Reflections on the current status of commercial automated segmentation systems in clinical practice

Delineation of multiple anatomical structures, both target and organs at risk, is a requirement for the planning of modern radiotherapy techniques such as intensity modulated radiation therapy (IMRT) and volumetric modulated arc radiation therapy (VMAT) as well as for the reporting of dose to structures for correlation with treatment outcome. Manual delineation of the many anatomical structures, particularly target structures which require a radiologist or radiation oncologists input, is time-consuming and therefore expensive. As such, it is one of the major obstacles to meeting the increasing demand for IMRT and VMAT. Automatic segmentation has for a long time held the promise of decreasing the requirement for manual delineation but has been the reserve of research institutions using ‘in-house’ software solutions. With the recent availability of commercial systems for automatic image segmentation (at least 11 now available), there has been a rapid increase in published articles evaluating the performance of these algorithms for the automatic segmentation of structures in various anatomical sites. In this issue of the Journal of Medical Radiation Sciences, Greenham et al. evaluate the performance of the ABAS system (Elekta AB, Stockholm, Sweden) for delineation of the prostate and pelvic organs. As with other automatic segmentation studies, they report varying degrees of success with some structures being clinically acceptable while others require considerable manual editing. Variation in performance across patients is also observed. Indeed, this is the finding of the author’s own institution (not published) where the ABAS system has been implemented for head and neck and prostate patients since 2011. Typically, there is a net reduction in the amount of time taken to delineate the contours if they are first automatically created and then manually edited. In this editorial, the different methods of automatic segmentation are introduced briefly before discussing some of their fundamental limitations. Methods of Auto-Segmentation