## 1 Introduction  
Since 2017, magnetic-resonance-guided radiotherapy (MRgRT) has been enabled thanks to a dedicated linear accelerator (Linac) with embedded MR imaging, called MR-Linac [1]. This type of RT device offers alternative image quality in comparison to traditional X-ray-based imaging. In this specific workflow, MR images for treatment planning and delivery must be acquired with the same MR-Linac, and then by extension, the MRI for planning cannot be performed on another classical MRI system. In particular, due to the non-radiative nature of MR imaging, continuous imaging can be performed without any limitation during treatment delivery in order to verify the tumor position. This option is particularly relevant for lung tumors that are often subject to breathing movements. MRgRT for lung tumors has been performed since 2019 at the Centre Georges-Francois Leclerc (CGFL) with the MRIdian (Viewray Inc., Oakwood Village, OH, USA) 0.35 T MR-Linac representing around 15% of the patients treated on this system [2]. The success of this treatment and the decrease in toxicity strongly rely on the exact definition of the different organs at risk (OARs). Indeed, the delineation of the target volume and the OARs on a reference set of MR images is required for planning and is usually performed manually by radiation oncologists. Nevertheless, manual segmentation is subject to inter- or intra-observer variability, particularly according to the level of expertise. Moreover, it represents a very time-consuming step in the RT workflow, requiring several dozens of minutes depending on the localization. Several studies have been proposed for the segmentation of various anatomical regions from MR images. [PERSON] et al. [3] introduced a method based on the generative adversarial network (GAN) with a shape constraint (SC-GAN) developed for fully automated head and neck OARs segmentation on a low-field MRI. Their proposed method underwent testing on 25 0.35 T MR images obtained from a dedicated MR-guided radiotherapy system. The OARs comprised the brainstem, the optical chiasm, the larynx, the mandible, and the pharynx. Additionally, they validated their method on other CT scans for different organs. For the same anatomical region (head and neck), [PERSON] et al. [4] proposed a head and neck multi-organ automatic segmentation on CT images. Nineteen OARs were targeted and the MR scans were acquired on a 1.5 T scanner (T1-weighted images). They achieved better results for the segmentation of the mandible, parotid, optic nerve, and chiasm compared to those obtained in the study performed by [PERSON] et al. [3]. [PERSON] et al. [5] developed an automated deep-learning-based technique for the segmentation of abdominal organs based on a two-dimensional U-net and a densely connected network structure. The images were acquired on a 3T system. As for the thoracic region, numerous approaches have been proposed for the automatic segmentation of thoracic organs on CT images, including atlas-based methods, level-set methods, and morphological methods [6; 7; 8]. The majority of existing studies concerning the segmentation of thoracic organs have primarily concentrated on CT scans [9; 10; 11; 12]. While there are several publicly available CT datasets that are widely used for multiorgan segmentation in the thoracic region, such as the MSD (Medical Segmentation Decathlon) lung and heart dataset [13] and the SegTHOR (Thoracic Organs at Risk) dataset [14], there is a lack of publicly available MRI datasets that specifically address the segmentation of thoracic organs. This disparity in data availability underscores the need to explore innovative segmentation methods. Traditionally, medical image segmentation has been explored using various \"statistical\" and \"prior-knowledge-based\" methods such as thresholding, region growing, and atlas-based segmentation. However, these methods exhibit limitations, such as sensitivity to parameter settings, poor adaptability to diverse data, manual intervention requirements, and difficulty with complex structures.