A statistical modeling approach for evaluating auto-segmentation methods for image-guided radiotherapy.

We proposed a statistical modeling method for the quantitative evaluation of segmentation methods used in image guided radiotherapy. A statistical model parameterized on a Beta distribution was built upon the observations of the volume overlap between the segmented structure and the referenced structure. A statistical performance profile (SPP) was then estimated from the model using the generalized maximum likelihood approach. The SPP defines the probability density function characterizing the distribution of performance values and provides a graphical visualization of the segmentation performance. Different segmentation approaches may be influenced by image quality or observer variability. Our statistical model was able to quantify the impact of these variations and displays the underlying statistical performance of the segmentation algorithm. We demonstrated the efficacy of this statistical model using both simulated data and clinical evaluation studies in head and neck radiotherapy. Furthermore, the resulting SPP facilitates the measurement of the correlation between quantitative metrics and clinical experts' decision, and ultimately is able to guide the clinicians in selecting segmentation methods for radiotherapy.