Surgery for gliomas (intrinsic brain tumors), especially when low-grade, is challenging due to the infiltrative nature of the lesion. Currently, no real-time, intra-operative, label-free and wide-field tool is available to assist and guide the surgeon to find the relevant demarcations for these tumors. While marker-based methods exist for the high-grade glioma case, there is no convenient solution available for the low-grade case; thus, marker-free optical techniques represent an attractive option. Although RGB imaging is a standard tool in surgical microscopes, it does not contain sufficient information for tissue differentiation. We leverage the richer information from hyperspectral imaging (HSI), acquired with a snapscan camera in the 468-787 nm range, coupled to a surgical microscope, to build a deep-learning-based diagnostic tool for cancer resection with potential for intra-operative guidance. However, the main limitation of the HSI snapscan camera is the image acquisition time, limiting its widespread deployment in the operation theater. Here, we investigate the effect of HSI channel reduction and pre-selection to scope the design space for the development of cheaper and faster sensors. Neural networks are used to identify the most important spectral channels for tumor tissue differentiation, optimizing the trade-off between the number of channels and precision to enable real-time intra-surgical application. We evaluate the performance of our method on a clinical dataset that was acquired during surgery on five patients. By demonstrating the possibility to efficiently detect low-grade glioma, these results can lead to better cancer resection demarcations, potentially improving treatment effectiveness and patient outcome.

翻译：胶质瘤（内源性脑肿瘤）的手术，尤其是低级别胶质瘤，由于病灶的浸润性而极具挑战性。目前，尚无实时、术中、无标记且大视野的工具可辅助并引导外科医生定位这些肿瘤的相关边界。虽然针对高级别胶质瘤存在基于标记物的方法，但低级别胶质瘤尚无便捷解决方案；因此，无标记光学技术成为一种有吸引力的选择。尽管RGB成像是手术显微镜中的标准工具，但它并不包含足够的组织区分信息。我们利用高光谱成像（HSI）提供的更丰富信息——使用在468-787纳米范围内采集的快扫相机，并结合手术显微镜——构建基于深度学习的癌症切除诊断工具，具备术中引导潜力。然而，HSI快扫相机的主要限制在于图像采集时间，这制约了其在手术室的广泛应用。在此，我们研究HSI通道缩减与预选的影响，以探索开发更便宜、更快速传感器的设计空间。利用神经网络识别对肿瘤组织区分最重要的光谱通道，优化通道数量与精度之间的权衡，从而实现实时术中应用。我们在临床数据集上评估了方法的性能，该数据集来自五名患者手术期间采集的数据。通过证明有效检测低级别胶质瘤的可能性，这些结果有望实现更好的癌症切除边界划定，从而提高治疗效果并改善患者预后。