A Shape Relationship Descriptor for Radiation Therapy Planning

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.

[1]  Martial Hebert,et al.  Object Representation in Computer Vision , 1994, Lecture Notes in Computer Science.

[2]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[3]  Jitendra Malik,et al.  Recognizing Objects in Range Data Using Regional Point Descriptors , 2004, ECCV.

[4]  Hans-Peter Kriegel,et al.  3D Shape Histograms for Similarity Search and Classification in Spatial Databases , 1999, SSD.

[5]  Dietmar Saupe,et al.  3D Model Retrieval with Spherical Harmonics and Moments , 2001, DAGM-Symposium.

[6]  Berthold K. P. Horn Extended Gaussian images , 1984, Proceedings of the IEEE.

[7]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.

[9]  James Scott,et al.  Fast polygon mesh querying by example , 1999, SIGGRAPH '99.

[10]  Ali Shokoufandeh,et al.  Retrieving Articulated 3-D Models Using Medial Surfaces and Their Graph Spectra , 2005, EMMCVPR.

[11]  Jun-ichiro Toriwaki,et al.  New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications , 1994, Pattern Recognit..

[12]  Martial Hebert,et al.  Efficient multiple model recognition in cluttered 3-D scenes , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[13]  Michael Garland,et al.  Curvature maps for local shape comparison , 2005, International Conference on Shape Modeling and Applications 2005 (SMI' 05).

[14]  Russell H. Taylor,et al.  Patient geometry-driven information retrieval for IMRT treatment plan quality control. , 2009, Medical Physics (Lancaster).

[15]  Sen Wang,et al.  Conformal Geometry and Its Applications on 3D Shape Matching, Recognition, and Stitching , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  David P. Dobkin,et al.  A search engine for 3D models , 2003, TOGS.

[17]  Paul J. Besl Triangles as a Primary Representation , 1994, Object Representation in Computer Vision.

[18]  Bernard Chazelle,et al.  Matching 3D models with shape distributions , 2001, Proceedings International Conference on Shape Modeling and Applications.

[19]  Andrew Jackson,et al.  Geometric factors influencing dosimetric sparing of the parotid glands using IMRT. , 2006, International journal of radiation oncology, biology, physics.

[20]  Ming Ouhyoung,et al.  On Visual Similarity Based 3D Model Retrieval , 2003, Comput. Graph. Forum.

[21]  Bernard Chazelle,et al.  A Reflective Symmetry Descriptor , 2002, ECCV.

[22]  R Mohan,et al.  Dose-volume histograms. , 1991, International journal of radiation oncology, biology, physics.

[23]  Max J. Egenhofer,et al.  Advances in Spatial Databases , 1997, Lecture Notes in Computer Science.