# MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation
## 5 Evaluation Results and Discussion
### Comparison with Other Segmentation Models  
We evaluated the performance of the proposed model against three segmentation models, such as U-Net [47], ResDUnet [9], Attention U-Net [14], and U-Net3+ [6]. The ResDUnet model proposed by [PERSON] et al. [9] was trained on the same dataset used here (LV dataset). To keep the number of parameters compared to our model, we implemented all models with a five blocks along the encoder and decoder. Experimental results were performed using two different classes of medical images, echocardiographic images for the left ventricle segmentation and CT images for lung segmentation. Table 1 shows the number of trainable parameters, the training time, and the segmentation performance of our proposed model (on the test set) in comparison with U-Net, ResDUnet, Attention U-Net, and U-Net3+. The results were demonstrated in percentage ratio for better readability. If we look at the DSC measure for the lung CT images, it can be observed that our model outperformed the basic U-Net, ResDUnet, Attention U-Net, and U-Net3+ model by 4.1%, 2.5%, 1.8%, and 0.4%, respectively. For the LV images, our model has outperformed the basic U-Net model, ResDUnet, Attention U-Net, and U-Net3++ model by 2.8%, 1.6%, 1.1%, and 0.6%, respectively. In comparison with Attention U-Net and U-Net3+, our model has slightly increased the performance but significantly reduced the number of trainable parameters and training time. Figure 7 illustrates a qualitative comparison between the proposed model and the other segmentation models. For the lung CT images, the U-Net model tends to under-segment the lung region (Figure 7a) or provide too many false positives (Figure 7b). For the same two images, Attention U-Net is observed to slightly outperform ResDUnet (Figure 7a); however, it partitioned the connected lung region into two parts (Figure 7b). In comparison with U-Net3+, our model has provided comparable performance. For both lung images, the proposed model shows the best performance with no false positives and accurate delineation, especially at the difficult deformed parts of the lung region (i.e., near the Trachea). For LV images, seen in Figure 7c,d, the three models, U-Net, ResDUnet, and attention U-Net show comparable delineation. However, for both frames, it can be observed that our model has significantly outperformed the basic U-Net model, providing a more uniform and accurate delineation, and has also improved the delineation in comparison with Attention U-Net and ResDUnet, especially seen in the more challenging frame: the end-systolic frame (Figure 7d). U-Net3+ and our model have provided comparable delineation; however, our model has also outperformed in the delineation of the end-systolic frame: the upper boundary of the left ventricle (i.e., near the apex). Generally, the outperformance of our model in comparison with ResDUnet could be explained by the multi-scale context aggregation induced by utilizing different dilation rates in the HHDC at each layer. In contrast, ResDUnet focused on enlarging the receptive field using a fixed dilation rate at each layer. Using one single-scale context may not represent the hierarchical dependencies between an object and its surrounding and might be insufficient in contributing all semantic information. In addition, the improvement over Attention U-Net could be explained by integrating the multi-scale spatial attention module, which enhanced representing the features at different locations. Moreover, Attention U-Net emphasizes spatial information from shallow encoder features. In contrast, our model incorporates the channel attention module with both encoder and decoder features to increase the contextual information of the low-level encoder features. In addition, despite the comparable performance with U-Net3+, still the credit goes to the proposed model in reducing the model complexity.