Evaluation of atlas based auto-segmentation for head and neck target volume delineation in adaptive/replan IMRT

Abstract

IMRT for head and neck patients requires clinicians to delineate clinical target volumes (CTV) on a planning-CT (>2hrs/patient). When patients require a replan-CT, CTVs must be re-delineated. This work assesses the performance of atlas-based autosegmentation (ABAS), which uses deformable image registration between planning and replan-CTs to auto-segment CTVs on the replan-CT, based on the planning contours. Fifteen patients with planning-CT and replan-CTs were selected. One clinician delineated CTVs on the planning-CTs and up to three clinicians delineated CTVs on the replan-CTs. Replan-CT volumes were auto-segmented using ABAS using the manual CTVs from the planning-CT as an atlas. ABAS CTVs were edited manually to make them clinically acceptable. Clinicians were timed to estimate savings using ABAS. CTVs were compared using dice similarity coefficient (DSC) and mean distance to agreement (MDA). Mean inter-observer variability (DSC>0.79 and MDA<2.1mm) was found to be greater than intra-observer variability (DSC>0.91 and MDA<1.5mm). Comparing ABAS to manual CTVs gave DSC=0.86 and MDA=2.07mm. Once edited, ABAS volumes agreed more closely with the manual CTVs (DSC=0.87 and MDA=1.87mm). The mean clinician time required to produce CTVs reduced from 169min to 57min when using ABAS. ABAS segments volumes with accuracy close to inter-observer variability however the volumes require some editing before clinical use. Using ABAS reduces contouring time by a factor of three.