# LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation
## 5 Conclusions  
Currently, there is a lack of reliable datasets for medaka embryonic ventricular segmentation tasks. Additionally, the semantic segmentation methods commonly used in this field demonstrate insufficient accuracy in ventricular localization and boundary segmentation when processing medaka heart videos due to the complexity of the task. To address these issues, this study first constructed a medaka heart video dataset containing 4200 frames with pixel-level annotations, which includes videos of medaka ventral views captured \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Encoder** & **J** & **F** & **J\&F** & **Parameters** \\ \hline mobilenetv2 & 85.4571 & 88.75 & 87.10 & 3.4 M \\ mobilenetv3 & 86.51 & 89.15 & 87.83 & 5.4 M \\ Resnet-50 & 88.06 & 91.95 & 90.01 & 25.5 M \\ Resnet-102 & 88.92 & 92.63 & 90.78 & 44.5 M \\ Resnet-152 & 89.18 & 92.57 & 90.88 & 60.2 M \\ LA-resnet & 90.25 & 94.31 & 92.28 & 26.3 M \\ SwinB & 90.03 & 94.85 & 92.44 & 88 M \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison results of different encoders. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment Number** & **LA-Resnet** & **AFPViS** & **J** & **F** \\ \hline
1 & \(\times\) & \(\times\) & 88.06 & 91.95 \\
2 & \(\times\) & \(\surd\) & 89.90 & 93.74 \\
3 & \(\surd\) & \(\times\) & 90.25 & 94.31 \\
4 & \(\surd\) & \(\surd\) & **92.89** & **95.66** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ablation experiments (\(\surd\): enabled; \(\times\): disabled). under several different environmental conditions. Furthermore, this paper proposes a semi-supervised learning-based video segmentation model, LAAVOS, which significantly improves the accuracy and robustness of medaka ventricular segmentation by introducing a locally attention-enhanced LA-ResNet encoder and a progressive multi-scale feature fusion decoder, AFPViS. The experimental results indicate that the proposed LAAVOS model significantly outperforms the conventional U-Net-based image segmentation approach, achieving a notable improvement of 13.48% in the mean Intersection over Union (mIoU) metric. Moreover, in comparison with DeAOT--a state-of-the-art semi-supervised video segmentation method--LAAVOS demonstrates a competitive advantage, attaining a 4.83% enhancement in the Jaccard and F-measure (J&F) metric. Specifically, the LA-ResNet encoder, by integrating the ResNet-50 backbone with a local attention mechanism, effectively enhances the model's ability to extract ventricular features and significantly suppresses noise interference such as blurred images and organ occlusion. The AFPViS decoder, through a progressive multi-scale feature fusion mechanism, reduces semantic information degradation and loss during feature fusion in traditional Feature Pyramid Networks (FPNs), further improving the precision of segmentation. In summary, this study provides a more accurate and environmentally adaptive segmentation method for medaka heart development research, establishing a technical foundation for studying the pathological mechanisms of heart diseases and developing treatment strategies. However, this study still has the following limitations: the segmentation accuracy may decline due to blurred cardiac boundary images when blood pumping from the atria to the ventricles causes dynamic tissue deformation; and the model exhibits insufficient generalization capability for pathological features when dealing with embryonic specimens with ventricular malformations or severe lesions. Future research directions include expanding the dataset further and investigating more powerful deep learning models to enhance domain generalization and adaptation capabilities, thereby achieving performance that can accurately segment more complex cases such as malformed embryos. Meanwhile, developing more efficient and flexible tools based on deep learning models for heart development research can improve biologists' research efficiency.