Background: Visualization of the vast placental vasculature is crucial in fetoscopic laser photocoagulation for twin-to-twin transfusion syndrome treatment. However, vasculature mosaic is challenging due to the fluctuating imaging conditions during fetoscopic surgery.
Method: A scene adaptive feature-based approach for image correspondence in free-hand endoscopic placental video is proposed. It contributes towards existing techniques by introducing a failure detection method based on statistical attributes of the feature distribution, and an updating mechanism that self-tunes parameters to recover from registration failures.
Results: Validations on endoscopic image sequences of a phantom and a monkey placenta are carried out to demonstrate mismatch recovery. In two 100-frame sequences, automatic self-tuned results improved by 8% compared with manual experience-based tuning and a slight 2.5% deterioration against exhaustive tuning (gold standard).
Conclusion: This scene-adaptive image correspondence approach, which is not restricted to a set of generalized parameters, is suitable for applications associated with dynamically changing imaging conditions. Copyright © 2015 John Wiley & Sons, Ltd.
Keywords: image correspondence; minimally invasive fetal endoscopic surgery; placental imaging.
Copyright © 2015 John Wiley & Sons, Ltd.