## 1 Introduction  
In radiation therapy, continuous tumor motion presents a major challenge in accurately targeting the tumor while minimizing exposure of surrounding healthy tissues. This issue is further compounded when the tumor experiences substantial movement and shape deformation (e.g., up to 40 mm in superior-inferior, 15 mm in anterior-posterior, and 10 mm in left-right directions [1; 2; 3], with volume changes up to 20% and rotations up to 50 degrees with respect to each axis during normal breathing for lung tumors [4]). Recently, real-time magnetic resonance (MR)-guided radiotherapy has become feasible through the development of novel hybrid systems known as linac-MR [5; 6; 7; 8], which combine a radiotherapy device called a linear accelerator and the imaging capability of MRI. These systems enable real-time MR imaging of the tumor region with high soft-tissue contrast during irradiation, offering the potential to achieve non-invasive intrafractional tumor-tracked radiotherapy (nifteRT) [9; 10; 11; 12]. nifteRT is a novel radiotherapy approach under development in which the therapeutic radiation beam dynamically tracks the motion of the tumor in real time, guided by intrafractional MR imaging. This is achieved by continuously adjusting the beam's shape and position using multi-leaf collimator (MLC) control. The feasibility of nifteRT was first demonstrated in a phantom study by [PERSON] et al. [9]. Further details on the development and validation of nifteRT can be found in Reference [9]. For nifteRT using a linac-MR, the initial step is to identify the tumor's shape and position in each MR image. According to the American Association of Physicists in Medicine (AAPM) Task Group 76 report [13], a maximum delay of 500 ms is recommended between detecting the tumor's position and delivering the beam once the MLC aligns with the tumor. This delay includes the time for image acquisition, reconstruction, contouring, and MLC repositioning. For image acquisition and reconstruction, ideally, a 3D image volume is acquired to capture any 3D motion of the tumor. However, techniques used for 3D imaging have long acquisition and reconstruction times (>400 ms) [14; 15], which are not suitable for real-time tracking. Hence, we have chosen to use fast 2D MR imaging at ~4 frames/s for nifteRT. For contouring, the clinical gold standard for tumor detection is manual contouring by experts. Since it is not feasible for a human expert to contour images at the fast imaging rate of linac-MR for the duration of treatment, various real-time autocontouring methods have been previously developed [9; 16; 17; 18; 19; 20]. These methods are based on template matching, deformable image registration (DIR), and neural networks. Template-matching methods use a pre-defined template and a comparison metric (e.g., cross-correlation) to determine the best possible location of the template within the search image [21]. Thus, these methods assume the same tumor shape from the dynamic images. The DIR methods include B-splines [22] and demons [23; 24]. Compared to template-matching and neural network-based methods, DIR methods may perform worse for tracking a tumor with large motion, as they typically assume small motion in the sequence of dynamic images [23]. More recently, neural network-based methods have been used, which include pulse-coupled neural network [16] and U-Net [25]. In contrast to the template matching methods, these methods contour the tumor within each dynamic image, detecting any real-time changes in tumor shape. Also, they have shown superior performance compared to the DIR with B-splines, which generally require only one reference image [18]. With these advantages of neural networks, this study used an in-house U-Net-based autocontouring algorithm. Once an autocontouring algorithm is developed, proper assessment of the algorithm must follow.