Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately.

In this paper, a method of vascular structure identification in intraoperative 3D Contrast-Enhanced Ultrasound (CEUS) data is presented. Ultrasound imaging is commonly used in brain tumor surgery to investigate in real time the current status of cerebral structures. The use of an ultrasound contrast agent enables to highlight tumor tissue, but also surrounding blood vessels. However, these structures can be used as landmarks to estimate and correct the brain shift. This work proposes an alternative method for extracting small vascular segments close to the tumor as landmark. The patient image dataset involved in brain tumor operations includes preoperative contrast T1MR (cT1MR) data and 3D intraoperative contrast enhanced ultrasound data acquired before (3D-iCEUS ${}_{start}$ ) and after (3D-iCEUS ${}_{end}$ ) tumor resection. Based on rigid registration techniques, a preselected vascular segment in cT1MR is searched in 3D-iCEUS ${}_{start}$ and 3D-iCEUS ${}_{end}$ data. The method was validated by using three similarity measures (Normalized Gradient Field, Normalized Mutual Information and Normalized Cross Correlation). Tests were performed on data obtained from ten patients overcoming a brain tumor operation and it succeeded in nine cases. Despite the small size of the vascular structures, the artifacts in the ultrasound images and the brain tissue deformations, blood vessels were successfully identified.

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.