## 1 Introduction
Recently, deep learning (DL) methodologies have emerged as state-of-the-art approaches in medical imaging, offering enhanced performance, flexibility, and automation using learning features directly from the data and capturing contextual information. Notably, the UNet architecture has gained significant recognition for its effectiveness in semantic segmentation tasks [15]. Researchers have extended the original UNet architecture to improve its performance. For instance, the Residual Attention UNet [16] incorporates residual attention modules to enhance the model's capability to focus on relevant regions while suppressing noise and irrelevant features. TransUNet [17] integrates transformer-based self-attention mechanisms into the UNet architecture, allowing the model to capture long-range dependencies and spatial relationships effectively. One notable encoder architecture is EfficientNet [18], which has demonstrated remarkable performance in various computer vision tasks. EfficientNet is known for its efficient and scalable design, achieving excellent results with fewer parameters [19]. The combination of UNet's skip connections and EfficientNet's feature extraction capabilities enables the model to capture both local and global contextual information, leading to more accurate and robust segmentation. In the context of multiorgan segmentation, the 2D multi-class strategies independently segment 2D slices, and have proven effective in various medical imaging applications. Studies of thoracic CT scans consistently favor 2D approaches, as demonstrated by [PERSON] et al. [20]. Their work highlights 2D's superiority over 3D, emphasizing its precision in multiorgan segmentation. However, 2D multi-class strategies are susceptible to losing 3D spatial context, thereby influencingthe volumetric interrelationships. They may encounter difficulties when confronted with intricate anatomical structures that extend across multiple slices and may face challenges in the segmentation of irregularly shaped or variable organs. To overcome these limitations, a 2.5D strategy is proposed [21], which combines contextual awareness with 2D efficiency. It integrates neighboring slice data, compensating for 3D context loss in 2D strategies. This improved context enhances precision, especially with complex or irregular organs. The main objective of this work is to facilitate treatment planning by reducing organ contouring time, taking advantage of the accuracy of DL-based techniques. Our proposed method for segmenting OARs in the thoracic region involves the use of MR-Linac-acquired images. Our approach is based on an internal dataset and is driven by clinical requirements. The primary focus is placed on five critical organs: the heart, the right lung, the left lung, the spinal cord, and the esophagus. Essential preprocessing steps are applied to prepare the dataset for training, employing various techniques. Model selection and the choice of associated loss functions are carefully considered, prioritizing performance evaluation through five specific metrics: the Dice similarity coefficient (DSC) [22], intersection over union (IoU) [23], the Hausdorff distance (HD) [24], results from Bland-Altman plots concerning the volumes of the organs [25], and correlation studies, also concerning the volumes of the organs [26], that assess organ volume relationships. Our proposed method aims to contribute to the field of multiorgan segmentation in the thoracic region using MR images. The key contributions can be summarized as follows:  
* A preprocessing approach is presented for MRI datasets, utilizing YOLO for region-of-interest detection, then removal of unwanted regions, followed by adaptive histogram equalization and thresholding, to enhance dataset quality and completeness. * A novel approach for multiorgan segmentation in the thoracic region of MR images is proposed based on the EfficientNet as an encoder for UNet with a 2.5D strategy in the context of a 0.35 T MR-Linac radiation therapy system.