# STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
## 7 Conclusions  
Firstly, we created a video object segmentation task dataset with over 7000 annotated images, filling a significant gap and providing a robust foundation for calculating medaka ventricle parameters.  
Additionally, to address the issue of edge blurriness in videos, we introduced a spatiotemporal attention module for video object segmentation. This module enhances the performance of semi-supervised video object segmentation models.  
Secondly, addressing the issue of edge blurring in videos, we proposed a spatiotemporal attention module for VOS. Our model achieved a J&F improvement of 0.032 compared to the state-of-the-art model TBD on the DAVIS2016 validation set. On the medaka test set, it demonstrated a mean accuracy improvement of 0.392 over the traditional U-Net model and an additional 0.038 improvement compared to TBD in mean accuracy.  
Finally, we designed an automated system capable of automatically segmenting the medaka ventricle and providing relevant ventricular parameters, including heart rate (HR), stroke volume (SV), ejection fraction (EF), etc. Additionally, the system outputs dynamic segmentation result GIFs, a 3D dynamic simulation GIF of heartbeats, and an ECG GIF. This provides researchers with convenience, enabling them to visually observe the ventricular status of medaka.  
To foster academic exchange and further research, we have open-sourced all the resources used in this paper, including the dataset, model code, pre-trained weights, and the relevant code for the automated system. Interested researchers can access these resources at [[https://github.com/KuiZeng/STAVOS](https://github.com/KuiZeng/STAVOS)]([https://github.com/KuiZeng/STAVOS](https://github.com/KuiZeng/STAVOS)) (accessed on 1 December 2023).  
Figure 8: Comparison Line Chart of Actual and Predicted Heartbeat Counts.  
**Author Contributions:** Conceptualization, S.X. and M.C.; methodology, K.Z.; software, K.Z. and D.S.; validation, K.Z., S.X. and D.S.; formal analysis, K.Z. and S.X.; data curation, S.X. and K.Z.; writing--original draft preparation, K.Z.; writing--review and editing, K.Z., S.X. and M.C.; supervision, M.C.; project administration, S.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Research and Development Planning in Key Areas of Guangdong Province (No. 2021B0202070001) and The Bioinformatics Research and Database Construction of Antifreeze Genes in Fish (No. A2-2006-21-200208).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** If any researcher needs a dataset and code related to this article, they can do so on the website [[https://github.com/kuizeng/stavos](https://github.com/kuizeng/stavos)]([https://github.com/kuizeng/stavos](https://github.com/kuizeng/stavos)) (accessed on 1 December 2023). Download it yourself or request it from the author via email. Email: [EMAIL_ADDRESS].  
**Acknowledgments:** The authors are very grateful to [PERSON] of the German Centre for Organizational Studies at Heidelberg University for publicly releasing heartbeat video data from their recorded medaka.  
**Conflicts of Interest:** The authors declare no conflicts of interest.