Resumen
Intraoperative ultrasound (iUS) imaging is routinely performed to assist neurosurgeons during tumor surgery. In particular, the identification of the possible presence of residual tumors at the end of the intervention is crucial for the operation outcome. B-mode ultrasound remains the standard modality because it depicts brain structures well. However, tumorous tissue is hard to differentiate from resection cavity borders, blood and artifacts. On the other hand, contrast enhanced ultrasound (CEUS) highlights residuals of the tumor, but the interpretation of the image is complex. Therefore, an assistance system to support the identification of tumor remnants in the iUS data is needed. Our approach is based on image segmentation and data fusion techniques. It consists of combining relevant information, automatically extracted from both intraoperative B-mode and CEUS image data, according to decision rules that model the analysis process of neurosurgeons to interpret the iUS data. The method was tested on an image dataset of 23 patients suffering from glioblastoma. The detection rate of brain areas with tumor residuals reached by the algorithm was qualitatively and quantitatively compared with manual annotations provided by experts. The results showed that the assistance tool was able to successfully identify areas with suspicious tissue.