# STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
## 5 Results
### Automated System Results  
We applied the optimal weights trained by STAVOS to our automated system. For inputting a medaka video, the system produces a visual segmentation result GIF, a ventricular parameter table, an ECG GIF, and a 3D dynamic simulation GIF of heartbeats. We selected outputs from two videos (R0039, N0080) for presentation, as shown in Table 4 and Figure 7.  
Figure 6: Qualitative Comparison of STAVOS with U-Net and TBD. The blue curve represents the true contour of the ventricle, while the red curve represents the predicted ventricular contour. ED stands for end-diastole, and ES stands for end-systole. N denotes the Data-medaka-Lateral-right Dataset, and R denotes the Data-medaka-Ventral Dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **U-Net** & **TBD** & **STAVOS** \\ \hline IoU of N & 0.490 & 0.789 & 0.863 \\ mIoU of N & 0.534 & 0.891 & 0.930 \\ IoU of R & 0.496 & 0.883 & 0.908 \\ mIoU of R & 0.565 & 0.937 & 0.951 \\ mean accuracy & 0.521 & 0.875 & 0.913 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The accuracy of U-Net, TBD, and STAVOS on the medaka test set.  
We manually measured the heartbeat counts of medaka in the medaka dataset and compared them with the counts calculated by the automated system, as shown in Figure 8. Among the 21 videos, our system achieved complete accuracy in 15 videos, and for the incorrect predictions, the errors were consistently within 1 count. This demonstrates that our automated system can accurately predict the heartbeat of medaka.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**ES Frame** & **ED Frame** & **Minor FS [\%]** & **Major FS [\%]** & **EDV [px3]** & **ESV [px3]** & **SV [px3]** & **EF [\%]** & **HR [bpm]** \\ \hline
2 & 4 & 17.85142 & 11.90518 & 1,375,722 & 2,314,089 & 938,367.3 & 40.5 & 106.25 \\
10 & 11 & 26.07969 & 9.845666 & 1,341,991 & 2,724,177 & 1,382,186 & 50.7 & 93.75 \\
17 & 22 & 9.965191 & 15.01318 & 1,414,451 & 2,053,125 & 638,673.9 & 31.1 & 114.2857 \\
25 & 28 & 16.94796 & 8.436501 & 1,362,875 & 1,257,988 & 795,022.8 & 36.8 & 106.25 \\
33 & 35 & 20.17185 & 9.791008 & 1,439,276 & 2,503,698 & 1,064,423 & 42.5 & 106.25 \\
40 & 43 & 19.40398 & 15.18253 & 1,353,724 & 2,457,071 & 1,103,347 & 44.9 & 106.25 \\
48 & 50 & 22.30603 & 1.42044 & 1,536,526 & 2,582,133 & 1,045,606 & 40.4 & 100 \\
56 & 58 & 18.81942 & 8.56368 & 1,480,583 & 2,457,025 & 976,442.4 & 39.7 & 100 \\
64 & 66 & 10.00727 & 6.485503 & 1,715,452 & 2,265,086 & 549,634.3 & 24.2 & 95 \\
72 & 75 & 14.61971 & 16.09441 & 1,401,323 & 2,291,037 & 889,713.9 & 38.8 & 112.5 \\ \hline \end{tabular}
\end{table}
Table 4: Ventricular parameters for R0039 output by the automated system. “ED”: end-diastole; “ES”: end-systole; “FS”: fractional shortening; “EDV”: the volume at ED; “ESV”: the volume at ES; “SV”: stroke volume; “EF”: ejection fraction; “HR”: heartrate. Due to the original table’s extensive length, the presented table is a truncated version, including only the first 10 rows.  
Figure 7: Screenshot of ECG and 3D Heartbeat Simulation. (**a**) Screenshots of the ECG for N0080; (**b**) Screenshots of the ECG for R0039; (**c**) Screenshots of the 3D dynamic simulation at ED for R0039; (**d**) Screenshots of the 3D dynamic simulation at ES for R0039.