## 2 Related Work
In 2022, the top-performing model was an ensemble combining DeepSeg [28], an enhanced nnU-Net, with DeepSCAN architectures [29] and using the STAPLE algorithm [30] to fuse final predictions. This ensemble achieved Dice scores of 0.93 for WT, 0.88 for TC, and 0.88 for ET, further advancing the state-of-the-art in glioma subregion segmentation [31]. [PERSON] et al. [32] proposed an ensemble comprising nnU-Net, Swin UNETR, and the BraTS 2021 winning model, as well as integrating advanced synthetic data augmentation techniques such as GANs and registration. This approach achieved impressive Dice score results of 0.90 for WT, 0.87 for TC, and 0.85 for ET on the BraTS 2023 dataset by combining the strengths of CNNs and transformers, albeit with increased computational cost. [PERSON] et al. [33] proposed BTSwin-Unet, a 3D symmetric U-shaped segmentation network that integrates Swin Transformer modules into both the encoder and decoder pathways for volumetric brain tumor segmentation. Their model employs window-based self-attention to capture long-range dependencies, an overlapping patch embedding strategy to improve local feature representation, and a convolutional stem to stabilize training, reporting an average Dice score of 0.86 across ET, WT, and, TC subregions. Another architecture tested for brain tumor segmentation is TSEUnet by [PERSON] et al. [34]. Their model, inspired by nnU-Net, integrates a transformer module in the encoder path along with Squeeze-and-Excitation (SE) attention in the decoder to selectively enhance informative features. This strategy resulted in improved performance on the BraTS 2018 dataset, achieving Dice scores of 0.82 for ET, 0.91 for WT, and 0.87 for TC, outperforming the top-ranked method of that year by 0.62%, 0.37%, and 1.23%, respectively. Further investigating transformer-based architectures, [PERSON] et al. [35] proposed NestedFormer. This model employs a multi-encoder/single-decoder structure with two key components: Modality-Sensitive Gating (MSG) for improved skip connections at lower scales, and the Nested Modality-aware Feature Aggregation (NMaFA) module for high-level fusion, achieving Dice scores of 0.92 for WT, 0.86 for TC, and 0.80 for ET on BraTS 2020 dataset. [PERSON] et al. [18] developed SegFormer3D for medical image segmentation to overcome the limitation of high computational resources typically associated with complex transformer architectures, enabling efficient training and deployment without compromising performance. This model succeeded in reducing the computational burden and capturing multi-scale information by encoding feature maps at various resolutions of the input volume, following the hierarchical structure introduced by Pyramid Vision Transformer [36]. Moreover, the efficient self-attention mechanism and overlapping patch embedding module employed in the encoder enable both local continuity and global context modeling. For the decoder, SegFormer3D uses the all-MLP approach presented by [PERSON] et al. [37]. All of these strategies led to a significant reduction in model size and complexity, with only 4.5M parameters while achieving competitive performance in terms of Dice scores, with 0.90, 0.74, and 0.82 for WT, ET, and TC, respectively, on the BraTS 2017 dataset. Nonetheless, a common limitation across existing works is their primary focus on the delineation of composite tumor regions as defined by the BraTS challenge protocol. To the best of our knowledge, only a few works have investigated a more detailed segmentation of key brain tumor subregions [38, 39], reporting very limited results, particularly in the delineation of necrotic areas and edema. Therefore, having assessed the important clinical significance of each tumor subregion, the aim of this work is to design and validate a novel DL architecture for accurate segmentation of the ET, NCR, and ED regions while maintaining moderate computational complexity.