# Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data
## Materials and Methods
### Vascular Structure Identification  
The proposed method consists in identifying the vascular structures in the image data involved in the patient treatment. During the planning stage, some vascular structures close to the tumor are selected by the neurosurgeon. These patterns are automatically recognized in the 3D-iCEUS data by using image registration techniques. The identification method of vascular structures is depicted in Figure 3 and is described as follows:  
* Step 1: Selecting a vascular segment pattern in cT1 MR Interaction with the application in the operating room has to be limited because of sterilization constraints and restricted time. Due to this, during the operation planning, the user delineates interactively a region of interest including a blood vessel near to the tumor in the cT1 MR data. The vascular structure is segmented using the method described in Section 2.2. It performs well because the blood vessels are enhanced in the cT1 MR data due to the contrast agent, and any other anatomical structure represented with similar intensities is included in the region of interest. The segmented blood vessel represents the pattern (white frame, Step 1, Figure 3) that is searched for in the 3D-iCEUS\({}_{start}\) and 3D-iCEUS\({}_{end}\) data.
* Step 2: Blood vessel identification in 3D-iCEUS\({}_{start}\) The pattern is firstly searched in the 3D-iCEUS\({}_{start}\) data acquired before resection. In order to reduce the computing time, the search space (large white frame, Step 2, Figure 3) is smaller than the entire 3D-iCEUS\({}_{start}\), but large enough to take the tissue deformations into account. It has twice the volume of the region of interest defined in the cT1 MR data, and is centered on the same image position. The enhanced structures in this region are segmented and then a rigid registration method is used to find the sample in the 3D-iCEUS\({}_{start}\) data (yellow frame, Step 2, Figure 3) which corresponds best to the pattern. A rigid transformation is sufficient here, since the goal is the identification of the position of the blood vessel in the 3D-iCEUS\({}_{start}\) data, which looks like the vascular segment pattern selected in the cT1 MR data. The blood vessel detected in the 3D-iCEUS\({}_{start}\) then becomes the new pattern, which has to be identified in the 3D-iCEUS\({}_{end}\) data after resection.
* Step 3: Blood vessel identification in the 3D-iCEUS\({}_{end}\) A similar method as described in the step 2 enables to find the position of the target blood vessel (red frame, Step 3, Figure 4) within the search space (yellow frame, Step 3, Figure 3) defined in the 3D-iCEUS\({}_{end}\) data acquired after tumor resection.  
Finally, the preselected vessel in the cT1 MR is overlapped on the 3D-iCEUS\({}_{start}\) and 3D-iCEUS\({}_{end}\) images for visualization purpose. Figure 4 illustrates the workflow of the entire method and illustrations of the blood vessel identification on patient data is shown in Section 3.  
Figure 3: Process of the proposed vascular structure identification.  
It should be noted that the method is so far not able to recognize an incorrect identification of the selected blood vessel. In this case, the user has to reposition interactively the area including the vascular segment corresponding to the pattern. However, the approach is to minimize the interaction between surgeon and computer in operating room because of the sterilization requirement.