title: Automated delineation of radiotherapy volumes: are we going in the right direction?
abstract: Rapid and accurate delineation of target volumes and multiple organs at risk, within the enduring International Commission on Radiation Units and Measurement framework, is now hugely important in radiotherapy, owing to the rapid proliferation of intensity-modulated radiotherapy and the advent of four-dimensional image-guided adaption. Nevertheless, delineation is still generally clinically performed with little if any machine assistance, even though it is both time-consuming and prone to interobserver variation. Currently available segmentation tools include those based on image greyscale interrogation, statistical shape modelling and body atlas-based methods. However, all too often these are not able to match the accuracy of the expert clinician, which remains the universally acknowledged gold standard. In this article we suggest that current methods are fundamentally limited by their lack of ability to incorporate essential human clinical decision-making into the underlying models. Hybrid techniques that utilise prior knowledge, make sophisticated use of greyscale information and allow clinical expertise to be integrated are needed. This may require a change in focus from automated segmentation to machine-assisted delineation. Similarly, new metrics of image quality reflecting fitness for purpose would be extremely valuable. We conclude that methods need to be developed to take account of the clinician's expertise and honed visual processing capabilities as much as the underlying, clinically meaningful information content of the image data being interrogated. We illustrate our observations and suggestions through our own experiences with two software tools developed as part of research council-funded projects.
authors.affiliation: University of Manchester, Manchester, UK.
authors.forename: G A
authors.initials: GA
authors.lastname: Whitfield
authors.forename: P
authors.initials: P
authors.lastname: Price
authors.forename: G J
authors.initials: GJ
authors.lastname: Price
authors.forename: C J
authors.initials: CJ
authors.lastname: Moore
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence / Q000639:trends*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods / Q000639:trends*; D011856:Radiographic Image Enhancement / Q000379:methods / Q000639:trends*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D061089:Radiotherapy, Image-Guided / Q000379:methods / Q000639:trends; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods / Q000639:trends*