Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images

PURPOSE

Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR-iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS.

METHODS

A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented.

RESULTS

Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods.

CONCLUSION

The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.

Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data

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 3D contrast-enhanced ultrasound (CEUS): a prospective study of 50 patients with brain tumours

BACKGROUND

Reliable intraoperative resection control during surgery of malignant brain tumours is associated with the longer overall survival of patients. B-mode ultrasound (BUS) is a familiar intraoperative imaging application in neurosurgical procedures and supplies excellent image quality. However, due to resection-induced artefacts, its ability to distinguish between tumour borders, oedema, surrounding tissue and tumour remnants is sometimes limited. In experienced hands, this "bright rim effect" could be reduced. However, it should be determined, if contrast-enhanced ultrasound can improve this situation by providing high-quality imaging during the resection. The aim of this clinical study was to examine contrast-enhanced and three-dimensional reconstructed ultrasound (3D CEUS) in brain tumour surgery regarding the uptake of contrast agent pre- and post-tumour resection, imaging quality and in comparison with postoperative magnetic resonance imaging in different tumour entities.

METHODS

Fifty patients, suffering from various brain tumours intra-axial and extra-axial, who had all undergone surgery with the support of neuronavigation in our neurosurgical department, were included in the study. Their median age was 56 years (range, 28-79). Ultrasound imaging was performed before the Dura was opened and for resection control at the end of tumour resection as defined by the neurosurgeon. A high-end ultrasound (US) device (Toshiba Aplio XG®) with linear and sector probes for B-mode and CEUS was used. Navigation and 3D reconstruction were performed with a LOCALITE SonoNavigator® and the images were transferred digitally (DVI) to the navigation system. The contrast agent consists of echoic micro-bubbles showing tumour vascularisation. The ultrasound images were compared with the corresponding postoperative MR data in order to determine the accuracy and imaging quality of the tumours and tumour remnants after resection.

RESULTS

Different types of tumours were investigated. High, dynamic contrast agent uptake was observed in 19 of 21 patients (90 %) suffering from glioblastoma, while in 2 patients uptake was low and insufficient. In 52.4 % of glioblastoma and grade III astrocytoma patients CEUS led to an improved delineation in comparison to BUS and showed a high-resolution imaging quality of the tumour margins and tumour boarders. Grade II and grade III astrocytoma (n = 6) as well as metastasis (n = 18) also showed high contrast agent uptake, which led in 50 % to an improved imaging quality. In 5 of these 17 patients, intraoperative CEUS for resection control showed tumour remnants, leading to further tumour resection. Patients treated with CEUS showed no increased neurological deficits after tumour resection. No pharmacological side-effects occurred.

CONCLUSIONS

Three-dimensional CEUS is a reliable intraoperative imaging modality and could improve imaging quality. Ninety percent of the high-grade gliomas (HGG, glioblastoma and astrocytoma grade III) showed high contrast uptake with an improved imaging quality in more than 50 %. Gross total resection and incomplete resection of glioblastoma were adequately highlighted by 3D CEUS intraoperatively. The application of US contrast agent could be a helpful imaging tool, especially for resection control in glioblastoma surgery.

A generic concept for the development of model-guided clinical decision support systems

Disease development and progression are very complex processes which make clinical decision making non-trivial. On the one hand, examination results that are stored in multiple formats and data types in clinical information systems need to be considered. Beyond, biological or molecular-biological processes can influence clinical decision making. So far, biological knowledge and patient data is separated from each other. This complicates inclusion of all relevant knowledge and information into the decision making. In this paper, we describe a concept of model-based decision support that links knowledge about biological processes, treatment decisions and clinical data. It consists of three models: 1) a biological model, 2) a decision model encompassing medical knowledge about the treatment workflow and decision parameters, and 3) a patient data model generated from clinical data. Requirements and future steps for realizing the concept will be presented and it will be shown how the concept can support the clinical decision making.