# LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation
## 4 Experimental Results and Analysis
### Ablation Experiments  
To validate the efficacy of the proposed LAAVOS method for the medaka heart segmentation task, we conducted ablation experiments to evaluate the contributions of its various enhancements. These experiments were designed to comprehensively assess the impact of each enhancement on overall performance and investigate their practical implications across diverse tasks. To ensure fairness and consistency, all comparative experiments were performed under identical experimental conditions and parameter settings.  
First, we evaluated the performance of the original DeAOT model without AFPViS, using different encoders to investigate the impact of LA-ResNet on segmentation accuracy and model complexity. We conducted a comparative analysis across several mainstream backbone architectures, including MobileNetV2, MobileNetV3, ResNet-50/102/152, and the SwinB Transformer [22; 23]. As summarized in Table 2, LA-ResNet achieved a Jaccard index of 90.25% and an F-score of 94.31%, resulting in a combined J&F metric of 92.28%, outperforming the baseline ResNet-50 by 2.27 percentage points. Despite its significant performance gain, LA-ResNet maintained a lightweight parameter count of 26.3 M, representing only 29.9% of the SwinB encoder's 88 M parameters, while delivering comparable accuracy (a J&F difference of merely 0.16%). Compared to deeper ResNet variants such  
Figure 8: Qualitative comparison of LAAVOS with other state-of-the-art segmentation methods. The first row presents the original frames from four video samples, and the second row shows the corresponding ground truth masks. Each subsequent row illustrates the predicted segmentation results from different models. In the visualizations, red regions denote the ground truth masks, blue regions indicate the predicted masks, and green regions represent the areas of overlap between ground truth and prediction.  
as ResNet-102 (44.5 M) and ResNet-152 (60.2 M), LA-ResNet offered better or equivalent segmentation performance with nearly half the model size. This demonstrates LA-ResNet's superior ability to achieve an optimal trade-off between segmentation precision and computational efficiency.  
These results validate that the integration of local attention mechanisms in LA-ResNet substantially enhances the model's ability to extract fine-grained features of medaka ventricles, while improving robustness to image degradation factors such as blur, occlusion, and color variance. The detailed performance comparison is presented in Table 2.  
Secondly, we designed four experiments to evaluate the impact of the AFPViS decoder on the performance of the LAAVOS model, as well as the interaction between AFPViS and LA-ResNet, while keeping all other parameters consistent. The experimental results, presented in Table 3, clearly demonstrate the contribution of each improvement to the model's performance.  
In Experiment 2, the introduction of the AFPViS decoder alone resulted in an increase of 1.84% in the Jaccard coefficient and 1.79% in the F-score compared to the original model. This demonstrates that the AFPViS decoder effectively enhances the fusion capability of multi-scale features and underscores its importance in improving segmentation performance.  
In Experiment 4, when AFPViS and LA-ResNet were combined, the model's performance improved significantly: the Jaccard coefficient increased by 4.83% compared to Experiment 1, and the F-score reached 95.66%, surpassing the best performance achieved by either module alone. These results indicate that the combination of the two modules significantly enhances the model's segmentation performance, effectively addressing the challenges of the complex medaka ventricular segmentation problem.