## 1 Introduction
Currently, prior to radiotherapy planning, autocontours generated by commercially available algorithms (e.g., Limbus [26], MIM [27]) are generally used as a starting point for subsequent manual edits by experts. However, these algorithms cannot be used for nifteRT, during which real-time manual edits are not possible. For algorithms with higher contouring accuracy and speed (i.e., greater potential to be used for nifteRT), proper assessment of their contouring accuracy can still be difficult, as there is currently no set value for a clinically acceptable contouring accuracy. For instance, the autocontouring algorithm by [PERSON] et al. [16] achieved mean Dice coefficients (DCs) of 0.87-0.92 between manual and autocontours for lung cancer patients. However, we are not able to determine whether the algorithm can be utilized in clinics. Since manual contours by human experts are the current clinical standard for tumor contouring, one way of determining this is to compare contouring performance against experts' inherent uncertainties (i.e., intra- and inter-observer variations). In the literature, studies have investigated intra- and inter-observer variations in manual contours for 3D imaging of various sites within patients [28; 29; 30]. [PERSON] et al. [28] estimated inter-observer variation in manual contours for 3D prostate MR images, which was found to be 0.919 in terms of DC. In the study by [PERSON] et al. [29], intra- and inter-observer variations for 3D MRI of tumor vasculature were reported with Jaccard Index values of 0.870 and 0.630, respectively. Also, intra- and inter-observer variations using 2D cine MR images have been previously investigated for some tumor sites [31; 32]. In the study by [PERSON] et al. [31], inter-observer variation in manual contours for cine MR images of patients with lung, pancreatic, renal, and adrenal tumors was found to be 0.89 in terms of DC, averaged over all patients. However, intra- and inter-observer variations have not yet been quantified for 2D cine MR images of liver and prostate cancer patients. Those variations for lung patients have also not been quantified using numerous patient datasets. In addition, these three tumor sites have the potential for a large range of intrafractional tumor motion (up to 40 mm for lung [1] and liver [33], and 18 mm for prostate [34]) maximizing the clinical impact of nifteRT, while they also constitute a great portion of the radiotherapy patient population (~43% of all patients at our institution). Therefore, the primary aim of this study is to quantify intra- and inter-observer variations in manual contours for each of these tumor sites, and the secondary aim is to evaluate our tumor-autocontouring algorithm against the manual contours from multiple experts. In this work, three experts (radiation oncologists) performed manual contouring of the tumor in dynamic MR images of 10 liver, 10 prostate, and 10 lung patients in two different sessions (300 images per patient, ~1-h tumor contouring per patient, and >20 weeks between sessions). An in-house-built tumor-autocontouring algorithm was optimized and trained for each patient, expert, and session (PES) to generate autocontours. The contours were subsequently evaluated by performing Automatic vs. Manual and Manual vs. Manual contour comparisons. With the tumors being patient-specific and the contours being PES-specific, customization of the autocontouring algorithm through the PES-specific optimization can largely improve the performance of the algorithm compared to using a single, general algorithm across all PESs. We hypothesize that our algorithm can generate tumor autocontours in each MR image within a range of tens of milliseconds, whose evaluation metrics against the manual contours are comparable to the manual intra- and inter-observer variation metrics.