The combination of the information in the iB-mode and iCEUS image data, also called data fusion, can support the identification of tumor residuals. Image fusion consists of combining relevant information from various source images of the same scene into a single resulting image called the \"fused image\". The aim of fusion is to preserve specific details of the source images within the fused image to obtain a better representation and understanding of the scene. In theory, three levels of image fusion can be distinguished: the pixel level, the feature level and the decision level [18; 19]. The initial level is known as the lowest level because it directly involves the pixels of the source images. The second level utilizes features or objects extracted from source images. The highest level involves decision rules. This technique is largely used in many applications like remote sensing [20; 21], computer vision [22; 23] and medical imaging [24; 25]. In the medical field, image fusion is mainly applied to provide a high quality in patient data representation by using images from different modalities. The objectives of image fusion are mainly the improvement of the image's contrast and rectification of image degradation. Image fusion is performed using various fundamental methods. [PERSON] et al. [26] combined a non-subsampled contourlet transform (NST) with a reduced pulse-coupled neural network and fuzzy logic technique to overcome the image fusion problems such as contrast reduction and image degradations. [PERSON] et al. employed a dictionary learning approach [27]. Due to the limited and redundant information in image patches created by using traditional dictionary learning methods, an alternative scheme of image patch sampling and clustering was proposed. Then, the K-SVD algorithm was used for training of patch groups into compact sub-dictionaries, which were then combined into a complete dictionary. Furthermore, a multimodal (CT/MRI) image fusion method based on NST was introduced by [PERSON] et al. [28]. The resultant low and high-frequency coefficients were respectively combined through the phase congruency and directive contrast-based models. Then, the inverse NST was applied on composite coefficients to recover the fused image. Since nature-inspired techniques became popular in computer vision, they have been applied extensively in medical image fusion. [PERSON] et al. [29] have fused multimodal medical images by means of adaptive pulse-coupled neural networks (PCNN). They proposed automatic and optimum parameters tuning of the PCNN model by using the quantum-behaved particle swarm optimization algorithm. In the same fashion, the swarm intelligence of the ant colony and neural network was used for fusing images from PET, MRI, SPECT and MRI modalities [30]. The loss of edges and directional information often occurs during feeding of neural network inputs. Therefore, to solve this problem, the ant colony optimization and statistical scaling techniques were respectively used to detect and enhance the image's edges before the neural network training and testing. Above all, the image fusion has demonstrated its effectiveness for planning and intraoperative interventions, especially in  
Figure 2: Intraoperative B-mode ultrasound (iB-mode) (**left**) and iCEUS (contrast enhanced ultrasound (CEUS)) (**right**) patient image data acquired at the end of a brain tumor operation. neurosurgery. Fusion techniques allow, in this context, augmenting the visualization of anatomical structures that are depicted only in one imaging modality or to monitor in time the evolution of a disease. For instance, the CT-MR fusion images were used by [PERSON] et al. [31] for supporting the surgeon to improve the surgical performance of temporal bone tumors. Furthermore, [PERSON] et al. [32] presented the fusion imaging between preoperative MRI and iUS for intra-operative ultrasound-based navigation in the context of brain tumor removal.