# Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data
## 3 Results  
The collection of data has been performed at the University Hospital, Department of Neurosurgery, University of Leipzig, Germany, in the context of a previous research project funded by the German Research Society (Deutsche Forschungsgemein-schaft) and accepted by the ethic commission of the University of Leipzig. The implementation was done with an Intel Celeron, 1.5 GHz and 2 GB of memory using MeVisLab tool. The manual registration was validated by the neurosurgeon of this institution. The identification algorithm was tested on ten datasets of patients who overcame a brain tumor operation. A simple vascular segment near to the tumor was selected in the preoperative cT1 MR data. Table 1 includes the DSI, Hausdorff distances and the processing time values computed between the segmented blood vessels in the cT1 MR and 3D-iCEUS\({}_{start}\) data and between the segmented blood vessels in the 3D-iCEUS\({}_{start}\) and 3D-iCEUS\({}_{end}\) data, both normalized with the scores obtained by the expert registration. The values followed by a star indicate that the algorithm failed to find the correct vascular segment. If it occurred before resection, the identification in the 3D-iCEUS\({}_{end}\) data was not performed. It is clearly observed that the proposed approach was able to find correctly the targeted vascular structure with at least one similarity measure used. Besides, Table 2 includes the mean DSI values and the mean Hausdorff distances, averaged on the successful cases, while Table 3 presents the mean computing time. The outcomes show that the NGF achieves the highest mean rate of registration than the NMI and NCC. Nevertheless, the last ones performs the registration in a shortest time. In Figure 5, the results obtained step by step for five cases are illustrated. First, the ROI is defined in the cT1 MR by the surgeon (column (a)) in the planning stage and it encloses the selected vascular structure so as to reduce the space of segmentation. Second, the vessel is extracted by using the process described in Section 2.2 (column (b)). Third, the vascular structure extracted from the cT1 MR data is overlaid on the 3D-iCEUS\({}_{start}\) (column (c)). It should be observed that the extracted structure is not spatially aligned with its corresponding in the 3D-iCEUS\({}_{start}\). Fourth, after the segmentation of the vascular structure in 3D-iCEUS\({}_{start}\), column (d) presents the registration results, in which, vascular structures are aligned. At this step, the blood vessel found in 3D-iCEUS\({}_{start}\) becomes the new pattern and it is superimposed on the 3D-iCEUS\({}_{end}\) image in column (e). In most of cases, there is no matching between the new pattern with its corresponding vessel in the 3D-iCEUS\({}_{end}\) because of large tissue deformations at this stage of the operation. Finally, the registration of vascular structures from the 3D-iCEUS\({}_{star}\) to 3D-iCEUS\({}_{end}\) is carried out (column (f)). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Blood Vessel Identification in the 3D-CEUS\({}_{start}\)**} & \multicolumn{3}{c}{**Blood Vessel Identification in the 3D-CEUS\({}_{start}\)**} \\
**Patient** & **Similarity Measure** & **ROI in CTIMR (vocules)** & \multicolumn{3}{c}{**Using the Pattern from the CTIMR**} & \multicolumn{3}{c}{**Using the Pattern from the 3D-CEUS\({}_{start}\)**} \\ \cline{4-7}  & & & **Processing Time (s)** & **DSI** & **Hausdorff Distance (mm)** &