## 4 Discussion  
### General Approach  
The automatic detection of brain areas including tumor residuals is based on the representation of tumor tissue in iB-mode and iCEUS image data. Ultrasound contrast enhancement is visible  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patient** & **Qualitative** & \(Overlap\) & **Success** & \(Acc\) & \(AUC\) & \(Err\) \\ \hline  
1/1\) & 0 & 0 & – & – & – \\
19 & \(1/-1\) & 0.6464 & 1 & 0.9766 & 0.8837 & 0.0234 \\
20 & \(/1\) & – & – & – & – & – \\
21 & \(/-1\) & – & – & – & – \\
22 & \(/-1\) & – & – & – & – \\
23 & \(/1\) & – & – & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(Overlap\), accuracy (\(Acc\)), area under the curve (\(AUC\)) and error rate (\(Err\)) measures obtained from the identification of residual brain tumors by using the proposed data fusion approach. \(Overlap\) values above 0.5 indicate the successful localization of the residual tumor (success = 1), and those under this threshold value mean failure (success = 0). Patients 1 to 19 presented tumor residuals, while tumor tissue was completely removed during the operation for Patients 20 to 23.  
only in vascularized tissue, like tumors or vascular structures. Therefore, these structures are easily distinguishable from surrounding lobar parenchyma in the iCEUS data. In some cases, local brain tissue edema and local small blood layers show a slight enhancement, but they are different in echogenity to normal tissue in B-mode. Besides, with our linear probe, we have focused on the tumor and the surrounding tissue. Therefore, the basal ganglia area was mainly out of our focus, and in the remaining cases, we found no remarkable higher enhancement. Because this region plays a relevant role in the brain [43], a study taking into account the problem of tumor residual detection based on CEUS close to this area is important in the future. However, the iCEUS modality is still at the evaluation stage for brain tumor applications. The comparison of highlighted areas in the iCEUS data with their histological findings on the same patient dataset was performed previously [16]. A sensitivity of 85% and a specificity of 28% were obtained. Moreover, the evaluation of the approach was performed using manual segmentation, the reliability of which is questionable. As described previously, four experts (neurosurgeons and scientists) with experience with intraoperative ultrasound imaging were involved in the manual segmentation. Postoperative MR data and radiological findings were used to confirm the annotations. Even if the certainty about the manual segmentation of tumor residuals has not been proven, no better validation method is currently available. Therefore, first, a global quantitative evaluation method based on an overlap similarity measure was used. It quantified the position agreement of two regions, rather than the number of common elements (or voxels). This method is more suitable when uncertainties on the target regions are obvious. Second, additional metrics were used to evaluate the method in terms of voxel classification.  
The manual and quantitative validation results showed three limitations in the approach of tumor residual detection. Firstly, the current algorithm may miss residual tumors. Secondly, the algorithm extracts extra regions, which were not labeled as residual tumors by the expert. Thirdly, regions including tumor remnants segmented by the algorithm and in the ground truth have different sizes and positions. These three points are discussed in the next paragraphs.