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.
Danying Hu, Yang Jiang, Evgenii Belykh, Yuanzheng Gong, Mark C. Preul, Blake Hannaford, and Eric J. Seibel, "Toward real-time tumor margin identification in image-guided robotic brain tumor resection," Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101350D (Presented at SPIE Medical Imaging: February 14, 2017; Published: 3 March 2017); https://doi.org/10.1117/12.2255417.
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