCED-UNet3+: a hybrid CNN-transformer model for precise lung nodule segmentation.Frontiers in oncology · 2025Article
- Segmentation-based cardiomegaly detection based on semi-supervised estimation of cardiothoracic ratio.Scientific reports · 2024Article
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2 authors at 1 institution in 1 country.
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Abstract
Accurate segmentation of the lungs in CXR images is the basis for an automated CXR image analysis system. It helps radiologists in detecting lung areas, subtle signs of disease and improving the diagnosis process for patients. However, precise semantic segmentation of lungs is considered a challenging case due to the presence of the edge rib cage, wide variation of lung shape, and lungs affected by diseases. In this paper, we address the problem of lung segmentation in healthy and unhealthy CXR images. Five models were developed and used in detecting and segmenting lung regions. Two loss functions and three benchmark datasets were employed to evaluate these models. Experimental results showed that the proposed models were able to extract salient global and local features from the input CXR images. The best performing model achieved an F1 score of 97.47%, outperforming recent published models. They proved their ability to separate lung regions from the rib cage and clavicle edges and segment varying lung shape depending on age and gender, as well as challenging cases of lungs affected by anomalies such as tuberculosis and the presence of nodules.
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