## 3 Results  
### Image Segmentation Assessment  
We tested our C_q_SL model on varying proportions of labeled and unlabeled data available through the STACOM 2017 ACDC cine cardiac MRI dataset. Training and validation segmentation accuracies for three different classes (RV, LV, and LV-Myo) are shown in Figure 8 for 100 epochs. Note that the validation curves show similar trends as the training curves (Figure 8). The C_q_SL experimental results were compared against a fully supervised U-Net model trained from scratch, as reported in Tables 1-3. Furthermore, to explore the effectiveness of each component in our model, we propose three different semi-supervised ablations, i.e., model **I:** only a GAN architecture (Figure 3c); model **II:** I + reconstruction (Figure 3c,d); model **III:** II + disentangler block (Figure 3a-d), which are also reported in Tables 1-3. The detailed comparison of our model can be seen in Table 4. The segmentation performance is evaluated both qualitatively and quantitatively. As shown in Tables 1-3, our proposed model significantly improves the segmentation performance of right ventricle (RV), left ventricle blood-pool (LV), and LV-Myocardium, respectively on varying proportions of annotated data in terms of the Dice and Jaccard indices, Hausdorff distance, precision and recall rates. Our C_q_SL model achieves a high dice score (\(\pm\)std. dev.) of 75.50 \(\pm\) 10.9% for the RV, 83.21 \(\pm\) 7.1% for the LV blood-pool and 77.65 \(\pm\) 9.3% for the LV-Myocardium even if we use only 1% labeled data. Figure 9 illustrates a qualitative segmentation output that compared C_q_SL and two others semi-supervised models, i.e., model **I:** only a GAN architecture (Figure 3c); model **II:** I + reconstruction (Figure 3c,d). For simplicity, this comparison is based on 20% unlabeled training data. As demonstrated, when only 20% of the training annotation is employed, U-Net fails completely to segment the cardiac structures from base to apex, particularly \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & & & & & \\ \cline{2-5}
**Models** & **Dice \(\uparrow\)** & **Jaccard \(\uparrow\)** & **HD \(\downarrow\)** & **Prec. \(\uparrow\)** & **Rec. \(\uparrow\)** \\ \hline
**Model I:** GAN & 76.56 \(\pm\) 9.97 & 71.74 \(\pm\) 14.54 & 8.26 \(\pm\) 7.37 & 82.87 \(\pm\) 7.66 & 85.78 \(\pm\) 6.34 \\
**Model II:** GAN + REC & 77.82 \(\pm\) 9.87 & 73.10 \(\pm\) 13.92 & 8.11 \(\pm\) 6.74 & 83.84 \(\pm\) 7.12 & 87.06 \(\pm\) 5.65 \\
**Model III:** GAN + REC + DISEN- & **84.92 \(\pm\) 6.55** & **77.85 \(\pm\) 11.06** & **7.20 \(\pm\) 6.06** & **87.76 \(\pm\) 5.45** & **89.56 \(\pm\) 5.04** \\ TANGLE (C\(\sharp\)GL) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Our proposed C_q_SL model achieves 84.9% accuracy, significantly outperforming other baselines. We incrementally add each component, aiming to study their effectiveness on the final results; (model **I:** only a GAN architecture (Figure 3c); model **II:** GAN + reconstruction (Figure 3c,d); model **III:** GAN + reconstruction + disentangled block (Figure 3a–d)). \(\uparrow\) denotes higher the value better the result; \(\downarrow\) denotes lower the value better the result. Figure 8: Representative accuracy curves showing the training and validation accuracy of three different classes (RV blood-pool, LV blood-pool, and LV-Myocardium). RV segmentation. As shown in the figure, the segmentation results improve with each consecutive addition of a distinct block. The GAN-only architecture performs badly, particularly during RV segmentation, whereas the addition of a reconstruction block improves performance. Finally, adding a disentangled block to the GAN and reconstruction block yielded the greatest results. Even the least performing version of our proposed CqSL model (\({}^{4}\)CqSL) achieves an overall accuracy superior to the U-Net, GAN-only, as well as GAN+REC model, confirming that the proposed model is able to effectively learn correct features that ensure correct segmentation.