Pulmonary dynamics of anatomical structures of interest in 4DCT imaging
Vol 3, Issue 1, 2020
VIEWS - 4460 (Abstract) 1394 (PDF)
Abstract
The present work shows an application of the Chan-Vese algorithm for the semi-automatic segmentation of anatomical structures of interest (lungs and lung tumor) in 4DCT images of the thorax, as well as their three-dimensional reconstruction. The segmentation and reconstruction were performed on 10 CT images, which make up an inspiration-expiration cycle. The maximum displacement was calculated for the case of the lung tumor using the reconstructions of the onset of inspiration, the onset of expiration, and the voxel information. The proposed method achieves appropriate segmentation of the studied structures regardless of their size and shape. The three-dimensional reconstruction allows us to visualize the dynamics of the structures of interest throughout the respiratory cycle. In the future, it is expected to have more evidence of the good performance of the proposed method and to have the feedback of the clinical expert, since the knowledge of the characteristics of anatomical structures, such as their dimension and spatial position, helps in the planning of Radiotherapy (RT) treatments, optimizing the radiation dose to cancer cells and minimizing it in healthy organs. Therefore, the information found in this work may be of interest for the planning of RT treatments.
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1. Aubert G, Kornprobst P. Mathematical problems in image processing: Partial differential equations and the calculus of variations. Springer Science and Business Media 2006; 147.
2. doi: 10.1007/978-0-387-44588-5.
3. Faggiano E, Fiorino C, Scalco E, et al. An automatic contour propagation method to follow parotid glands deformation during head-and-neck cancer Tomotherapy. Physics in Medicine and Biology 2011; 56(3): 775–791. doi: 10.1088/0031-9155/56/3/015.
4. Fox J, Ford E, Redmond K, et al. Quantification of tumor volume changes during radiotherapy for non-small-cell lung cancer. International Journal of Radiation Oncology Biology Physics 2009; 74(2): 341–348. doi: 10.1016/j.ijrobp.2008.07.063.
5. Mageras GS, Mechalakos J. Planning in the IGRT context: Closing the loop. Seminars in Radiation Oncology 2007; 17: 268–277. doi: 10.1016/j.semradonc.2007.06.002.
6. Pal NR, Pal SK. A review on image segmentation techniques. Pattern Recognition 1993; 26(9): 1277–1294. doi: 10.1016/0031-3203(93)90135-J.
7. Chandhok C, Chaturvedi S, Khurshid AA. An approach to image segmentation using K-means clustering algorithm. International Journal of Information Technology 2012; 1: 11–17.
8. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 1979l; 9(1): 62–66. doi: 10.1109/TSMC.1979.4310076.
9. Hernandez S, Mejia AR, Arce ER, et al. Evaluación cuantitativa del desempeño de métodos de segmentación aplicados a imágenes médicas para el análisis de estructuras anatómicas de interés (Spanish) [Quantitative evaluation of the performance of segmentation methods applied to medical images for the analysis of anatomical structures of interest]. Memorias Congreso Nacional de Ingeniería Biomédica 2015; 2: 374–377.
10. Vandemeulebroucke J, Rit S, Kybic J, et al. Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Medical Physics 2011; 38(1): 166–178. doi: 10.1118/1.3523619.
11. Jianyuan D, Chongyang H. 3D fast level set image segmentation based on Chan Vese model. 3rd International Conference on Bioinformatics and Biomedical Engineering; 2009. p. 11–13. doi: 10.1109/ICBBE.2009.5162136.
12. Liu L, Zeng L, Luan X. 3D robust Chan-Vese model for industrial computed tomography volume data segmentation. Optics and Lasers in Engineering 2013; 51(11): 1235–1244. doi: 10.1016/j.optlaseng.2013.04.019.
13. He F, Sun Y. Segmentation of noisy CT volume data using improved 3D Chan-Vese model. 2015 IEEE 7th International Conference on Awareness Science and Technology (iCAST); 2015. p. 31–36. doi: 10.1109/ICAwST.2015.7314016.
14. Chan TF, Vese LA. Active contours without edges. IEEE Transactions on Image Processing 2001; 10(2): 266–277. doi: 10.1109/83.902291.
15. Faggiano E, Cattaneo GM, Ciavarro C, et al. Validation of an elastic registration technique to estimate anatomical lung modification in non-small-cell lung cancer tomotherapy. Radiation Oncology 2011; 6(1). doi: 10.1186/1748-717X-6-31.
16. McAuliffe MJ, Lalonde FM, McGarry D, et al. Medical image processing, analysis and visualization in clinical research. Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems. Washington: IEEE Computer Society. 2001. p. 381. doi: 10.1109/CBMS.2001.941749.
17. Mejia AR. Deformable image registration for radiotherapy monomodal applications [PhD thesis]. Milan: Politecnico di Milano; 2013. Available from: https://www.politesi.polimi.it/handle/10589/82803.
DOI: https://doi.org/10.24294/irr.v3i1.1723
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