Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a noncontact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

翻译：脑癌手术是神经外科领域的一大难题。由于肿瘤对周围正常脑组织的弥漫性浸润，肉眼难以准确识别。由于手术是脑癌的常见治疗手段，精确的肿瘤根治性切除可提高患者的生存率。然而，手术过程中肿瘤边界的识别极具挑战性。高光谱成像是一种适用于医学诊断的非接触、非电离、无创技术。本研究提出了一种考虑高光谱图像空间和光谱特征的新型分类方法，旨在帮助神经外科医生在手术中准确定位肿瘤边界，避免过度切除正常组织或意外残留肿瘤。本研究提出的高效解决方案采用了一种结合监督与非监督机器学习方法的混合框架。为评估该方法的有效性，使用了来自五名不同患者的五张受胶质母细胞瘤影响的大脑表面体内高光谱图像。最终获得的分类图谱经专家分析验证，初步结果令人满意，能够精准勾勒肿瘤区域。