  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.
