# Unsupervised Image Segmentation on 2D Echocardiogram
## Appendix A Appendix A Appendix A.1 Quality Assessment of Selected vs. Non-Selected Videos
### Comparison with State-of-the-Art Models  
In this study, we compared our segmentation model with several state-of-the-art models, including three supervised models-DeepLabV3, Mask R-CNN, and U-Net-as well as the unsupervised W-Net. Each of these models represents a different approach to image segmentation, and the comparison serves to highlight both the strengths and limitations of our unsupervised approach in relation to these widely-used supervised and unsupervised architectures. Comparison with Supervised Models: DeepLabV3, Mask R-CNN, and U-Net are the state-of-the-art models widely used for image segmentation in various applications. They are all supervised models that require labeled datasets to learn the mapping between input images and the corresponding segmentation masks. These models have shown strong performance in a variety of segmentation tasks, especially in medical imaging where accurate boundary detection and class differentiation are critical. * DeepLabV3: Known for its ability to capture multi-scale contextual information, DeepLabV3 uses atrous convolutions and is particularly effective for semantic segmentation tasks where precise boundary detection is important. The model employs a ResNet-101 backbone and requires pixel-level annotations to achieve high-performance [26]. In this study, we used the pre-trained version of DeepLabV3 with ResNet-101 to evaluate its performance on echocardiiograms. The results show that without specific fine-tuning for gray-scale images, it cannot identify any useful information from the echocardiiograms (see Figure A6(4)). * Mask R-CNN: The Mask R-CNN approach extends Faster R-CNN by adding a segmentation branch to predict pixel-wise masks for each detected object. The model is highly effective for instance segmentation tasks, though it also depends on labeled data to generate accurate segmentation results [27]. It uses a ResNet-50 backbone with a Feature Pyramid Network (FPN) to capture multi-scale features. While Mask R-CNN is effective at object detection and segmentation, its performance on gray-scale echocardiiograms (see Figure A6(5)) is limited because it was pre-trained on RGB images and does not account for medical-specific features like heart chambers without further adaptation. * U-Net: Widely used in biomedical image segmentation, U-Net employs an encoder-decoder structure and is particularly successful in medical imaging due to its ability to capture both low- and high-level features. Like the other supervised models, it relies on annotated data for training [28]. In this study, we adopted a ResNet-34 encoder pre-trained on ImageNet. While the U-Net has demonstrated strong performance in medical imaging tasks due to its ability to capture both low- and high-level features, like the other models, it had poor segmentation accuracy for gray-scale segmentation tasks in echocardiiogram analysis (see Figure A6(6)). The supervised nature of these models allows them to achieve high accuracy when large annotated datasets are available. However, one challenge is that labeled data can be expensive and time-consuming to obtain, especially in medical imaging tasks, where expert annotations are required. In this study, pre-trained weights were used for all the supervised models to evaluate how well these state-of-the-art architectures generalize to gray-scale echocardiographic data. Using pre-trained models saves time during training, but, as seen in Figure A6, these models struggle to segment echocardiiograms without specialized objective functions or further fine-tuning. This highlights the importance of adapting both model architecture and training processes when applying general-purpose segmentation models to domain-specific medical data. Comparison with Unsupervised Models: Unsupervised models like W-Net are particularly useful in situations where labeled data are scarce or unavailable, making them an attractive choice for medical applications.