## 4 Discussion  
Up to now, the most common screening tool for radiation therapy has been CT imaging [47]. Through the rapid advancements of the MR-Linac radiation therapy systems, MR imaging can screen anatomical structures with superior soft-tissue contrast compared to CT scans. In this context, an automatic multiorgan segmentation tool for the pelvic region dedicated to a 0.35 T MR-Linac system was proposed in our study. A modified version of the U-Net [33] combined with AGs and residual blocks using a 2.5D training strategy was used, utilizing multi-slice input and deep connections. The use of a 2.5D strategy allows for the obtainment of pseudo-3D information of the patient's volume without the excessive computational complexity of 3D-based methods [48; 49; 50]. The 3D modeling allows the physicians to access a global view of the region of interest in question, with any kind of view or angle. It has been used in a lot of medical applications [51; 52; 53], and this display could complement the advanced imaging capabilities of the MR-Linac device. [PERSON] et al. [32] reported shorter computational times in both strategies than in 3D without significant performance drawbacks. Clearly, the 3D training strategy is not always the optimal solution for robust results [54]. For the ResAttU-Net, we conducted experiments on two training strategies as the results of the proposed approach exhibited higher DSC than the U-Net and Attention U-Net in the 2D strategy. Following the optimal network from 2D, the 2.5D ResAttU-Net strategy surpassed the 2D strategy. More specifically, per organ comparison showed a significant increase in the accuracy for the segmentation of the bladder and prostate despite a slight decrease for the rectum. The results show the effectiveness of the 2.5D method, exploiting pseudo-3D information through adjacent segments with insignificant time savings. The segmentation of the bladder obtained the best results among all the contours, having mean DSC: 0.92 \(\pm\) 0.09, HD: 6.13 \(\pm\) 5.46 mm. Both femoral heads obtained stable results between both strategies without affecting the overall accuracy. On the contrary, the segmentation of the prostate showed lower performance with the 2.5D strategy. The observer variability in the prostate segmentation was subject-specific, with target shape and disease staging. This issue is particularly noticeable in cases where patients have intermediate-to-high risk. Similarly, the delineated prostate contains the base of seminal vesicles that can be delineated differently [55]. Additionally, the prostate base is in continuity with seminal vesicles and bladder, which may cause unexpected artifacts throughout the inference. There are not many studies related to multiorgan segmentation in the male pelvis dedicated to 0.35 T MRI. Specifically, the related works are MRI-based but with different sequence types or different magnetic fields. Our work demonstrated better or similar performance compared with the existing studies on T2-weighted images and on the multisequence approach (T1-weighted, T2-weighted, and enhanced Dixon T1-weighted images) [56]. To our knowledge, there is no related work based on the SSFP sequence for multiorgan segmentation in the pelvic region. A comparison shows that our work has comparable results regarding the related approaches (Table 4). However, the prostate remains lower compared to the results of [PERSON] et al. The work of [PERSON] et al. has no available prostate results since the work was based on cervical cancer. It is worth noting that it is not possible for the model to work in the same manner with MR imaging systems that have different technical specifications and magnetic field strength due to the differences in image resolution, contrast, and noise levels. Therefore, it is important to train deep learning models on MRIs of the same magnetic strength as those that will be used in clinical practice to ensure optimal performance.