## 5 Discussion  
This paper presents a detailed review of the current major frameworks and techniques within the field of medical image segmentation, critically analyzing the limitations of these approaches in handling complex medical images. To address these challenges, we propose an innovative network called MSLUnet. This method efficiently captures semantic features and global contextual information at various scales through symmetric multi-scale feature extraction modules and depth-separable large-core convolutional blocks. Additionally, we introduce a lightweight three-branch attention mechanism that enables simultaneous processing of information across spatial and channel dimensions. This mechanism effectively reduces semantic discrepancies between the encoder and decoder, significantly minimizing information loss. Our MSLUnet model has been validated across multiple publicly available datasets, demonstrating superior performance compared to other state-of-the-art methods.  
In a study examining accelerated cardiac T1 mapping, various deep learning architectures were implemented within MyoMapNet [46] to enhance the estimation of T1 from a minimal set of T1-weighted images. MyoMapNet demonstrated that both fully connected and U-Net architectures could achieve remarkable accuracy and quality in T1 estimation. Specifically, U-Net outperformed in terms of precision, showcasing the potential of encoder-decoder networks with skip connections for medical imaging tasks. This aligns with our findings in MSLUnet, where the incorporation of multi-scale features and attention mechanisms similarly boosted segmentation accuracy and detail resolution. Building upon the strengths observed in U-Net from the MyoMapNet study, our MSLUnet incorporates enhanced multi-scale feature extraction modules and a lightweight attention mechanism to further refine the precision and adaptability of segmentation. This approach not only addresses the challenges seen in conventional architectures but also advances the state of medical image segmentation toward achieving higher fidelity.  
Despite the notable enhancements in lesion segmentation, the model still exhibits some limitations, particularly in capturing deep semantic details and handling irregularly shaped lesion edges. We acknowledge the potential for further enhancement in the model's performance. In practical applications, especially when deployed on mobile medical devices, we continue to encounter challenges related to processing power, memory, and energy efficiency. Future work will focus on enhancing the precision of edge segmentation by further optimizing the network architecture and exploring adaptive optimization strategies to improve the model's adaptability and reliability across different hardware platforms. Research from the literature [18] on multi-task learning indicates that such strategies can facilitate the sharing of underlying feature representations, allowing different tasks to utilize the same convolutional layers. This not only reduces the number of model parameters and computational resource demands but also helps the model capture universal features applicable across multiple tasks, thereby enhancing learning efficiency and generalization capability. We plan to integrate multi-task learning strategies into our future work to refine the model architecture, enabling the execution of multiple segmentation tasks while reducing parameters and simplifying network complexity. This approach will improve the model's generality and flexibility, allowing it to effectively minimize its complexity and computational demands while maintaining high segmentation accuracy. Furthermore, considering that current experimental validations primarily focus on a limited number of publicly available datasets, we aim to explore more underutilized datasets and validate their performance across a broader range of standard benchmark tests. This strategy will allow us to comprehensively assess the robustness and practicality of MSLUnet.