title: The suitability of common metrics for assessing parotid and larynx autosegmentation accuracy.
abstract: Contouring structures in the head and neck is time-consuming, and automatic seg-mentation is an important part of an adaptive radiotherapy workflow. Geometric accuracy of automatic segmentation algorithms has been widely reported, but there is no consensus as to which metrics provide clinically meaningful results. This study investigated whether geometric accuracy (as quantified by several commonly used metrics) was associated with dosimetric differences for the parotid and larynx, comparing automatically generated contours against manually drawn ground truth contours. This enabled the suitability of different commonly used metrics to be assessed for measuring automatic segmentation accuracy of the parotid and larynx. Parotid and larynx structures for 10 head and neck patients were outlined by five clinicians to create ground truth structures. An automatic segmentation algorithm was used to create automatically generated normal structures, which were then used to create volumetric-modulated arc therapy plans. The mean doses to the automatically generated structures were compared with those of the corresponding ground truth structures, and the relative difference in mean dose was calculated for each structure. It was found that this difference did not correlate with the geometric accuracy provided by several metrics, notably the Dice similarity coefficient, which is a commonly used measure of spatial overlap. Surface-based metrics provided stronger correlation and are, therefore, more suitable for assessing automatic seg-mentation of the parotid and larynx.
authors.affiliation: University of Manchester; The Christie NHS Foundation Trust. william.beasley@christie.nhs.uk.
authors.forename: William J
authors.initials: WJ
authors.lastname: Beasley
authors.forename: Alan
authors.initials: A
authors.lastname: McWilliam
authors.forename: Adam
authors.initials: A
authors.lastname: Aitkenhead
authors.forename: Ranald I
authors.initials: RI
authors.lastname: Mackay
authors.forename: Carl G
authors.initials: CG
authors.lastname: Rowbottom
mesh_terms: D000465:Algorithms; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D006801:Humans; D007830:Larynx / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D010306:Parotid Gland / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated; D014057:Tomography, X-Ray Computed / Q000379:methods*