## 4 Discussion  
Intrafractional MR-guided radiotherapy demands accurate and fast tumor segmentation in each dynamic image. To implement a tumor-autocontouring algorithm, its accuracy must be evaluated against \"gold standard\" contours, which are currently expert-drawn but vary among experts and different sessions of an expert. Since there is no practical way to determine which set of expert-drawn contours is the actual truth, the accuracy of the \"gold standard\" can be indicated by intra- and inter-observer variations, which are unknown for cine MR images of liver and prostate cancer patients. This work quantified these variations to establish a clinically acceptable accuracy level for a tumor-autocontouring algorithm in intrafractional MR-guided radiotherapy. Depending on clinical scenarios, an autocontouring algorithm can be trained and tested using either (i) an expert's contours from different sessions, or (ii) different experts' contours. For scenario (i), an autocontour can be considered acceptable if it has Automatic vs. Manual SEDS evaluation metrics comparable to Manual vs. Manual intra-observer variations. For scenario (ii), an autocontour can be considered acceptable if it has Automatic vs. Manual DE metrics comparable to Manual vs. Manual inter-observer variations. If those metrics are comparable in each scenario, we can claim that the algorithm faithfully imitates the contouring performance of the human experts and thus can be adopted for nifeRT. As shown in Table 2, the Automatic vs. Manual SEDS and DE metrics were similar to the Manual vs. Manual intra- and inter-observer variations, respectively. For example, the Automatic vs. Manual SEDS and DE DCs for liver (0.79 and 0.70) were 1% higher than the Manual vs. Manual SEDS and DE DCs (0.78 and 0.69). This demonstrates that the algorithm mimicked each expert's contours with as much uncertainty as there is between different sessions or different experts. The worst overall agreement was observed for liver patients, whereas the best agreement was observed for prostate patients for most of the comparisons. For the DC values, this can be due to the smaller size of the liver tumor (8.3 cm\({}^{2}\) on average) compared to the prostate (18.7 cm\({}^{2}\) on average) for most patient cases. Since DC is a percentage of two contour areas, even a slight change in the intersecting area between two small contours leads to a large change in the overall DC value. In terms of HD, agreement was better for liver patients for the Automatic vs. Manual SEDS and DE matches, as well as for the Manual vs. Manual DE match, as shown in Table 2. Also, another potential cause is the lower image contrast between the liver tumor and the normal tissue background for some patient cases. If the patients are imaged with the gadolinium contrast agent, the contouring performance of the algorithm and the experts, as well as their agreements, would likely be improved. In contrast, for both prostate and lung patients, the boundary between the prostate and its background (especially in coronal or axial plane images), as well as the lung tumor and its background, was quite clear without contrast enhancement. The manual intra-observer agreements were found to be better than the manual inter-observer agreements consistently for all tumor sites, suggesting that the experts tend to agree more with themselves than with the other experts. This is consistent with what has been demonstrated in the literature [43; 44; 45]. Also, our manual intra-/inter-observer agreements differ from those in the previous tumor contouring studies. For lung patients, our agreements (mean DC: 0.84/0.75) are lower than those reported by [PERSON] et al. (mean DC: 0.88/0.87) [32]. This discrepancy may be due to variations in study protocols, including different contouring software (3D Slicer vs. computational environment for radiotherapy research (CERR)) and numbers of patients (10 vs. 6 patients). For prostate patients, our agreements (mean DC: 0.92/0.87) are higher than those found by [PERSON] et al.