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Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours
1. P. M. Harari, S. Song, and W. A. Tome, “Emphasizing conformal avoidance versus target definition for imrt planning in head-and-neck cancer,” Int. J. Radiat. Oncol., Biol., Phys. 77(3), 950–958 (2010).
2. C. L. Brouwer, R. J. H. M. Steenbakkers, E. van den Heuvel, J. C. Duppen, A. Navran, H. P. Bijl, O. Chouvalova, F. R. Burlage, H. Meertens, J. A. Langendijk, and A. A. van 't Veld, “3d variation in delineation of head and neck organs at risk,” Radiat. Oncol. 7, 32 (2012).
3. E. Weiss and C. F. Hess, “The impact of gross tumor volume (gtv) and clinical target volume (ctv) definition on the total accuracy in radiotherapy theoretical aspects and practical experiences,” Strahlenther. Onkol. 179(1), 21–30 (2003).
4. A. A. Qazi, V. Pekar, J. Kim, J. Xie, S. L. Breen, and D. A. Jaffray, “Auto-segmentation of normal and target structures in head and neck ct images: A feature-driven model-based approach,” Med. Phys. 38(11), 6160–6170 (2011).
5. X. Han, M. S. Hoogeman, P. C. Levendag, L. S. Hibbard, D. N. Teguh, P. Voet, A. C. Cowen, and T. K. Wolf, “Atlas-based auto-segmentation of head and neck CT images,” Med. Image Comput. Comput. Assist. Interv. 11(Pt 2), 434–441 (2008).
6. X. Zhang, J. Tian, Y. Wu, J. Zheng, and K. Deng, “Segmentation of head and neckCTscans using atlas-based level set method,” MIDAS J. (2009).
7. O. Commowick, S. K. Warfield, and G. Malandain, “Using frankenstein's creature paradigm to build a patient specific atlas,” Med. Image Comput. Comput. Assist. Interv. 12(Pt 2), 993–1000 (2009).
8. T. Rohlfing, R. Brandt, R. Menzel, and C. R. Maurer, “Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains,” Neuroimage 21(4), 1428–1442 (2004).
9. O. Commowick and G. Malandain, “Efficient selection of the most similar image in a database for critical structures segmentation,” Med. Image Comput. Comput. Assist. Interv. 10(Pt 2), 203–210 (2007).
10. B. M. Dawant, S. L. Hartmann, J. P. Thirion, F. Maes, D. Vandermeulen, and P. Demaerel, “Automatic 3-d segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations. I. Methodology and validation on normal subjects,” IEEE Trans. Med. Imaging 18(10), 909–916 (1999).
11. M. R. Sabuncu, B. T. Thomas Yeo, K. v. Leemput, Br. Fischl, and P. Golland, “A generative model for image segmentation based on label fusion,” IEEE Trans. Med. Imaging 29(10), 1714–1729 (2010).
12. F. van der Lijn, M. de Bruijne, S. Klein, T. den Heijer, Y. Y. Hoogendam, A. van der Lugt, M. M. B. Breteler, and W. J. Niessen, “Automated brain structure segmentation based on atlas registration and appearance models,” IEEE Trans. Med. Imaging 31(2), 276–286 (2012).
13. V. Fortunati, R. F. Verhaart, F. van der Lijn, W. J. Niessen, J. F. Veenland, M. M. Paulides, and T. van Walsum, “Tissue segmentation of head and neckCTimages for treatment planning: A multiatlas approach combined with intensity modeling,” Med. Phys. 40(7), 071905 (14pp.) (2013).
14. T. F. Cootes, G. J. Edwards, and C. J. Taylor, “Active appearance models,” IEEE Trans. Pattern Anal. Machine Intell. 23(6), 681–685 (2001).
15. T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models-their training and application,” Comput. Vis. Image Understand. 61(1), 38–59 (1995).
16. S. Zhang, Y. Zhan, X. Cui, M. Gao, J. Huang, and D. Metaxas, “3d anatomical shape atlas construction using mesh quality preserved deformable models,” Comput. Vis. Image Understand. 117(9), 1061–1071 (2013).
17. D. Rueckert, A. F. Frangi, and J. A. Schnabel, “Automatic construction of 3-d statistical deformation models of the brain using nonrigid registration,” IEEE Trans. Med. Imaging 22(8), 1014–1025 (2003).
18. K. D. Fritscher, A. Gruenerbl, and R. Schubert, “3d image segmentation using combined shape-intensity prior models,” Int. J. Comput. Assist. Radiol. Surg. 1(6), 341–350 (2007).
19. C. Wachinger, G. Sharp, and P. Golland, “Contour-driven regression for label inference in atlas-based segmentation,” Med. Image Comput. Comput. Assist. Interv. 16(Pt 3), 211–218 (2013).
21. M. Peroni, “Methods and algorithms for image guided adaptive radio- and hardon-therapy,” Ph.D. thesis (Politecnico di Milano, 2011).
22. J. V. Hajnal, D. Hill, and D. Hawkes, Medical Image Registration, Biomedical Engineering Series (CRC Press, Boca Raton, FL, 2001).
23. G. C. Sharp, M. Peroni, R. Li, J. Shackleford, and N. Kandasamy, “Evaluation of plastimatch b-spline registration on the empire10 data set,” Medical Image Analysis for the Clinic: A Grand Challenge (2010), pp. 99–108.
25. D. Mattes, D. R. Haynor, H. Vesselle, Th. K. Lewellyn, and W. Eubank, “Nonrigid multimodality image registration,” Proc. SPIE 4322, 1609–1620 (2001).
26. C. R. Maurer, R. Qi, and V. Raghavan, “A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions,” IEEE Trans. Pattern Anal. Machine Intell. 25(2), 265–270 (2003).
27. G. C. Sharp, M. Peroni Peroni, N. Shusharina, J. Shackleford, P. Golland, and G. Baroni, “A robust intensity similarity measure for multi-atlas segmentation,” Med. Phys. 40(6), 536–537 (2013).
29. M. Chen, W. Lu, Q. Chen, K. J. Ruchala, and G. H. Olivera, “A simple fixed-point approach to invert a deformation field,” Med. Phys. 35(1), 81–88 (2008).
30. K. Fritscher, A. Grunerbl, M. Hanni, N. Suhm, C. Hengg, and R. Schubert, “Trabecular bone analysis in CT and X-ray images of the proximal femur for the assessment of local bone quality,” IEEE Trans. Med. Imaging 28(10), 1560–1575 (2009).
31. B. Schuler, K. D. Fritscher, V. Kuhn, F. Eckstein, T. M. Link, and R. Schubert, “Assessment of the individual fracture risk of the proximal femur by using statistical appearance models,” Med. Phys. 37, 2560–2571 (2010).
32. J. C. Spall, Stochastic Optimization, Stochastic Approximation and Simulated Annealing (John Wiley and Sons, Inc., New York, 2001).
33. T. A. van de Water, H. P. Bijl, H. E. Westerlaan, and J. A. Langendijk, “Delineation guidelines for organs at risk involved in radiation-induced salivary dysfunction and xerostomia,” Radiother. Oncol. 93(3), 545–552 (2009).
34. B. v. Ginneken, K. Murphy, T. Heimann, V. Pekar, and X. Deng, Medical Image Analysis for the Clinic—A Grand Challenge (CreateSpace Independent Publishing Platform, 2010).
35. V. Pekar, S. Allaire, J. Kim, and D. Jaffray, “Head and neck auto-segmentation challenge,” MIDAS J. 11 (2009).
36. R. Sims, A. Isambert, V. Gregoire, F. Bidault, L. Fresco, J. Sage, J. Mills, J. Bourhis, D. Lefkopoulos, O. Commowick, M. Benkebil, and G. Malandain, “A pre-clinical assessment of an atlas-based automatic segmentation tool for the head and neck,” Radiother. Oncol. 93(3), 474–478 (2009).
37. J.-F. Daisne and A. Blumhofer, “Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: A clinical validation,” Radiat. Oncol. 8(1), 154 (2013).
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Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented.
The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other.
18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved.
The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.
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