Learning Expected Appearances for Intraoperative Registration during
  Neurosurgery
Nazim Haouchine Reuben Dorent Parikshit Juvekar Erickson Torio William M. Wells III Tina Kapur Alexandra J. Golby Sarah Frisken
We present a novel method for intraoperative patient-to-image registration by
learning Expected Appearances. Our method uses preoperative imaging to
synthesize patient-specific expected views through a surgical microscope for a
predicted range of transformations. Our method estimates the camera pose by
minimizing the dissimilarity between the intraoperative 2D view through the
optical microscope and the synthesized expected texture. In contrast to
conventional methods, our approach transfers the processing tasks to the
preoperative stage, reducing thereby the impact of low-resolution, distorted,
and noisy intraoperative images, that often degrade the registration accuracy.
We applied our method in the context of neuronavigation during brain surgery.
We evaluated our approach on synthetic data and on retrospective data from 6
clinical cases. Our method outperformed state-of-the-art methods and achieved
accuracies that met current clinical standards.