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Hand-eye calibration for rigid laparoscopes using an invariant point.

Identifieur interne : 000071 ( PubMed/Corpus ); précédent : 000070; suivant : 000072

Hand-eye calibration for rigid laparoscopes using an invariant point.

Auteurs : Stephen Thompson ; Danail Stoyanov ; Crispin Schneider ; Kurinchi Gurusamy ; Sébastien Ourselin ; Brian Davidson ; David Hawkes ; Matthew J. Clarkson

Source :

RBID : pubmed:26995597

Abstract

Laparoscopic liver resection has significant advantages over open surgery due to less patient trauma and faster recovery times, yet it can be difficult due to the restricted field of view and lack of haptic feedback. Image guidance provides a potential solution but one current challenge is in accurate "hand-eye" calibration, which determines the position and orientation of the laparoscope camera relative to the tracking markers.

DOI: 10.1007/s11548-016-1364-9
PubMed: 26995597

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pubmed:26995597

Le document en format XML

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<name sortKey="Stoyanov, Danail" sort="Stoyanov, Danail" uniqKey="Stoyanov D" first="Danail" last="Stoyanov">Danail Stoyanov</name>
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<name sortKey="Schneider, Crispin" sort="Schneider, Crispin" uniqKey="Schneider C" first="Crispin" last="Schneider">Crispin Schneider</name>
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<title level="j">International journal of computer assisted radiology and surgery</title>
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<div type="abstract" xml:lang="en">Laparoscopic liver resection has significant advantages over open surgery due to less patient trauma and faster recovery times, yet it can be difficult due to the restricted field of view and lack of haptic feedback. Image guidance provides a potential solution but one current challenge is in accurate "hand-eye" calibration, which determines the position and orientation of the laparoscope camera relative to the tracking markers.</div>
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<Title>International journal of computer assisted radiology and surgery</Title>
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<ArticleTitle>Hand-eye calibration for rigid laparoscopes using an invariant point.</ArticleTitle>
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<AbstractText Label="PURPOSE" NlmCategory="OBJECTIVE">Laparoscopic liver resection has significant advantages over open surgery due to less patient trauma and faster recovery times, yet it can be difficult due to the restricted field of view and lack of haptic feedback. Image guidance provides a potential solution but one current challenge is in accurate "hand-eye" calibration, which determines the position and orientation of the laparoscope camera relative to the tracking markers.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">In this paper, we propose a simple and clinically feasible calibration method based on a single invariant point. The method requires no additional hardware, can be constructed by theatre staff during surgical setup, requires minimal image processing and can be visualised in real time. Real-time visualisation allows the surgical team to assess the calibration accuracy before use in surgery. In addition, in the laboratory, we have developed a laparoscope with an electromagnetic tracking sensor attached to the camera end and an optical tracking marker attached to the distal end. This enables a comparison of tracking performance.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">We have evaluated our method in the laboratory and compared it to two widely used methods, "Tsai's method" and "direct" calibration. The new method is of comparable accuracy to existing methods, and we show RMS projected error due to calibration of 1.95 mm for optical tracking and 0.85 mm for EM tracking, versus 4.13 and 1.00 mm respectively, using existing methods. The new method has also been shown to be workable under sterile conditions in the operating room.</AbstractText>
<AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We have proposed a new method of hand-eye calibration, based on a single invariant point. Initial experience has shown that the method provides visual feedback, satisfactory accuracy and can be performed during surgery. We also show that an EM sensor placed near the camera would provide significantly improved image overlay accuracy.</AbstractText>
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<Keyword MajorTopicYN="N">Augmented reality</Keyword>
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