Abstract
Purpose/Objectives Manual contouring techniques are tedious and time consuming, increasing the importance of automatic segmentation approaches to reduce the contouring burden. Addressing this need, we previously demonstrated a single-best matched atlas-based segmentation method (SBM) that provided a contouring time savings of 67-86% for head and neck cancer and 46% for prostate cancer1,2. While offering significant time savings, single subject atlas segmentation may not adequately address the anatomical and positional variability that can occur even when using a subject that is found to be most similar to the patient. A new multi-atlas approach has been shown to provide greater accuracy than SBM for cancer of the head and neck, prostate, and liver3,4,5. The goal of this study was to evaluate the multi-atlas technique for lung cancer treatment planning.
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