Advanced minimally invasive neurosurgery navigation relies mainly on Magnetic Resonance Imaging (MRI) guidance. MRI guidance, however, only provides pre-operative information in the majority of the cases. Once the surgery begins, the value of this guidance diminishes to some extent because of the anatomical changes due to surgery. Guidance with live image feedback coming directly from the surgical device, e.g., endoscope, can complement MRI-based navigation or be an alternative if MRI guidance is not feasible. With this motivation, we present a method for live image-only guidance leveraging a large data set of annotated neurosurgical videos.First, we report the performance of a deep learning-based object detection method, YOLO, on detecting anatomical structures in neurosurgical images. Second, we present a method for generating neurosurgical roadmaps using unsupervised embedding without assuming exact anatomical matches between patients, presence of an extensive anatomical atlas, or the need for simultaneous localization and mapping. A generated roadmap encodes the common anatomical paths taken in surgeries in the training set. At inference, the roadmap can be used to map a surgeon's current location using live image feedback on the path to provide guidance by being able to predict which structures should appear going forward or backward, much like a mapping application. Even though the embedding is not supervised by position information, we show that it is correlated to the location inside the brain and on the surgical path. We trained and evaluated the proposed method with a data set of 166 transsphenoidal adenomectomy procedures.

翻译：先进的微创神经外科导航主要依赖磁共振成像（MRI）引导。然而，在大多数情况下，MRI引导仅能提供术前信息。一旦手术开始，由于手术造成的解剖结构变化，这种引导的价值会在一定程度上降低。来自手术设备（如内窥镜）的实时图像反馈引导可以补充基于MRI的导航，或在MRI引导不可行时作为替代方案。基于这一动机，我们提出了一种仅依赖活体图像引导的方法，利用大规模标注的神经外科视频数据集。首先，我们报告了基于深度学习的物体检测方法YOLO在神经外科图像中检测解剖结构的性能。其次，我们提出了一种利用无监督嵌入生成神经外科路径图的方法，该方法不假设患者之间具有精确的解剖匹配、不存在广泛解剖图谱、也不需要使用同步定位与地图构建。生成的路径图编码了训练集手术中常见的解剖路径。在推理时，该路径图可用于将外科医生的当前位置（通过实时图像反馈获得）映射到路径上，从而能够预测前进或后退时应该出现的结构，类似于地图导航应用。尽管嵌入未受位置信息的监督，但我们证明其与大脑内部及手术路径上的位置具有相关性。我们使用包含166例经蝶腺瘤切除术程序的数据集对所提方法进行了训练与评估。