Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery

Yanwu Xu; Lixin Duan; Huazhu Fu; Xiaoqin Zhang; Damon Wing Kee Wong; Baskaran Mani; Tin Aung; Jiang Liu

doi:10.1007/978-3-319-46726-9_51

Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery

Yanwu Xu
, Lixin Duan
, Huazhu Fu
, Xiaoqin Zhang
, Damon Wing Kee Wong
, Baskaran Mani
, Tin Aung
, Jiang Liu

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

We present a one-step approach based on low-rank tensor recovery for axial alignment in 360-degree anterior chamber optical coherence tomography. Achieving translational alignment and rotation correction of cross-sections simultaneously,this technique obtains a better anterior segment topographical representation and improves quantitative measurement accuracy and reproducibility of disease related parameters. Through its use of global information,the proposed method is more robust compared to using only individual or paired slices,and less sensitive to noise and motion artifacts. In angle closure analysis on 30 patient eyes,the preliminary results indicate that the proposed axial alignment method can not only facilitate manual qualitative analysis with more distinct landmark representation and much less human labor,but also can improve the accuracy of automatic quantitative assessment by 2.9%,which demonstrates that the proposed approach is promising for a wide range of clinical applications.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
Publisher	Springer Verlag
Pages	441-449
Number of pages	9
ISBN (Print)	9783319467252
DOIs	https://doi.org/10.1007/978-3-319-46726-9_51
Publication status	Published - 2016
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9902 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46726-9_51

Cite this

Xu, Y., Duan, L., Fu, H., Zhang, X., Wong, D. W. K., Mani, B., Aung, T., & Liu, J. (2016). Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery. In L. Joskowicz, M. R. Sabuncu, W. Wells, G. Unal, & S. Ourselin (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 441-449). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9902 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46726-9_51

Xu, Yanwu ; Duan, Lixin ; Fu, Huazhu et al. / Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Leo Joskowicz ; Mert R. Sabuncu ; William Wells ; Gozde Unal ; Sebastian Ourselin. Springer Verlag, 2016. pp. 441-449 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery",

abstract = "We present a one-step approach based on low-rank tensor recovery for axial alignment in 360-degree anterior chamber optical coherence tomography. Achieving translational alignment and rotation correction of cross-sections simultaneously,this technique obtains a better anterior segment topographical representation and improves quantitative measurement accuracy and reproducibility of disease related parameters. Through its use of global information,the proposed method is more robust compared to using only individual or paired slices,and less sensitive to noise and motion artifacts. In angle closure analysis on 30 patient eyes,the preliminary results indicate that the proposed axial alignment method can not only facilitate manual qualitative analysis with more distinct landmark representation and much less human labor,but also can improve the accuracy of automatic quantitative assessment by 2.9\%,which demonstrates that the proposed approach is promising for a wide range of clinical applications.",

author = "Yanwu Xu and Lixin Duan and Huazhu Fu and Xiaoqin Zhang and Wong, \{Damon Wing Kee\} and Baskaran Mani and Tin Aung and Jiang Liu",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",

year = "2016",

doi = "10.1007/978-3-319-46726-9\_51",

language = "English",

isbn = "9783319467252",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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address = "Germany",

}

Xu, Y, Duan, L, Fu, H, Zhang, X, Wong, DWK, Mani, B, Aung, T & Liu, J 2016, Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery. in L Joskowicz, MR Sabuncu, W Wells, G Unal & S Ourselin (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9902 LNCS, Springer Verlag, pp. 441-449. https://doi.org/10.1007/978-3-319-46726-9_51

Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery. / Xu, Yanwu; Duan, Lixin; Fu, Huazhu et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Leo Joskowicz; Mert R. Sabuncu; William Wells; Gozde Unal; Sebastian Ourselin. Springer Verlag, 2016. p. 441-449 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9902 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery

AU - Xu, Yanwu

AU - Duan, Lixin

AU - Fu, Huazhu

AU - Zhang, Xiaoqin

AU - Wong, Damon Wing Kee

AU - Mani, Baskaran

AU - Aung, Tin

AU - Liu, Jiang

PY - 2016

Y1 - 2016

N2 - We present a one-step approach based on low-rank tensor recovery for axial alignment in 360-degree anterior chamber optical coherence tomography. Achieving translational alignment and rotation correction of cross-sections simultaneously,this technique obtains a better anterior segment topographical representation and improves quantitative measurement accuracy and reproducibility of disease related parameters. Through its use of global information,the proposed method is more robust compared to using only individual or paired slices,and less sensitive to noise and motion artifacts. In angle closure analysis on 30 patient eyes,the preliminary results indicate that the proposed axial alignment method can not only facilitate manual qualitative analysis with more distinct landmark representation and much less human labor,but also can improve the accuracy of automatic quantitative assessment by 2.9%,which demonstrates that the proposed approach is promising for a wide range of clinical applications.

AB - We present a one-step approach based on low-rank tensor recovery for axial alignment in 360-degree anterior chamber optical coherence tomography. Achieving translational alignment and rotation correction of cross-sections simultaneously,this technique obtains a better anterior segment topographical representation and improves quantitative measurement accuracy and reproducibility of disease related parameters. Through its use of global information,the proposed method is more robust compared to using only individual or paired slices,and less sensitive to noise and motion artifacts. In angle closure analysis on 30 patient eyes,the preliminary results indicate that the proposed axial alignment method can not only facilitate manual qualitative analysis with more distinct landmark representation and much less human labor,but also can improve the accuracy of automatic quantitative assessment by 2.9%,which demonstrates that the proposed approach is promising for a wide range of clinical applications.

UR - https://www.scopus.com/pages/publications/84996525881

U2 - 10.1007/978-3-319-46726-9_51

DO - 10.1007/978-3-319-46726-9_51

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AN - SCOPUS:84996525881

SN - 9783319467252

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 441

EP - 449

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

A2 - Joskowicz, Leo

A2 - Sabuncu, Mert R.

A2 - Wells, William

A2 - Unal, Gozde

A2 - Ourselin, Sebastian

PB - Springer Verlag

ER -

Xu Y, Duan L, Fu H, Zhang X, Wong DWK, Mani B et al. Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery. In Joskowicz L, Sabuncu MR, Wells W, Unal G, Ourselin S, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 441-449. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46726-9_51

Axial alignment for anterior segment swept source optical coherence tomography via robust low-rank tensor recovery

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