## 1 Introduction
For segmentation, a fully convolutional network (FCN) [25] was proposed to leverage the computational limitation by implementing symmetrical encoding and decoding paths to learn low- and high-level features from the input image. Later, [PERSON] et al. [26] proposed a successful encoder-decoder architecture for medical image segmentation called U-Net. Nowadays, a wide variety of implementations rely on the U-Net architecture [27; 28; 29; 30]. Another aspect of neural network models for medical image analysis is the training strategy 2D, 2.5D, or 3D. Each strategy learns features from the training data in a different manner. The most common strategy is 2D, in which a single image (or slice) is passed as an input. Similarly, 2.5D considers stacks of adjacent slices as input, collecting pseudo-3D information using 2D operations [31]. The 3D training strategy is significantly more demanding than the other strategies since it performs 3D operations. Indeed, the 2.5D strategy is regarded as computationally efficient since the memory requirements are reduced [32]. Based on the MR modality, there is considerable literature on semantic segmentation. [PERSON] et al. [33] proposed a modified 2D version of Attention U-Net [26; 30; 34] with an attention-guided decoder for semantic segmentation of brain tumors for MR-only treatment planning. [PERSON] et al. [35] proposed densely connected blocks instead of plain convolutional blocks [36] in a U-Net-based architecture for the automatic segmentation of prostate parts. [PERSON] et al. [37] developed a multiorgan automatic segmentation model for prostate radiotherapy using transfer learning with the fully convolutional network (FCN) architecture [38]. All the proposed architectures used a combination of Dice and cross-entropy loss functions. In the 2.5D strategy, [PERSON] et al. [39] implemented a deep FCN based on SegNet for prostate cancer detection and segmentation using T2-weighted MR images. The input with their method was a single slice replicated into three channels. Similarly, [PERSON] et al. [40] proposed a modified version of Fuse U-Net with an attention mechanism to accurately segment multiple OARs (femoral heads, bladder, rectum, and anal canal) through multi-sequence MR images to facilitate MR-only treatment planning preparation for prostate and cervical cancer. Nevertheless, no study has been found focusing on 0.35 T MR Linac images. In this context, we intend to develop a deep-learning-based automated multiorgan segmentation approach dedicated to the images from a 0.35 T MR-Linac for the pelvic area. A challenge of this study is the evaluation of the 2.5D strategy to mitigate the lack of contextual information in 2D-based networks. State-of-the-art U-Net variations were configured and compared with a 2D training method for multi-class segmentation in the male pelvis. Then, the best-performing model was trained and tested with 2.5D training. The organs of interest are the pelvic OARs: femoral heads, bladder, and rectum. The prostate was considered as the target volume. MRI studies were acquired from the same machine (0.35 T MRIdian, ViewRay Inc., Oakwood, OH, USA) at two institutions to create a large enough dataset. After evaluating deep learning architectures, an automated tool was developed for clinical use. This work should reduce the radiation oncologists' workload and inter-operator variability and consequently reinforce the use of MR images for treatment planning. To our knowledge, it is the first time that the development of a deep-learning-based automatic segmentation tool has been performed on 0.35 T MR-Linac images.