Toxicity to cardiac and coronary structures is an important late morbidity for patients undergoing left-sided breast radiotherapy. Many current studies have relied on estimates of cardiac doses assuming standardised anatomy, with a calculated increase in relative risk of 7.4% per Gy (mean heart dose). To provide individualised estimates for dose, delineation of various cardiac structures on patient images is required. Automatic multi-atlas based segmentation can provide a consistent, robust solution, however there are challenges to this method. We are aiming to develop and validate a cardiac atlas and segmentation framework, with a focus on the limitations and uncertainties in the process. We present a probabilistic approach to segmentation, which provides a simple method to incorporate inter-observer variation, as well as a useful tool for evaluating the accuracy and sources of error in segmentation. A dataset consisting of 20 planning computed tomography (CT) images of Australian breast cancer patients with delineations of 17 structures (including whole heart, four chambers, coronary arteries and valves) was manually contoured by three independent observers, following a protocol based on a published reference atlas, with verification by a cardiologist. To develop and validate the segmentation framework a leave-one-out cross-validation strategy was implemented. Performance of the automatic segmentations was evaluated relative to inter-observer variability in manually-derived contours; measures of volume and surface accuracy (Dice similarity coefficient (DSC) and mean absolute surface distance (MASD), respectively) were used to compare automatic segmentation to the consensus segmentation from manual contours. For the whole heart, the resulting segmentation achieved a DSC of , with a MASD of mm. Quantitative results, together with the analysis of probabilistic labelling, indicate the feasibility of accurate and consistent segmentation of larger structures, whereas this is not the case for many smaller structures, where a major limitation in segmentation accuracy is the inter-observer variability in manual contouring.

Introduction Inter‐observer variability (IOV) in target volume delineation is a source of error in head and neck radiotherapy. Diffusion‐weighted imaging (DWI) has been shown to be useful in detecting recurrent head and neck cancer. This study aims to determine whether DWI improves target volume delineation and IOV. Methods Four radiation oncologists delineated the gross tumour volume (GTV) for ten head and neck cancer patients. Delineation was performed on CT alone as well as fused image sets which incorporated fluorodeoxyglucose (FDG)‐positron emission tomography (PET) and magnetic resonance imaging (MRI) in the form of CT/PET, CT/PET/T2W and CT/PET/T2W/DWI image sets. Analysis of the variability of contour volumes was completed by comparison to the simultaneous truth and performance level estimation (STAPLE) volumes. The DICE Similarity Coefficient (DSC) and other IOV metrics for each observer's contour were compared to the STAPLE for each patient and image dataset. A DWI usability scoresheet for delineation was completed. Results The CT/PET/T2W/DWI mean GTV volume of 13.37 (10.35–16.39)cm3 was shown to be different to the mean GTV of 10.92 (8.32–13.51)cm3 when using CT alone (P < 0.001). The GTV DSC amongst observers for CT alone was 0.72 (0.65–0.79), CT/PET was 0.73 (0.67–0.80), CT/PET/T2W was 0.71 (0.64–0.77) and CT/PET/T2W/DWI was 0.69 (0.61–0.75). Conclusion Mean GTVs with the addition of DWI had slightly larger volumes compared to standard CT and CT/PET volumes. DWI may add supplemental visual information for GTV delineation while having a small impact on IOV, therefore potentially improving target volume delineation.

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