## 7 Experiments and Results
### Experiment 3: Linear Blending with the RORPO Filter
#### 7.3.1 Comparison of Segmentation Models with Different Blending Values  
We have compared our method of linear blending with the RORPO filterm using either 3D U-Net or 3D V-Net as the segmentation architecture. The highest dice score is achieved by blending with 60% (\(\alpha=0.6\)) of the image enhanced us  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Filter**} & \multicolumn{4}{c}{**Original annotations**} \\ \cline{2-5}  & **DSC** & **ACC** & **SEN** & **PRC** \\ \hline Unenhanced & \(56.4\pm 13.8\) & \(98.3\pm 2.3\) & \(66.0\pm 14.2\) & \(56.5\pm 23.9\) \\ [PERSON] & \(60.0\pm 12.1\) & \(99.0\pm 0.3\) & \(62.9\pm 6.2\) & \(62.4\pm 21.6\) \\ Frangi & \(62.1\pm 6.6\) & \(99.0\pm 0.4\) & \(\mathbf{74.6\pm 6.6}\) & \(53.9\pm 8.6\) \\ Meijering & \(62.4\pm 8.9\) & \(99.0\pm 0.7\) & \(69.1\pm 9.2\) & \(57.6\pm 11.9\) \\ Sato & \(58.3\pm 9.1\) & \(98.7\pm 0.9\) & \(68.4\pm 7.3\) & \(51.5\pm 11.4\) \\ RORPO & \(\mathbf{62.7\pm 6.6}\) & \(\mathbf{99.2\pm 0.2}\) & \(60.7\pm 6.7\) & \(\mathbf{69.4\pm 19.1}\) \\ \hline \hline \multirow{2}{*}{**Filter**} & \multicolumn{4}{c}{**Improved annotations**} \\ \cline{2-5}  & **DSC** & **ACC** & **SEN** & **PRC** \\ \hline Unenhanced & \(62.7\pm 15.4\) & \(98.2\pm 2.3\) & \(72.5\pm 13.7\) & \(63.2\pm 25.5\) \\ Hessian & \(66.1\pm 8.0\) & \(99.0\pm 0.3\) & \(68.3\pm 5.3\) & \(65.4\pm 13.0\) \\ Frangi & \(69.6\pm 3.9\) & \(99.1\pm 0.1\) & \(75.7\pm 6.1\) & \(64.6\pm 3.7\) \\ Meijering & \(66.7\pm 10.1\) & \(98.7\pm 1.0\) & \(76.8\pm 6.2\) & \(61.2\pm 16.2\) \\ Sato & \(67.0\pm 4.8\) & \(98.9\pm 0.4\) & \(\mathbf{77.7\pm 7.1}\) & \(59.2\pm 4.9\) \\ RORPO & \(\mathbf{69.7+4.8}\) & \(\mathbf{99.2\pm 0.1}\) & \(62.9\pm 6.1\) & \(\mathbf{79.1\pm 9.0}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different enhancement filters on the 3D-IRCADb dataset and 3D U-Net as a segmentation network.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Filter**} & \multicolumn{4}{c}{**3D U-Net**} \\ \cline{2-5}  & **DSC** & **ACC** & **SEN** & **PRC** \\ \hline Unprocessed & \(62.7\pm 15.4\) & \(98.2\pm 2.3\) & \(72.5\pm 13.7\) & \(63.2\pm 25.5\) \\ FilterAdded\({}_{He}\) & \(67.8\pm 4.8\) & \(99.0\pm 0.4\) & \(72.8\pm 5.6\) & \(64.4\pm 9.0\) \\ FilterAdded\({}_{Ro}\) & \(71.7\pm 3.2\) & \(99.1\pm 0.2\) & \(77.3\pm 5.0\) & \(67.1\pm 3.7\) \\ SegAdded\({}_{He}\) & \(70.2\pm 4.2\) & \(99.0\pm 0.3\) & \(\mathbf{79.5\pm 6.1}\) & \(63.1\pm 5.0\) \\ SegAdded\({}_{Ro}\) & \(70.9\pm 3.8\) & \(99.0\pm 0.3\) & \(79.3\pm 6.1\) & \(64.3\pm 4.2\) \\ Our method & \(\mathbf{74.4\pm 4.6}\) & \(\mathbf{99.3\pm 0.1}\) & \(72.7\pm 5.8\) & \(\mathbf{76.5\pm 6.4}\) \\ \hline \hline \multirow{2}{*}{**Filter**} & \multicolumn{4}{c}{**3D V-Net**} \\ \cline{2-5}  & **DSC** & **ACC** & **SEN** & **PRC** \\ \hline Unprocessed & \(68.4\pm 12.2\) & \(98.7\pm 1.1\) & \(80.7\pm 3.6\) & \(61.3\pm 17.3\) \\ FilterAdded\({}_{He}\) & \(68.8\pm 4.4\) & \(99.1\pm 0.2\) & \(69.5\pm 4.9\) & \(68.3\pm 6.0\) \\ FilterAdded\({}_{Ro}\) & \(73.5\pm 3.8\) & \(99.2\pm 0.1\) & \(75.9\pm 4.8\) & \(71.5\pm 5.3\) \\ SegAdded\({}_{He}\) & \(72.9\pm 3.5\) & \(99.1\pm 0.1\) & \(81.5\pm 4.4\) & \(66.1\pm 4.4\) \\ SegAdded\({}_{Ro}\) & \(72.7\pm 3.7\) & \(99.1\pm 0.1\) & \(\mathbf{82.1\pm 3.8}\) & \(65.5\pm 5.0\) \\ Our method & \(\mathbf{75.4\pm 4.3}\) & \(\mathbf{99.3\pm 0.1}\) & \(76.9\pm 4.1\) & \(\mathbf{74.4\pm 7.8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of segmentation pipelines with enhancement filters applied to improved 3D-IRCADb with standard Dice Loss function and different networks.  
(\(\beta=0.4\)) of the original image in both 3D U-Net- and 3D V-Net-based architectures; see Figure 11. It also shows that segmentation with 3D V-Net provides better dice scores and more consistent results over a wider spectrum of \(\alpha\) values than 3D U-Net. As an acceptable region for blending value, the region between 40 and 60% is set.