Abstract
In recent years, deep learning techniques have been widely applied in the field of medical image processing, particularly in the segmentation of pulmonary nodules, which has garnered increasing attention from researchers. The segmentation of pulmonary computed tomography (CT) images is fundamental to the three-dimensional reconstruction of medical images, and the accuracy of image segmentation directly impacts the practical application value of three-dimensional reconstruction in the medical field. However, within the entire CT image of the lungs, pulmonary nodules only occupy a small region, and the complex nature of nodule manifestations and significant size variations present a major challenge for accurate segmentation in modern medicine. To address the issues of precise segmentation of pulmonary nodule CT images and the occurrence of voids in three-dimensional reconstruction, this study focuses on pulmonary CT images and achieves both accurate pulmonary nodule segmentation and three-dimensional reconstruction.
Supported by organizations of the National Natural Science Foundation of China, the Natural Science Foundation of Jiangxi Provincial, and the Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province.
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References
Siegel, R.L., Miller, K.D., Wagle, N.S., Jemal, A.: Cancer statistics, 2023. CA Cancer J. Clin. 73(1), 17–48 (2023)
Caviezel, C., Kostopanagiotou, K., Puippe, G.D., Werner, R.S., Opitz, I.: ‘One-stop-shop’ diagnosis and stage-adapted surgical therapy for small nodules of early stage lung cancer in a hybrid operating room. Br. J. Surg. 109(Suppl. 3), znac185.003 (2022)
Davies, B., Ghosh, S., Hopkinson D., Vaughan, R., Wing, C.: Institutional report - thoracic general solitary pulmonary nodules: pathological outcome of 150 consecutively resected lesions (2022)
Sathya Udayakumar, A., Keerthi Praveen, B.: Advancements in industrial wastewater treatment by integrated membrane technologies. In: Integrated Environmental Technologies for Wastewater Treatment and Sustainable Development, pp. 369–382 (2022)
Hazewinkel, A.D., Bowden, J., Wade, K.H., Palmer, T., Wiles, N.J., Tilling, K.: Sensitivity to missing not at random dropout in clinical trials: use and interpretation of the trimmed means estimator. Stat. Med. 41(8), 1462–1481 (2022)
Mazzone, P.J., Lam, L.: Evaluating the patient with a pulmonary nodule: a review. JAMA 327(3), 264–273 (2022)
Sunil, R., Joies, K.M., Cherungottil, A., Bharath, T.U., Renjith, S.: BitMedi: an application to store medical records efficiently and securely. In: Suma, V., Fernando, X., Du, K.-L., Wang, H. (eds.) Evolutionary Computing and Mobile Sustainable Networks. LNDECT, vol. 116, pp. 93–105. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-9605-3_7
Ale, G.B., Simpson, R., Mertens, C., Santillana, E., Hill, B., Carlo, W., Batra, H.: Shape-sensing robotic bronchoscopy in the diagnosis of pulmonary lesions in children. Authorea Preprints (2022)
Yu, L., Wen-Bin, L., Yun-Feng, K., Jian-Feng, D., Meng-Han, W., Yong-Xiang, C.: Identification method of illegal electronic devices based on deep learning and application analysis. Communications Technology (2019)
Liu, F., Que, D.: Realization of solid plane cutting system and reconstruction of medical 3D image based on MC algorithm. In: 2nd International Conference on Computer Engineering, Information Science & Application Technology, ICCIA 2017 (2017)
Hu, S., Liao, Z., Xia, Y.: Domain specific convolution and high frequency reconstruction based unsupervised domain adaptation for medical image segmentation (2022)
Xiao, Z., Liu, B., Geng, L., Zhang, F., Liu, Y.: Segmentation of lung nodules using improved 3D-UNet neural network. Symmetry 12(11), 1787 (2020)
Cai, L., Long, T., Dai, Y., Huang, Y.: Mask R-CNN-based detection and segmentation for pulmonary nodule 3D visualization diagnosis. IEEE Access 8, 44400–44409 (2020)
Du, G., Cao, X., Liang, J., Chen, X., Zhan, Y.: Medical image segmentation based on U-Net: a review. J. Imaging Sci. Technol. (2020)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 92159102, the Natural Science Foundation of Jiangxi Provincial under Grant 20232ACB205001 and 20212BAB204005, and the Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province under Grant 20232BCJ22025.
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Xu, C., Hua, S., Zhong, M. (2024). Deep Learning-Based Lung Nodule Segmentation and 3D Reconstruction Algorithm for CT Images. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14510. Springer, Singapore. https://doi.org/10.1007/978-981-99-9788-6_17
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DOI: https://doi.org/10.1007/978-981-99-9788-6_17
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