Poster — Thur Eve — 65: Optimization of an automatic image contouring system for radiation therapy

Intensity modulated radiation therapy (IMRT) is an advanced technique used to concentrate the prescribed dose in the tumour while minimizing exposure to healthy tissues. Success in IMRT is greatly dependent upon the localization of the target volume and normal tissue, thus accurate contouring is crucial. In this paper, we describe an automated atlas-based image contouring system and our approach for improving the system by performing a full-scale optimization of registration parameters using high-performance computing. To achieve this, we use manually pre-contoured CT images of ten head and neck patients. For any parameter set, each patient data is registered with the remaining patients. Accuracy of the resulting contours is determined automatically by comparing their overlap with manually defined targets using Dice's similarity coefficient (DSC). This allows us to compare all permutations of the image registration parameter sets and input data to investigate their impact on final contour accuracy. Investigating the parameter space required 27,000 image registrations and 216,000 DSC computations. To perform these registrations we introduced a large cluster of high-performance computers and developed a parallel testing harness. The metrics collected from the tests show a wide range of performance, indicating that parameter selection is crucial in our contouring system. By selecting an optimized parameter set, we increased the mean overlap of the automatically contoured regions of interest by 50% and reduced registration time by 50% compared to the original parameters. Our findings illustrate that full-scale optimization is an effective method for improving the performance of the automated image contouring system.