## 4 Discussion  
This study aims to extract meniscal ROIs from knee MRI images and improve segmentation results using ensemble-based approaches. We trained segmentation models based on state-of-the-art YOLO versions. We optimized the results using innovative ensemble methods such as pixel-based voting, weighted multiple voting, and dynamic weighted multiple voting with grid search. Meniscal volume is known to be less than 0.1% of the entire knee joint MRI scan [35] and, in the 2D images used in this study, meniscal ROI areas represent less than 1.5% (Figure 7). The manual segmentation of such small tissues has limited applicability due to its high risk of error and time-consuming nature. Therefore, deep learning-based approaches offer practical solutions to overcome these challenges. Furthermore, this is the first known study in which segmentation models based on the YOLO series are integrated with innovative ensemble methods (e.g., grid-search-based dynamic weighting) to improve performance. The YOLO series has an innovative architecture capable of fast and highly accurate object detection and stands out for its speed and accuracy advantages in biomedical imaging and segmentation tasks [36]. Ensemble methods combine the strengths of these models to improve their results further. Pixel-based voting, weighted multiple voting, and dynamic weighted voting with grid search combine the outputs from different models to provide more accurate and consistent results. These methods are powerful solutions for challenging segmentation tasks, especially in complex biomedical images. Incomplete segmentation results are reduced due to inhomogeneous intensity levels in the detection of meniscus ROI areas on MRI images. The ground truth comparison of the proposed method (Method 3) and the mask obtained using Model 1, which shows the best result in training, is given in Figure 8. As can be seen in Figure 8, one of the main reasons for incomplete segmentation is the small size of the meniscus ROI areas and the inhomogeneous intensity levels in the MRI images. Meniscal tissue shows similar intensities to other soft tissues on MRI images, resulting in limited contrast, especially in the posterior horn regions of the lateral and medial meniscus. This may lead to incomplete or incorrect boundaries in the segmentation masks. In addition, artifacts and low signal intensity in some regions of the images also contribute to incomplete segmentation. In Figure 8, when the masks produced by Model 1 are compared with ground truth, such missing boundaries are more evident. However, the proposed Method 3 overcomes some of these shortcomings through dynamic weighting and obtains results that are more consistent with the ground truth. This shows the success of Method 3, especially in balancing intensity differences and reducing incomplete segmentation. Figure 7: An example of knee MRI images that represent the sizes of meniscal ROI areas. (**a**) Medial meniscus with meniscal tear in the internal set. (**b**) Healthy lateral meniscus in the external set. Tables 3 and 4 show that Model 1 performs the best among the trained models. This can be explained by the contribution of the increase in the number of parameters in the YOLO series to the performance improvement [37]. The parameter numbers (Parameters/M) of the models are ~71.70 M (Model 1), ~5.09 M (Model 2), ~27.62 M (Model 3), ~59.68 M (Model 4), and ~2.83 M (Model 5). Although Model 1 outperformed the other models with its high number of parameters, qualitative evaluations also revealed cases where other models incorrectly estimated ROI areas. The main goal of ensemble methods is to improve segmentation accuracy by combining the strengths of each YOLO model and minimizing the inconsistencies between the outputs obtained from different models. In this study, the combination of masks from five different YOLO models demonstrates the contribution of this diversity to segmentation performance.