Abstract
Autosegmentation of image guidance (IG) scans is crucial for streamlining and optimising delivered dose calculation in radiotherapy. By accounting for interfraction motion, daily delivered dose can be accumulated and incorporated into automated systems for adaptive radiotherapy. Autosegmentation of IG scans is challenging due to poorer image quality than typical planning kilovoltage computed tomography (kVCT) systems, and the resulting reduction of soft tissue contrast in regions such as the pelvis makes organ boundaries less distinguishable. Current autosegmentation solutions generally involve propagation of planning contours to the IG scan by deformable image registration (DIR). Here, we present a novel approach for primary autosegmentation of the rectum on megavoltage IG scans acquired during prostate radiotherapy, based on the Chan-Vese algorithm. Pre-processing steps such as Hounsfield unit/intensity scaling, identifying search regions, dealing with air, and handling the prostate, are detailed. Post-processing features include identification of implausible contours (nominally those affected by muscle or air), 3D self-checking, smoothing, and interpolation. In cases where the algorithm struggles, the best estimate on a given slice may revert to the propagated kVCT rectal contour. Algorithm parameters were optimised systematically for a training cohort of 26 scans, and tested on a validation cohort of 30 scans, from 10 patients. Manual intervention was not required. Comparing Chan-Vese autocontours with contours manually segmented by an experienced clinical oncologist achieved a mean Dice Similarity Coefficient of 0.78 (SE < 0.011). This was comparable with DIR methods for kVCT and CBCT published in the literature. The autosegmentation system was developed within the VoxTox Research Programme for accumulation of delivered dose to the rectum in prostate radiotherapy, but may have applicability to further anatomical sites and imaging modalities.
Highlights
Automated segmentation of the anatomy, or autosegmentation, is crucial for optimising the efficacy of adaptive radiotherapy (ART) (Jaffray et al 2010, Godley et al 2013, Thor et al 2013, Whitfield et al 2013)
The expanse of information contained within Image guided radiotherapy (IGRT) images is not currently being realised to its full potential, and this is partly due to the dependency on manual contouring
The development of robust and automated approaches to segmentation has been identified as a key aspect in the pursuit of delivered dose calculation for ART (Jaffray et al 2010), as manual contouring of daily image guidance (IG) scans is unfeasible
Summary
Automated segmentation of the anatomy, or autosegmentation, is crucial for optimising the efficacy of adaptive radiotherapy (ART) (Jaffray et al 2010, Godley et al 2013, Thor et al 2013, Whitfield et al 2013). The development of robust and automated approaches to segmentation has been identified as a key aspect in the pursuit of delivered dose calculation for ART (Jaffray et al 2010), as manual contouring of daily IG scans is unfeasible. Would this introduce an impracticable excess to the clinical workload (Gambacorta et al 2013, Scaife et al 2014), but additional training would be required due to the poorer soft tissue definition of IG scans when compared with the more familiar kilovoltage (kV) treatment planning scans (Whitfield et al 2013). Approaches for autosegmentation to date have generally focused on intensity values, atlas-based tools, or shape-based models, each with their own limitations (Whitfield et al 2013)
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