## 4 Discussion  
Despite an enormous increase in ultrasound image quality in recent years, recognizing anatomical structures in oblique and narrow US sections can become tedious. The first attempts to address this issue date as far back as 1993, when [PERSON] et al. used intraoperative ultrasound as a control  
Figure 4: Scatterplots with median line of (**a**) Hausdorff distance. Median: 8.1 mm, mean \(\pm\) SD: 8.1 \(\pm\) 2.9 mm. (**b**) Euclidean distance of geometric center of gravity. Median: 3.0 mm, mean \(\pm\) SD: 3.7 \(\pm\) 2.5 mm. Figure 3: Scatter plot of the Dice coefficients. The line represents the median Dice coefficient of 0.75.  
for reformatted CT and MRI image sets during neurosurgical procedures by establishing a common axes on which preoperative and intraoperative images where aligned [42]. However, only when trackable US probes were developed, US and navigational systems were able to coalesce to integrated systems [22; 43; 44], which allowed displaying preoperative and intraoperative image sets as overlays or side-by-side [23; 28]. This baseline study was conducted to describe the obvious differences in object appearances in iUS and preoperative MRI at the earliest possible surgical stage after craniotomy. We chose to acquire the iUS-scan before dural opening, to keep the influence of brain shifting as low as possible and to enable a comparison of the imaging modalities under optimized conditions. Initial patient registration was achieved by an iCT scan and fusion to the preoperative MRI data sets with high accuracy (mean \(\pm\) SD TRE: 0.84 \(\pm\) 0.36 mm), which is comparable to our previously published data [35]. The US probes were precalibrated, meaning that they were fully implemented into the navigational system, and thus a co-registration was automatically established. This kind of MRI and US fusion is based on spatial position information and can be categorized as a rigid or non-deformable registration [45; 46]. Alternatively, rigid registration can be performed image-based using different algorithms to match structures in MRI and US [47; 48], but any non-deformable registration approach does not tackle the issue of brain deformation and distortion. However, rigid co-registration allowed the assessment of the spatial deviation and deformation of structures segmented in both modalities. Statistical analysis of the segmented tumor volumes in preoperative MRI and intraoperative US revealed a very similar mean \(\pm\) SD (preMRI: 13.6 \(\pm\) 16.0 cm\({}^{3}\); iUS: 13.5 \(\pm\)16.0 cm\({}^{3}\)) and median (preMRI: 8.5 cm\({}^{3}\); iUS: 8.8 cm\({}^{3}\)), which did not differ significantly. The median of the magnitude of the differences was 0.40 cm\({}^{3}\). These results indicate both modalities being comparable with respect to tumor delineation. This might be partially fostered by the study design that only included brain metastases, whose tumor boundaries could be clearly identified in both T1-enhanced MRI and US. Including gliomas in this study, especially low-grade gliomas, on the other hand, would have made tumor identification and subsequent analysis of data dependent on the segmentation much more difficult. Spatial overlap was assessed using the Dice coefficient, which was 0.68 \(\pm\) 0.22 (mean \(\pm\) SD) with a median of 0.75, where 1.0 would indicate a perfect match and 0 no association at all [36]. Consistently, the Dice coefficient correlated positively to the tumor size. Even though the Dice coefficient is thought to allow straightforward comparison, interpretation of the coefficient in this study must take into account several factors. First, the calculated measures are influenced by segmentation inaccuracy, as shown by [PERSON] et al., who found in a series of repeated segmentation of the prostate peripheral zone in patients with prostate cancer, a mean Dice coefficient of 0.883 in 1.5 T MRI and 0.838 in 0.5 T MRI segmentations [37].