# Unsupervised Image Segmentation on 2D Echocardiogram
## 4 Results
### Quantitative Analysis
#### 4.1.2 Ejection Fraction  
To evaluate the accuracy of the predicted EF values, we calculated the MAE, MAPE, and RMSE between the predicted and ground truth EF values. These metrics were selected to provide a comprehensive view of the prediction accuracy, with each metric emphasizing different aspects of error (absolute, percentage, and squared errors). Table 2 summarizes the comparison of EF prediction accuracy between our method and the EchoNet Dynamic model. In the evaluation of EF prediction (Table 2), our method demonstrated competitive performance, with an MAE of 13.7057 and an RMSE of 16.9375, compared to the EchoNet Dynamic model, which reported an MAE of 13.1116 and an RMSE of 18.5931. Although EchoNet achieved a slightly lower MAE, indicating marginally better overall accuracy in EF prediction, our model exhibited a lower RMSE, suggesting that it produced fewer large errors in individual cases. The MAPE also reflects this balance, where our method had an error of 28.01%, while EchoNet achieved 25.38%. This comparison suggests \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Method** & **Value** & **95\% CI** \\ \hline Dice Coefficient & Our Method (Unsupervised) & 0.8225 & [0.8098, 0.8353] \\  & EchoNet Dynamic (Supervised) & 0.9241 & [0.9207, 0.9275] \\ \hline IoU & Our Method (Unsupervised) & 0.7071 & [0.6928, 0.7213] \\  & EchoNet Dynamic (Supervised) & 0.8600 & [0.8542, 0.8657] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of segmentation accuracy using Dice and IoU between our method (Unsupervised) and EchoNet Dynamic (Supervised), including 95% confidence intervals (CI). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Our Method vs. GT** & **EchoNet D vs. GT** \\ \hline MAE & 13.7057 & 13.1116 \\ MAPE & 28.01\% & 25.38\% \\ RMSE & 16.9375 & 18.5931 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Ejection Fraction (EF) calculation accuracy using MAE, MAPE, and RMSE between our method and EchoNet Dynamic. Figure 7: Comparison of segmentation results for Systolic (**top row**) and Diastolic (**bottom row**) frames: (**a**) original frame, (**b**) ground truth mask, (**c**) our model mask, (**d**) EchoNet mask. that while both models are capable of accurately predicting EF values, our method may perform more consistently across different cases, even if it produces slightly larger errors in some instances. Given that EF is a critical clinical metric for assessing cardiac function, the small differences in these results highlight the robustness and potential clinical relevance of our approach. One of the primary sources of error in our model's segmentation output is the overestimation of the left ventricle's area as the model approaches the edges of the heart wall. This is a consequence of the pixel-level precision of our unsupervised segmentation method, which can result in slight overshooting of boundaries, particularly in regions where the heart wall is less distinct. Additionally, the natural movement of the heart during the cardiac cycle, including the motion of the valves, is also captured in our segmentation output. This differs from methods that rely on annotated points to trace an elliptical shape, which typically exclude such anatomical features. As a result, our model's segmentation of the left ventricle sometimes includes the valves and slight imperfections in the heart wall, leading to a more irregular shape compared to the smoother, idealized boundary defined by annotations. Furthermore, shades of the heart wall or the back wall of the heart, visible within the chamber, introduce additional variance in the segmentation output. These shaded areas can cause the model to misinterpret noise as part of the anatomical structure, leading to minor inaccuracies. Such issues are common in echocardiographic image analysis due to the inherent variability in ultrasound image quality. Despite these sources of variance, the errors in segmentation are well within the typical range of measurement variation observed between clinicians.