Abstract
In this paper we address the challenge of matching patient geometry to facilitate the design of patient treatment plans in radiotherapy. To this end we propose a novel shape descriptor, the Overlap Volume Histogram, which provides a rotation and translation invariant representation of a patient’s organs at risk relative to the tumor volume. Using our descriptor, it is possible to accurately identify database patients with similar constellations of organ and tumor geometries, enabling the transfer of treatment plans between patients with similar geometries. We demonstrate the utility of our method for such tasks by outperforming state of the art shape descriptors in the retrieval of patients with similar treatment plans. We also preliminarily show its potential as a quality control tool by demonstrating how it is used to identify an organ at risk whose dose can be significantly reduced.
This research was supported in part by the generosity of Paul Maritz, Philips Radiation Oncology Systems (Madison, WI), and by Johns Hopkins University internal funds.
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Keywords
- Shape Descriptor
- Radiation Therapy Planning
- Shape Match
- Quality Control Tool
- Euclidean Distance Transformation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Eisbruch, A., Chao, K.C., Garden, A.: Phase I/II study of conformal and intensity modulated irradiation for oropharyngeal cancer (RTOG 0022). Radiation Therapy Oncology Group of the American College of Radiology (2004)
Ankerst, M., Kastenmüller, G., Kriegel, H.-P., Seidl, T.: 3D shape histograms for similarity search and classification in spatial databases. In: Güting, R.H., Papadias, D., Lochovsky, F.H. (eds.) SSD 1999. LNCS, vol. 1651, pp. 207–228. Springer, Heidelberg (1999)
Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Matching 3D models with shape distributions. In: IEEE International Conference on Shape Modeling and Applications, pp. 154–166 (2001)
Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape distributions. Transactions on Graphics 21(4), 807–832 (2002)
Besl, P.: Triangles as a primary representation. In: Hebert, M., Boult, T., Gross, A., Ponce, J. (eds.) NSF-WS 1994 and ARPA-WS 1994. LNCS, vol. 994, pp. 191–206. Springer, Heidelberg (1995)
Zhang, J., Siddiqi, K., Macrini, D., Shokouf, A., Dickinson, S.: Retrieving articulated 3-D models using medial surfaces and their graph spectra. In: Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 285–300 (2005)
Horn, B.: Extended Gaussian images. In: Proceedings of the IEEE, vol. 72, pp. 1656–1678 (1984)
Kazhdan, M., Chazelle, B., Dobkin, D., Finkelstein, A., Funkhouser, T.: A reflective symmetry descriptor. In: European Conference on Computer Vision, pp. 642–656 (2002)
Wang, S., Wang, Y., Jin, M., Gu, X.D., Samaras, D.: Conformal geometry and its applications on 3d shape matching, recognition, and stitching. IEEE PAMI 29(7) (2007)
Vranic, D., Saupe, D.: 3D model retrieval with spherical harmonics and moments. In: DAGM Symposium on Pattern Recognition, pp. 392–397 (2001)
Gain, J., Scott, J.: Fast polygon mesh querying by example. SIGGRAPH Technical Sketches, 241 (1999)
Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., Jacobs, D.: A search engine for 3D models. ACM Transactions on Graphics 22(1), 83–105 (2003)
Chen, D., Tian, X., Shen, Y., Ouhyoung, M.: On visual similarity based 3D model retrieval. Computer Graphics Forum 22(3), 223–232 (2003)
Johnson, A., Hebert, M.: Efficient multiple model recognition in cluttered 3-D scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 671–677 (1998)
Johnson, A.E., Hebert, M.: Using spin-images for efficient multiple model recognition in cluttered 3-D scenes. IEEE PAMI 21(5), 433–449 (1999)
Frome, A., Huber, D., Kolluri, R., Bulow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: European Conference on Computer Vision, pp. 224–237 (2004)
Gatzke, T., Grimm, C.: Curvature maps for local shape comparison. In: IEEE International Conference on Shape Modeling and Applications, pp. 244–253 (2005)
Hunt, M.A., Jackson, A., Narayana, A., Lee, N.: Geometric factors influencing dosimetric sparing of the parotid glands using IMRT. International journal of radiation oncology, biology, physics 66, 296–304 (2006)
Saito, T., Toriwaki, J.: New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications. Pattern Recognition 27(11), 1551–1565 (1994)
Rubner, Y., Tomasi, C., Guibas, L.: The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision 40, 99–121 (2000)
Drzymala, R., Brewster, L., Chu, J., Goitein, M., Harms, W., Urie, M.: Dose-volume histograms. International Journal of Radiation Oncology, Biology and Physics 21, 71–78 (1991)
Wu, B., Ricchetti, F., Sanguineti, G., Kazhdan, M., Simari, P., Chuang, M., Taylor, R., Jacques, R., McNutt, T.: Patient geometry-driven information retrieval for IMRT treatment plan quality control. Medical Physic. (in submission)
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Kazhdan, M. et al. (2009). A Shape Relationship Descriptor for Radiation Therapy Planning. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04271-3_13
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DOI: https://doi.org/10.1007/978-3-642-04271-3_13
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