The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks.

An efficient method for volumetric intensity modulated arc therapy (VMAT) planning was developed, where a single arc (360 degrees or less) is delivered under continuous variation of multileaf collimator (MLC) segments, dose rate, and gantry speed. Plans can be generated for any current linear accelerator that supports these degrees of freedom. MLC segments are derived from fluence maps at relatively coarsely sampled angular positions. The beam segments, dose rate, and gantry speed are then optimized using direct machine parameter optimization based on dose volume objectives and leaf motion constraints to minimize arc delivery time. The method can vary both dose rate and gantry speed or alternatively determine the optimal plan at constant dose rate and gantry speed. The method was used to retrospectively generate variable dose rate VMAT plans to ten patients (head and neck, prostate, brain, lung, and tonsil). In comparison to step-and-shoot intensity modulated radiation therapy, dosimetric plan quality was comparable or improved, estimated delivery times ranged from 70 to 160 s, and monitor units were consistently reduced in nine out of the ten cases by an average of approximately 6%. Optimization and final dose calculation took between 5 and 35 min depending on plan complexity.

An automated brain tumor segmentation method was developed and validated against manual segmentation with three-dimensional magnetic resonance images in 20 patients with meningiomas and low-grade gliomas. The automated method (operator time, 5-10 minutes) allowed rapid identification of brain and tumor tissue with an accuracy and reproducibility comparable to those of manual segmentation (operator time, 3-5 hours), making automated segmentation practical for low-grade gliomas and meningiomas.

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