## 4 Discussion
Concerning the calculation of the volume of each organ, broadly speaking, the results are similar between the two strategies. We note, in particular, good agreement for the spinal cord that counterbalances the low DSC and which demonstrates the impact of organ size in the calculation of the DSC. In the literature, we did not identify any articles addressing the multiorgan segmentation of OARs in the thoracic region, particularly when focusing on the automatic segmentation of the left lung, right lung, heart, spinal cord, and esophagus using MR images or MR images acquired with an MR Linac. However, several studies have been conducted using CT scans for thoracic organs, utilizing either private or publicly available datasets. [PERSON] et al. [30] introduced a deep learning model utilizing DenseNet and FCN on a private CT dataset. In comparison to their approach, our heart segmentation achieved a higher performance (DSC: 0.924 vs. 0.86). While the results for the lungs are approximately similar, [PERSON] et al. achieved superior outcomes for the spinal cord and esophagus. The differences in results for the latter two organs can be attributed to the type of modality. Indeed, the esophagus is more distinctly visible in CT scans compared to MR images. [PERSON] et al. [31] proposed a segmentation procedure for thoracic organs on CT images using Dilated UNet. The authors utilized a dilation kernel to achieve the desired resolution in feature maps at any layer, applying it to the network through an integrated model during training. In comparison to their approach, our segmentation of the heart (DSC: 0.924 vs. 0.8597) and the esophagus (DSC: 0.551 vs. 0.4694) exhibited better performance. [PERSON] et al. [32] proposed an abdominal organ segmentation approach using a regional convolutional neural network. This approach uses a localization algorithm for finding the region of interest. As a second step, a convolutional neural network is applied performing voxelwise classification. These authors also investigated esophagus segmentation, and there was very similar segmentation performance (DSC: 0.585 vs. 0.588). [PERSON] et al. [33] introduced a deep learning network named the cascaded SE-ResUnet, designed for the automatic segmentation of thoracic organs. Their approach involves a two-step process: initially a coarse segmentation network is used to identify the regions of interest, followed by a fine segmentation network to refine the results organ-by-organ. The final segmentation results are obtained through ensembling different configured models. In comparison to our approach, these authors demonstrated superior performance across all organs by employing a complex network and a weighted loss function tailored for 3D images. The differences can also be explained by the use of another image acquisition technique. There are several limitations and potential avenues for improvement that may be identified for our approach. Notably, we could enhance the complexity of our network by implementing a coarse-to-fine architecture, combining multiple models. Additionally, the current study's patient sample size was relatively small, and increasing it could provide more robust insights. To enhance the evaluation step, cross-validation techniques could be employed to provide a more realistic assessment of our model's performance. Exploring alternative methodologies, such as a 3D strategy, could also be beneficial. Moreover, expanding the scope to include additional thoracic organs could further enhance the comprehensiveness of our segmentation approach. Furthermore, exploring higher field strengths in the MRI could increase the signal-to-noise ratio (SNR). This enhancement is anticipated to refine the segmentation results by amplifying the clarity of the anatomical structures and reducing the background noise. Investigating the impact of increased SNR on the segmentation accuracy could provide valuable insights for optimizing the MRI imaging quality in future research endeavors.