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Towards scene adaptive image correspondence for placental vasculature mosaic in computer assisted fetoscopic procedures
Author(s) -
Yang Liangjing,
Wang Junchen,
Ando Takehiro,
Kubota Akihiro,
Yamashita Hiromasa,
Sakuma Ichiro,
Chiba Toshio,
Kobayashi Etsuko
Publication year - 2016
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1700
Subject(s) - computer science , computer vision , artificial intelligence , feature (linguistics) , visualization , imaging phantom , image (mathematics) , frame (networking) , medicine , radiology , philosophy , telecommunications , linguistics
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.