1. Methods Conclusions References ResultsIntroduction After all tests were performed, the optimal tolerance value was 1.125. This tolerance value had an overall agreement percentage with Eclipse™ of 92.69%, which was higher than the agreement percentage for the original tolerance value 0.001, which was 92.06%. In particular, the brain stem contour showed the largest improvement when using a tolerance of 1.125, as with this value it had an individual agreement percentage of 66.95%, which is significantly higher than the agreement percentage of 43.01% when using the original tolerance value. Contour rasterization evaluation Jennifer Andrea Mentors: Professor Gabor Fichtinger, Andras Lasso, Csaba Pinter Laboratory for Percutaneous Surgery, School of Computing, Queen’s University, Kingston, ON Development The effect of changing the tolerance value on the results was tested using 22 different tolerance values. The results were measured by computing the dose volume histograms (DVHs) for the labelmaps produced using the modified contour rasterization algorithm in SlicerRT. The same contours were then rasterized using the commercial software system Eclipse™, and DVHs were computed from these labelmaps. The two sets of DVHs were compared, using the Eclipse™ DVHs as the standard to which the SlicerRT DVHs were measured against. A similar evaluation method was used by the radiation therapy treatment planning research system CERR [2] to evaluate their contour rasterization algorithm. To quantify the difference between the sets of DVHs, the agreement percentage was computed for each contoured structure of the SlicerRT DVHs to the Eclipse™ DVHs. A broad range of values from 0 to 10 were tested, and further tests were performed to narrow in on the optimal tolerance value. Background In radiation oncology, contours are used to delineate structures including the target structure and structures at risk, in order to define them in 3D space. These contours are used when computing an optimized irradiation plan to ensure that the target receives the maximum dose possible and the structures at risk avoid being irradiated at a toxic level. In the medical image standard DICOM, contours are stored as a series of 2D planar contours. However, most analytical and processing algorithms require binary volumes, called labelmaps, as input. An accurate and robust algorithm for the conversion, which is called rasterization, is required. Software 3D Slicer (www.slicer.org) is an open-source software platform for medical image visualization and analysis SlicerRT (www.SlicerRT.org) is an open-source radiation therapy research toolkit developed for 3D Slicer [1] that provides RT-related data management, and analysis tools for contours and dose distributions Figure 1: Computing a dose volume histogram in SlicerRT. Figure 2: Overall agreement percentage between the dose volume histograms produced from the SlicerRT and Eclipse™ contour rasterization algorithms. Changing the tolerance value to a more optimal value improves the overall agreement percentage between the SlicerRT and Eclipse™ labelmaps and indicates an increase in accuracy of the contour rasterization algorithm. This optimization presents a step in achieving equivalent accuracy with the SlicerRT contour rasterization algorithm to those from commercial software systems. Future work will include adding out-of-plane interpolation of contours and rasterization error computation to the algorithm. [1] C. Pinter, A. Lasso, A. Wang, D. Jaffray, and G. Fichtinger, "SlicerRT: Radiation therapy research toolkit for 3D Slicer", Med. Phys. 39(10), 6332/7 (2012). [2] J. O. Deasy, A. I. Blanco, and V. H. Clark. "CERR: a computational environment for radiotherapy research." Medical physics 30.5: 979-985 (2003). Objective The purpose of this project was to determine the optimal rasterization settings for the contour rasterization algorithm currently present in SlicerRT [1]. Research Changing the result of the contour rasterization algorithm involves changing how border voxels from the reference volume are handled, which are those voxels that sit on the border of a contour and do not obviously fall either inside or outside of the contoured structure. Examination of the contour rasterization algorithm in SlicerRT revealed the presence of a variable for tolerance used in subroutines for filling in the contour and inserting points from line segments from the planar contour into the raster lines. The tolerance value is a variable that determines the bounds of the contour, which limits which voxels are considered inside the contour. A higher tolerance value results in more border voxels being included. In the original SlicerRT contour rasterization algorithm, the tolerance had a value of 0.001. We suspect that this value was suboptimal. Figure 3: Agreement percentages for each of the different structures tested.