  Purpose: Stereo matching methods that enable depth estimation are crucial for
visualization enhancement applications in computer-assisted surgery (CAS).
Learning-based stereo matching methods are promising to predict accurate
results on laparoscopic images. However, they require a large amount of
training data, and their performance may be degraded due to domain shifts.
  Methods: Maintaining robustness and improving the accuracy of learning-based
methods are still open problems. To overcome the limitations of learning-based
methods, we propose a disparity refinement framework consisting of a local
disparity refinement method and a global disparity refinement method to improve
the results of learning-based stereo matching methods in a cross-domain
setting. Those learning-based stereo matching methods are pre-trained on a
large public dataset of natural images and are tested on two datasets of
laparoscopic images.
  Results: Qualitative and quantitative results suggest that our proposed
disparity framework can effectively refine disparity maps when they are
noise-corrupted on an unseen dataset, without compromising prediction accuracy
when the network can generalize well on an unseen dataset.
  Conclusion: Our proposed disparity refinement framework could work with
learning-based methods to achieve robust and accurate disparity prediction.
Yet, as a large laparoscopic dataset for training learning-based methods does
not exist and the generalization ability of networks remains to be improved,
the incorporation of the proposed disparity refinement framework into existing
networks will contribute to improving their overall accuracy and robustness
associated with depth estimation.
