ArticleMedicine2021
Accurate segmentation for different types of lung nodules on CT images using improved U-Net convolutional network.
Article in Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Landscape of 2D Deep Learning Segmentation Networks Applied to CT Scan from Lung Cancer Patients: A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- HEE-SegGAN: A holistically-nested edge enhanced GAN for pulmonary nodule segmentation.PloS one · 2025Article
- Experimental Examination of Conventional, Semi-Automatic, and Automatic Volumetry Tools for Segmentation of Pulmonary Nodules in a Phantom Study.Diagnostics (Basel, Switzerland) · 2023Article
- MF-Net: Automated Muscle Fiber Segmentation From Immunofluorescence Images Using a Local-Global Feature Fusion Network.Journal of digital imaging · 2023Article
- Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis.Insights into imaging · 2023Review
- The predictive value of chest computed tomography images, tumor markers, and metabolomics in the identification of benign and malignant pulmonary nodules.Journal of thoracic disease · 2023Article
- 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
- An Effective Approach for Automated Lung Node Detection using CT Scans.Journal of biomedical physics & engineering · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
abstractSince lung nodules on computed tomography images can have different shapes, contours, textures or locations and may be attached to neighboring blood vessels or pleural surfaces, accurate segmentation is still challenging. In this study, we propose an accurate segmentation method based on an improved U-Net convolutional network for different types of lung nodules on computed tomography images.The first phase is to segment lung parenchyma and correct the lung contour by applying α-hull algorithm. The second phase is to extract image pairs of patches containing lung nodules in the center and the corresponding ground truth and build an improved U-Net network with introduction of batch normalization.A large number of experiments manifest that segmentation performance of Dice loss has superior results than mean square error and Binary_crossentropy loss. The α-hull algorithm and batch normalization can improve the segmentation performance effectively. Our best result for Dice similar coefficient (0.8623) is also more competitive than other state-of-the-art segmentation algorithms.In order to segment different types of lung nodules accurately, we propose an improved U-Net network, which can improve the segmentation accuracy effectively. Moreover, this work also has practical value in helping radiologists segment lung nodules and diagnose lung cancer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.