Learning Expected Appearances for Intraoperative Registration during Neurosurgery

TL;DR

This paper introduces a novel intraoperative registration method that synthesizes expected views from preoperative images to improve accuracy during brain surgery, reducing the impact of noisy intraoperative images.

Contribution

The proposed method predicts expected intraoperative views from preoperative data, enabling more robust registration by minimizing dissimilarity between real and synthesized images.

Findings

Outperforms state-of-the-art registration methods.

Achieves clinically acceptable accuracy levels.

Reduces impact of low-quality intraoperative images.

Abstract

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.