Towards scene adaptive image correspondence for placental vasculature mosaic in computer assisted fetoscopic procedures
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‐...
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Published in | The international journal of medical robotics + computer assisted surgery Vol. 12; no. 3; pp. 375 - 386 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Blackwell Publishing Ltd
01.09.2016
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | Supporting info item istex:2650F7575167D3714FE6DC27DC9B2107EFA7A39F ArticleID:RCS1700 ark:/67375/WNG-MLZVN320-B ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1478-5951 1478-596X |
DOI: | 10.1002/rcs.1700 |