An Automated Deep Learning-based Software for Total Lung Volume Calculation and Lung 3D Model Reconstruction
Main Article Content
Abstract
In this paper, we propose an automated deep learning-based software, especially for reconstructing 3D lung images and estimating Total Lung Volume (TLV). The system is designed mainly for Vietnamese users with Vietnamese as the default language. The purpose of our study is to build an automated system based on deep learning and machine-learning models to measure TLV and construct a 3D prototype of the lung. The training data is collected from The Cancer Imaging Archive (TCIA) dataset, provided by the 2017 Lung CT Segmentation Challenge for train and test the proposed model. Our proposed system utilizes a modified Bi-directional Convolutional (ConvLSTM) U-Net (BCDU-Net) neural network. We use the lung segmentation results as the data to build 3D lung image and calculate the lung volume. We test the reliability of the software with real dataset collected from Bach Mai hospital. The overall results have shown that accuracy against actual data from commercial products is approximately 99% when using our model to calculate the TLV.
Keywords
deep learning, lung CT image, total lung volume, 3D reconstruction
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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[2] American Cancer Society, 2020.
[3] Radiation risk from medical imaging, 31 September 2021.
[4] E. Sogancioglu, K. Murphy, E. Th.Scholten, L. H. Boulogne, M. Prokop and B. v. Ginneken, Automated estimation of total lung volume using chest radiographs and deep learning, Medical Physics, vol. 49, no. 7, pp. 4466-4477, 2022. https://doi.org/10.1002/mp.15655
[5] J. D. Flesch and C. J. Dine, Lung volumes: measurement, clinical use, and coding, Chest, vol. 142, no. 2, pp. 506-510, 2012. https://doi.org/10.1378/chest.11-2964
[6] H. Song, W. Wang, S. Zhao, J. Shen and K. Lam, Pyramid dilated deeper convlstm for video salient object detection, Proceedings of the European Conference on Computer Vision (ECCV), p. 715-731, 8-14 September 2018.
[7] C. Pedone, S. Scarlata, D. Chiurco, M. E. Conte, F. Forastiere and R. Antonelli-Incalzi, Association of reduced total lung capacity with mortality and use of health services, Chest, vol. 141, no. 4, pp. 1025-1030, 2012. https://doi.org/10.1378/chest.11-0899
[8] C. Tantucci, D. Bottone, A. Borghesi, M. Guerini, F. Quadri and L. Pini, Methods for Measuring Lung Volumes: Is There a Better One?, Respiration, vol. 91, no. 4, pp. 273-80, 2016. https://doi.org/10.1159/000444418
[9] S. F. Nemec, F. Molinari, V. Dufresne, N. Gosset, M. Silva and A. A. Bankier, Comparison of four software packages for CT lung volumetry in healthy individuals, European Radiology, vol. 25, pp. 1588-1597, 2015. https://doi.org/10.1007/s00330-014-3557-3
[10] J. Yang, H. Veeraraghavan, S. G. Armato III, K. Farahani, J. S. Kirby, J. Kalpathy-Kramer, W. van Elmpt, A. Dekker, X. Han, X. Feng, P. Aljabar, B. Oliveira, B. van der Heyden, L. Zamdborg, D. Lam, M. Gooding and G. C. Sharp, Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017, The International Journal of Medical Physics Research and Practice - Medical Physics, vol. 45, no. 10, pp. 4568-4581, 24 August 2018. https://doi.org/10.1002/mp.13141
[11] X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong and W.-c. Woo, Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, Advances in Neural Information Processing Systems, pp. 802-810, 2015. https://doi.org/10.48550/arXiv.1506.04214
[12] M. Zhou, M. Xiao, Y. Zhang, J. Gao and L. Gao, Marching cubes-based isogeometric topology optimization method with parametric level set, Applied Mathematical Modelling, vol. 107, pp. 275-295, 2022. https://doi.org/10.1016/j.apm.2022.02.032