## 2 Related Work  
An extensive branch of the literature on DL-based brain tumor segmentation is related to the BraTS Challenge dataset [12], which is a standard benchmark for assessing automatic brain tumor segmentation techniques. Thanks to its high-quality annotations, standardized format, and broad availability, the BraTS dataset has been widely adopted in various studies aimed at developing and evaluating DL-based brain tumor segmentation methods. Among CNN-based models, [PERSON] et al. [19] proposed DeepMedic, one of the top-performing algorithms in the BraTS 2016 competition. DeepMedic features a parallel path structure with two resolution levels, enabling simultaneous capture of both the local details and broader global context of the image. In subsequent BraTS challenges, models based on U-Net [8] gained significant prominence. Specifically, in the BraTS 2017 challenge, 16 models among those presented drew inspiration from U-Net, while in BraTS 2018 [20] over 35 models combined U-Net with other CNN architectures. Notably, [PERSON] et al. [21] tested an ensemble approach on the BraTS 2018 dataset that incorporated three advanced models: an anisotropic cascade CNN, a previously described 3D U-Net architecture [22], and a modified 3D version of the standard U-Net. By employing a majority voting mechanism to combine these independently trained networks, the ensemble approach achieved remarkable segmentation accuracy. Specifically, this strategy demonstrated improved segmentation for WT and ET, with Dice scores of 0.91 and 0.81, respectively, while the anisotropic cascade CNN excelled in segmenting TC, with a Dice score of 0.85. [PERSON] et al. implemented a 3D U-Net model incorporating deep supervision techniques, with four downsampling stages located in the encoder, symmetrically replicated in the decoder. This approach ranked among the top 10 in the BraTS 2020 segmentation challenge, with Dice scores of 0.79 for ET, 0.89 for WT, and 0.84 for TC [23]. Another innovative approach was the MS-SegNet architecture by [PERSON] et al., which was tested on the BraTS 2020 and 2021 datasets. This model utilizes convolutional filters of varying sizes to extract both local and global features across multiple MRI modalities, thereby expanding the receptive field and enhancing robustness. Its encoder employs a multi-scale feature extraction block to capture both low-level visual details and high-level semantic features, leading to Dice scores for ET, WT, and TC of 0.81, 0.91, and 0.83 on BraTS 2020 and 0.86, 0.92, and 0.84 on BraTS 2021, respectively [24]. Further exploration was conducted by [PERSON] et al., who employed DeepLabv3+ integrated with different backbone networks such as ResNet18 and MobileNetv2 along with customized loss functions to improve segmentation accuracy for various cancer regions in the BraTS 2020 dataset, achieving a very high average Dice score of 0.92 [25]. In 2020, [PERSON] et al. [26] ranked first in the challenge by applying the nnU-Net framework [10] to the segmentation task and integrating BraTS-specific optimizations such as batch Dice loss, extensive data augmentation, and targeted postprocessing to manage small ET regions. This approach achieved Dice scores of 0.89 for WT, 0.85 for TC, and 0.82 for ET. Starting from BraTS 2021, the introduction of the attention mechanism employed by transformers has provided new hybrid methods that account for previous limitations. [PERSON] et al. [27] presented an extension of the nnU-Net model based on a deeper architecture along with the introduction of group normalization and axial attention mechanisms within the decoder. This approach led to a substantial performance improvement, with Dice scores of 0.93 for WT, 0.88 for TC, and 0.85 for ET, although some of these gains were likely driven by the expanded dataset available that year, which provided more training examples.