ArticleJournal of healthcare engineering2021
A Pulmonary Vascular Extraction Algorithm from Chest CT/CTA Images.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- A generative adversarial network model for improved three-dimensional mapping of pulmonary arteries and veins from non-contrast computed tomography in sublobar resection planning.Quantitative imaging in medicine and surgery · 2026Article
- A self-training framework for semi-supervised pulmonary vessel segmentation and its application in COPD.Journal of X-ray science and technology · 2026Article
- Pediatric three-dimensional quantitative cardiovascular computed tomography.Pediatric radiology · 2025Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Segmentation of pulmonary vessels in CT/CTA images can help physicians better determine the patient's condition and treatment. However, due to the complexity of CT images, existing methods have limitations in the segmentation of pulmonary vessels. In this paper, a method based on the separation of pulmonary vessels in CT/CTA images is investigated. The method is divided into two steps: in the first step, the lung parenchyma is extracted using the Unet++ algorithm, which can effectively reduce the oversegmentation rate; in the second step, the pulmonary vessels in the lung parenchyma are extracted using nnUnet. According to the obtained lung parenchyma segmentation results, the "AND" operation is performed on the original image and the lung parenchyma segmentation results, and only the blood vessels within the lung parenchyma are segmented, which reduces the interference of external tissues and improves the segmentation accuracy. The experimental data source used CT/CTA images acquired from the partner hospital. After the experiments were performed on a total of 67 sets of images, the accuracy of CT and CTA images reached 85.1% and 87.7%, respectively. The comparison of whether to segment the lung parenchyma and with other conventional methods was also performed, and the experimental results showed that the algorithm in this paper has high accuracy.
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