## 4 Discussion
As treatment is replanned in the case of large anatomical changes, HPO will also be performed again in this case using updated patient images. One limitation of this study is that the patients were imaged on a 3 T MRI system although a linac-MR system, which typically operates at a lower field strength (B\({}_{0}\)) [17; 18; 19; 20; 69]. Hence, the quality of the images obtained with linac-MR will be different, which may result in different contouring performance of the algorithm. The ability to distinguish a tumor from tissue background during nifteRT largely depends on contrast-to-noise ratio (CNR) between the two, which is known to be affected by B\({}_{0}\)[70]. While there is a complex relationship between CNR and B\({}_{0}\), studies have shown better image contrast using a lower B\({}_{0}\) (e.g., 0.55 T) for sites including the liver and lung, provided that the MRI system is equipped with state-of-the-art hardware [70; 71]. This suggests that the contouring performance of our algorithm on the images acquired by a linac-MR system may be comparable to that presented in this study. In addition, other factors such as the MRI sequence, scan parameters, and presence of image artifacts can all affect image quality and the contouring performance of the algorithm. However, despite the pronounced artifacts shown in the P22 and P24 images in Figure 5, the algorithm was able to autoconutur with reasonable accuracy. This suggests solid performance of the algorithm even in the presence of artifacts. Another limitation of this study is that there can be through-plane tumor motion that is not captured by the 2D MR images. During the 2D imaging of a mobile tumor on the linac-MR, the imaging plane will be oriented to include the two major axes of tumor motion. This will allow the detection of any in-plane changes in tumor shape and position. For handling any potential through-plane motion, one possible solution is adjusting the slice thickness of the imaging plane to ensure that the tumor remains in the imaging plane. This issue can also be addressed by acquiring a 3D image volume, which can be achieved by using accelerated imaging techniques such as parallel imaging, compressed sensing, and neural network reconstructions [72; 73; 74]. Moreover, studies have shown that 2D multi-slice or orthogonal imaging can capture the 3D motion of mobile tumors [75; 76; 77]. Although the developed algorithm achieved the goal of a mean DC of \(\geq\)0.9 for most patient cases, there is still room for improvement as CD and HD for some of those cases were large; for example, maximum CD was 19.85 mm in P39 and maximum HD was 24.41 mm in P22. These can be further improved in various ways. For optimizations by CMA-ES, a fixed population size of 10 was used in this work. However, due to the nature of CMA-ES, using a varying population size over the course of the search may improve the optimizations. This is because using a small population size often leads to faster convergence but may lead to solutions being stuck in local minima by failing to explore various minima with diverse solutions, whereas using a large population helps to prevent being stuck in local minima for a more global search, but the search may be too slow until convergence. Also, optimizing more hyperparameters such as activation function and sampling distribution for initial weights could result in better contouring accuracy. Furthermore, contouring performance can be improved by adopting a different model architecture. U-Net uses concatenations to combine shallow, low-level, fine-grained feature maps from the encoder with deep, semantic, coarse-grained feature maps from the decoder. The fusion of semantically dissimilar feature maps using plain concatenations in U-Net can make the learning task difficult for the network. This semantic gap between the encoder and decoder can be reduced by employing variants of U-Net, such as U-Net++ [78] or U-Net3+ [79], which use nested, dense concatenations and redesigned concatenations to combine multi-scale feature maps, respectively.