## 5 Discussion
### Strength, Limitation, and Potential Future Application of the Study  
The described solution provides high-quality segmentation of liver parenchyma and CLRM on MRI data in less than 6 s. A manual correction was applied at the end of the method before medical use, as there were cases with low sensitivity in the test set. The overall time for correcting predictions varied from 12 to 32 min and is very likely shorter than creating segmentations purely using semi-automatic tools for both parenchyma and lesions. This is especially important for the segmentation of liver parenchyma, an extensive segmentation task covering multiple slices and challenging areas in contact with other organs, requiring medical knowledge to orient in patient-specific anatomy and also a lot of care and attention to determining liver borders. As lesions in the liver are often smaller and surrounded by liver parenchyma, the corrections could be performed using purely manual tools that are not extremely time demanding. The choice of the network to use in the final experiments was performed by the segmentation results achieved on our validation set. Although a large effort was made to find the best hyperparameters for the networks, the principal architectural components such as kernel size, batch normalization technique, and layer activation functions were predefined by the architecture implementation authors. Those parameters were different from FCNN, and maybe further experiments could increase the validation of the results. However, the aim of the study was not focused on network architecture development and modification, but rather on the application of already available DL tools into the created dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors, Year** & **Study Goal and DL Method** & **Liver Dice** & **Tumor Dice** \\ \hline [PERSON] et al., 2020 & Liver segmentation using 3D U-net via T1 & & \\
[53] & weighted dataset of 153 cases & 0.970 & n\(\backslash\)a \\ \hline [PERSON] et al., 2021 & Liver segmentation using V-net via T1 & & \\
[54] & weighted MRI dataset of 100 patients & 96.0 \(\pm\) 1.9 & n\(\backslash\)a \\ \hline [PERSON] et al., 2017 & Liver and HCC tumor segmentation via 2D & & \\
[36] & U-net from DW-MRI T2-weighted dataset of & 87 & 69.7 \\  & 31 patients & & \\ \hline [PERSON] et al., 2018 & HCC tumor segmentation via 2D U-net from & & \\
[55] & DCE-MRI dataset of 9 patients & n\(\backslash\)a & 0.482 \\ \hline [PERSON] et al., 2019 & Liver segmentation via FCNN network from & & \\
[56] & 6 SCE MR images and tumor detection via & & \\
[56] & dual pathway FCNN from SCE and DW NRI & & \\  & images from 121 patients & & \\ \hline [PERSON] al., 2020 & Liver and HCC tumor segmentation via 3D & & \\
[40] & U-net from multiphasic contrast-enhanced & 0.91 \(\pm\) 0.01 & 0.68 \(\pm\) 0.03 \\  & T1-weighted MRI dataset from 174 patients & & \\ \hline [PERSON] al., 2021 & HCC tumor and hemangioma segmentation & & \\
[41] & via united adversarial learning framework & & \\
[41] & UAL from multi modal contrast-enhances & & \\  & (T1-, T2-weighted and DWI) MRI images & & \\  & from 255 patients. & & \\ \hline Our method & Liver and CLRM tumor segmentation via & & \\  & HighResNet from a T1-weighted & & \\  & contrast-enhanced dataset of 80 MRI images & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of MRI based liver and liver tumor segmentation studies (n\(\backslash\)a—not applicable). Another set of experiments that could possibly improve the results would be the use of different strategies for weight initialization for the final network. It could include pre-training of the network using open medical image segmentation datasets, for example, the LITS dataset [23]. Further experiments and training on a larger dataset using the cascade method would make the method more robust and could further decrease the required time for the manual segmentation editing for the clinical use of proposed AI application.