## 3 Results
### Image Segmentation Assessment
Figure 10 illustrates a qualitative segmentation output that compared CqSL and U-Net results with increasing proportion of unlabeled training data. For simplicity, we have shown two of our best performing models. As shown, when only 1% training annotation is used, U-Net completely fails to segment the cardiac structures. Under similar conditions, our model is still able to yield a high segmentation accuracy of LV, RV, and LV-Myccardium. When the amount of labeled data increases from 1% to 10%, the U-Net model still performs poorly, especially for RV segmentation. On the other hand, although the performance of our model improves significantly when utilizing more than 30% annotated data, its performance with even 1% labeled data is still satisfactory, comparable to that of semi-supervised models, and superior to U-Net's performance under similar conditions. We assessed the performance of our proposed CqSL cardiac image segmentation method against the segmentation results yielded by the well-established, fully supervised U-Net architecture [55] in light of its effectiveness across various medical image segmentation applications, as well as its extensive use as a baseline method for comparison by the participants of the ACDC cardiac image segmentation challenge. Furthermore, to explore the effectiveness of each component in our model, we experiment on three different semi-supervised ablations, i.e., model **I:** only a GAN architecture; model **II:** GAN + reconstruction; and model **III:** GAN + reconstruction + disent  
Figure 9: Representative results showing the comparison across several best performing networks, including CqSL for the semantic segmentation of full cardiac image dataset from the base to apex showing of RV blood-pool, LV blood-pool, and LV-Myccardium on 20% labeled data in red, green, and yellow respectively. As shown in Figure 11, the accuracy of our C\(q\)SL models remains high when using as much as 50-90% unlabeled data, which essentially implies excellent performance with as little as as 10% annotated data. Nevertheless, both U-Net and C\(q\)SL models perform similar to each other when the amount of annotated data increases above 90%. We plot the mean accuracy for all the models in Figure 12 and confirm that under low amounts of annotated data conditions, even as low as 1%, our proposed C\(q\)SL model and all four of its semi-supervised variants (\({}^{1}\)C\(q\)SL, \({}^{2}\)C\(q\)SL, \({}^{3}\)C\(q\)SL, and \({}^{4}\)C\(q\)SL) outperform GAN, GAN+REC, as well as U-Net models for LV, RV, and LV-Myocardium. The typical segmentation contours of complete cardiac image dataset for the mid and apical slices are shown in Figure 13.  
Figure 11: Consistent improvement in segmentation accuracy by the proposed C\(q\)SL model over baseline semi-supervised (variants of our C\(q\)SL model: \({}^{1}\)C\(q\)SL, \({}^{2}\)C\(q\)SL, \({}^{3}\)C\(q\)SL, and \({}^{4}\)C\(q\)SL) and fully supervised models in varying proportions of labeled training data. Figure 10: Representative results showing the semantic segmentation of RV, LV blood-pool, and LV-Myocardium on different proportion of labeled data in red, green, and yellow, respectively. Figure 12: Evaluation on the robustness of CqSL in terms of mean accuracy over RV, LV, and LV-Myocardium segmentation tasks on varying amounts of labeled training samples. Note significant improvement in Dice score across all CqSL semi-supervised variants for as little as 1% unlabeled data. Figure 13: Representative segmentation contours of a complete cardiac cycle for the middle and apex slices showing RV and LV blood-pool, and LV-Myocardium in green, yellow, and brown, respectively, in three different view settings (axial, sagittal, and coronal).