# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 4 Experimental Results and Analysis
### Ablation Experiment  
In this section, we conduct an ablation study on the ACDC dataset to evaluate the effectiveness of different components in our proposed framework. Specifically, we analyze the contribution of HCP (hybrid copy-paste perturbation) and SGCS (semantic grouping contrastive sampling) to the overall segmentation performance. The ablation results in Table 3 indicate that both HCP and SGCS significantly contribute to performance improvement.  
The SGCS module is designed to optimize the contrastive learning process by effectively utilizing the weak perturbation branch. By sampling semantically grouped pixels, SGCS reduces variance and ensures more efficient contrastive learning, allowing the model to make better use of weak perturbations. This method enhances local semantic relationships, which improves model generalization across unlabeled data. SGCS and contrastive learning are complementary, as SGCS strengthens the sampling process, while contrastive learning further leverages these samples to enhance representation learning.  
The HCP module introduces hybrid perturbations through a novel copy-paste method, where labeled and unlabeled image regions are interchanged. This mechanism encourages the model to learn from diverse spatial contexts, significantly boosting the model's ability to generalize from limited labeled data. The addition of HCP strengthens the strongly perturbed branch by enhancing the consistency between pseudo-labels and ground truth labels, leading to improved segmentation results.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Labeled** & **Method** & **DICE\(\uparrow\)** & **HD95\(\downarrow\)** & **ASD\(\downarrow\)** \\ \hline
3\% Labeled & BCP & 88.0 & 6.17 & 1.46 \\  & ICTMSeg & 86.6 & 5.89 & 1.81 \\  & SLC-NET & 86.2 & 6.58 & 1.32 \\  & UniMatch & 86.6 & 6.78 & 1.31 \\  & ARCO & 88.3 & 5.36 & 1.23 \\  & Ours & **89.2** & **5.57** & **1.07** \\ \hline
7\% Labeled & BCP & 88.8 & 6.13 & 1.16 \\  & ICTMSeg & 88.9 & 5.41 & 1.48 \\  & SLC-NET & 89.3 & 5.51 & 1.12 \\  & UniMatch & 89.2 & 5.47 & 1.07 \\  & ARCO & 89.8 & 4.16 & 1.08 \\  & Ours & **90.6** & **3.45** & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the performance of our method (HCLM) with other state-of-the-art methods on the ACDC dataset. This table shows the mean HD95 and ASD for each segmentation category, as well as the overall mean HD95 and ASD, with bold indicating the best scores. The upward arrow (\(\uparrow\)) indicates that a higher value is better, while the downward arrow (\(\downarrow\)) indicates that a lower value is better.  
Figure 7: Box plots of DICE, HD95, and ASD for different models at 7% labeling rateOur experiments show that the combination of SGCS and HCP yields the best performance, achieving optimal segmentation accuracy. These two modules complement each other: SGCS optimizes contrastive learning, while HCP strengthens the model's capability to learn from strongly augmented data. The results shown in Figure 8 further illustrate the effectiveness of this combined approach.  
As shown in Figure 8, we provide visual comparisons of segmentation results after adding different modules to the model. In particular, #4 shows the segmentation results with both SGCS and HCP added, which yields the best performance, and #3 illustrates the results when SGCS and contrastive learning are added, while #2 shows the effects of adding only HCP. Lastly, #1 represents the baseline model without these enhancements. From these visualizations, it is clear that the addition of each module brings notable improvements in segmentation accuracy. The comparison highlights how different components contribute to refining the model's performance, with the combination of both SGCS and HCP leading to the most significant gains.