A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer.
This work describes the clinical validation of an automatic segmentation algorithm in CT-based radiotherapy planning for prostate cancer patients. The validated auto-segmentation algorithm (Smart Segmentation, version 1.0.05) is a rule-based algorithm using anatomical reference points and organ-specific segmentation methods, developed by Varian Medical Systems (Varian Medical Systems iLab, Baden, Switzerland). For the qualitative analysis, 39 prostate patients are analysed by six clinicians. Clinicians are asked to rate the auto-segmented organs (prostate, bladder, rectum and femoral heads) and to indicate the number of slices to correct. For the quantitative analysis, seven radiation oncologists are asked to contour seven prostate patients. The individual clinician contour variations are compared to the automatic contours by means of surface and volume statistics, calculating the relative volume errors and both the volume and slice-by-slice degree of support, a statistical metric developed for the purposes of this validation. The mean time needed for the automatic module to contour the four structures is about one minute on a standard computer. The qualitative evaluation using a score with four levels ("not acceptable", "acceptable", "good" and "excellent") shows that the mean score for the automatically contoured prostate is "good"; the bladder scores between "excellent" and "good"; the rectum scores between "acceptable" and "not acceptable". Using the concept of surface and volume degree of support, the degree of support given to the automatic module is comparable to the relative agreement among the clinicians for prostate and bladder. The slice-by-slice analysis of the surface degree of support pinpointed the areas of disagreement among the clinicians as well as between the clinicians and the automatic module. The efficiency and the limits of the automatic module are investigated with both a qualitative and a quantitative analysis. In general, with efficient correction tools at hand, the use of this auto-segmentation module will lead to a time gain for the prostate and the bladder; with the present version of the algorithm, modelling of the rectum still needs improvement. For the quantitative validation, the concept of relative volume error and degree of support proved very useful.