ArticleSensors (Basel, Switzerland)2021
Lung Nodule Segmentation with a Region-Based Fast Marching Method.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Development and Radiological Evaluation of 3D-Printed Patient-Specific Lung Phantoms: From CT Imaging to 3D Modeling and Material Characterization.Research square · 2026Article
- Automated Lesion Segmentation in Medical Imaging via Integration of nnU-Net Optimization and SAM Approach.Biomedical engineering and computational biology · 2026Article
- A lung nodule segmentation model based on the transformer with multiple thresholds and coordinate attention.Scientific reports · 2024Article
- Statistical Analysis of nnU-Net Models for Lung Nodule Segmentation.Journal of personalized medicine · 2024Article
- Artificial intelligence in lung cancer: Application and future thinking.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2022Article
- Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges.Journal of personalized medicine · 2022Review
- Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.Computational intelligence and neuroscience · 2022Article
- Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques.BioMed research international · 2022Article
- Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net.Computational and mathematical methods in medicine · 2022Article
- Efficacy Evaluation of Ultrasound with Active Contour Model for Hemodialysis in Children with Renal Failure.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
When dealing with computed tomography volume data, the accurate segmentation of lung nodules is of great importance to lung cancer analysis and diagnosis, being a vital part of computer-aided diagnosis systems. However, due to the variety of lung nodules and the similarity of visual characteristics for nodules and their surroundings, robust segmentation of nodules becomes a challenging problem. A segmentation algorithm based on the fast marching method is proposed that separates the image into regions with similar features, which are then merged by combining regions growing with k-means. An evaluation was performed with two distinct methods (objective and subjective) that were applied on two different datasets, containing simulation data generated for this study and real patient data, respectively. The objective experimental results show that the proposed technique can accurately segment nodules, especially in solid cases, given the mean Dice scores of 0.933 and 0.901 for round and irregular nodules. For non-solid and cavitary nodules the performance dropped-0.799 and 0.614 mean Dice scores, respectively. The proposed method was compared to active contour models and to two modern deep learning networks. It reached better overall accuracy than active contour models, having comparable results to DBResNet but lesser accuracy than 3D-UNet. The results show promise for the proposed method in computer-aided diagnosis applications.
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