## 4 Discussion
The quantitative results of the models and the proposed method on the test set and the external set are presented in Tables 5-7. In general, the findings in Table 5 show that Method 3 performs better and is more balanced than the other methods in terms of all metrics. In particular, the high performance of Method 3 on the external set highlights the model's ability to cope with data diversity and its generalization success. The consistency provided by multiple voting methods provides a generalizable and reliable solution for clinical applications. For example, Method 3 showed consistent results in metrics such as DSC (0.8976 (\(\pm\)0.0071), 0.9004 (\(\pm\)0.0064)) and Specificity (0.9467 (\(\pm\)0.0077), 0.9200 (\(\pm\)0.0119)) in the test set and external set, indicating that this method can work with similar accuracy on different patient groups. Table 6 gives quantitative evaluation results according to menu types. Although the images containing LM are fewer than MM in the datasets, DSC and Sensitivity in the test set and external set obtained very close results. For example, the DSC for MM in the test set was 0.9006 (\(\pm\)0.0084), while in the external set, it was 0.9055 (\(\pm\)0.0069). This shows that the generalization ability is strong across different meniscus types. Table 7 shows the relationship between healthy meniscal tissue and torn meniscal tissue. The detection of ROI areas of torn meniscus was more successful than healthy meniscus in both the test and external sets. For example, the DSC and Sensitivity for a torn meniscus in the test set were 0.9008 (\(\pm\)0.0077) and 0.9443 (\(\pm\)0.0090), respectively, while these values were 0.9051 (\(\pm\)0.0061) and 0.9102 (\(\pm\)0.0140) in the external set. One of the main reasons for this difference is that there are four times more torn meniscus images than healthy meniscus images in the datasets. This imbalance of data diversity is considered an essential factor affecting segmentation accuracy. Figure 8: Comparison of segmentation masks obtained by the proposed method (Method 3) and Model 1 with ground truth. (**a**,**b**) Internal test images are healthy and torn meniscus samples, respectively. (**c**,**d**) External test images are healthy and torn meniscus samples, respectively. Although Method 3 achieved good results using ensemble methods, the high parameter count of Model 1 provided an advantage in terms of segmentation accuracy and offered the potential to reduce the need for manual segmentation significantly. However, the DSC and Sensitivity differences between the test set of Method 3 and the external set were compared to evaluate the model's overall segmentation success and generalization ability. According to the \(t\)-test analysis, the \(p\)-value for DSC is 0.364, which is greater than 0.05, indicating that the DSC differences between the test set and the external set are not statistically significant. On the other hand, the \(p\)-value for Sensitivity was calculated as 0.0000087, and since this value is less than 0.05, the difference between the two datasets is statistically significant. These results demonstrate the generalization success of Method 3 and its potential to work reliably on different datasets. This suggests that, while the method maintains overall segmentation accuracy, its ability to recognize all positive regions may vary depending on differences between datasets. DL-based methods commonly used in the literature for meniscus segmentation include U-Net [12; 13; 14; 15; 16] and Mask R-CNN [11; 17; 18]. Although the first version of YOLO was released in 2016 [38], it gained the segmentation task later in the YOLO series. After U-Net was introduced as a convolutional network for biomedical image segmentation [39], U-Net-based architectures were developed for meniscus segmentation. Some of these studies have focused on the preprocessing of meniscal tear detection [11; 14; 16; 17], while other studies have focused only on the success of segmentation [12; 15; 18; 19].