  Fiducial markers are commonly used in navigation assisted minimally invasive
spine surgery (MISS) and they help transfer image coordinates into real world
coordinates. In practice, these markers might be located outside the
field-of-view (FOV), due to the limited detector sizes of C-arm cone-beam
computed tomography (CBCT) systems used in intraoperative surgeries. As a
consequence, reconstructed markers in CBCT volumes suffer from artifacts and
have distorted shapes, which sets an obstacle for navigation. In this work, we
propose two fiducial marker detection methods: direct detection from distorted
markers (direct method) and detection after marker recovery (recovery method).
For direct detection from distorted markers in reconstructed volumes, an
efficient automatic marker detection method using two neural networks and a
conventional circle detection algorithm is proposed. For marker recovery, a
task-specific learning strategy is proposed to recover markers from severely
truncated data. Afterwards, a conventional marker detection algorithm is
applied for position detection. The two methods are evaluated on simulated data
and real data, both achieving a marker registration error smaller than 0.2 mm.
Our experiments demonstrate that the direct method is capable of detecting
distorted markers accurately and the recovery method with task-specific
learning has high robustness and generalizability on various data sets.
