Abstract
For patients with malignant brain tumors (glioblastomas), a safe maximal resection of tumor is critical for an increased survival rate. However, complete resection of the cancer is hard to achieve due to the invasive nature of these tumors, where the margins of the tumors become blurred from frank tumor to more normal brain tissue, but in which single cells or clusters of malignant cells may have invaded. Recent developments in fluorescence imaging techniques have shown great potential for improved surgical outcomes by providing surgeons intraoperative contrast-enhanced visual information of tumor in neurosurgery. The current near-infrared (NIR) fluorophores, such as indocyanine green (ICG), cyanine5.5 (Cy5.5), 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX (PpIX), are showing clinical potential to be useful in targeting and guiding resections of such tumors. Real-time tumor margin identification in NIR imaging could be helpful to both surgeons and patients by reducing the operation time and space required by other imaging modalities such as intraoperative MRI, and has the potential to integrate with robotically assisted surgery. In this paper, a segmentation method based on the Chan-Vese model was developed for identifying the tumor boundaries in an ex-vivo mouse brain from relatively noisy fluorescence images acquired by a multimodal scanning fiber endoscope (mmSFE). Tumor contours were achieved iteratively by minimizing an energy function formed by a level set function and the segmentation model. Quantitative segmentation metrics based on tumor-to-background (T/B) ratio were evaluated. Results demonstrated feasibility in detecting the brain tumor margins at quasi-real-time and has the potential to yield improved precision brain tumor resection techniques or even robotic interventions in the future.