A 2.5D colon wall flattening model for CT-based virtual colonoscopy

Huafeng Wang; Lihong Li; Hao Han; Rui Shi; Bowen Song; Hao Peng; Yan Liu; Xianfeng Gu; Yunhong Wang; Zhengrong Liang

doi:10.1007/978-3-319-02267-3_26

A 2.5D colon wall flattening model for CT-based virtual colonoscopy

Huafeng Wang
, Lihong Li
, Hao Han
, Rui Shi
, Bowen Song
, Hao Peng
, Yan Liu
, Xianfeng Gu
, Yunhong Wang
, Zhengrong Liang

Stony Brook University
Beihang University
City University of New York

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

1 Scopus citations

Abstract

Conformal mapping for Computed Tomography Colonography(CTC) provides a two-dimensional (2D) representations for the original three-dimensional (3D) colon wall. Based on the flattening results of the colon, efforts have been devoted toward its applications for some medical uses, such as colon registration, Taniae Coli (TC) detection and Haustral folds segmentation, and so on. Though, the previously-used conformal mapping-based flattening methods can preserve the angle or area on the wall, the 2D flattening result still limits itself to provide more accurate information contained on the original colon wall due to its' lack of the undulating topography. In view of this limitation of the 2D flattening model, a novelty 2.5D approach was proposed in this paper. The new approach was tested for two of the many applications, i.e., the detections of Haustral folds and TCs. Experimental results revealed its validity in these applications.

Original language	English
Title of host publication	Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings
Publisher	Springer Verlag
Pages	203-210
Number of pages	8
ISBN (Print)	9783319022666
DOIs	https://doi.org/10.1007/978-3-319-02267-3_26
State	Published - 2013
Event	4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: Sep 22 2013 → Sep 22 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8184 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	09/22/13 → 09/22/13

Keywords

2.5D representation
colon wall
Computed Tomography Colonography
Conformal mapping
medical imaging

Access to Document

10.1007/978-3-319-02267-3_26

Cite this

Wang, H., Li, L., Han, H., Shi, R., Song, B., Peng, H., Liu, Y., Gu, X., Wang, Y., & Liang, Z. (2013). A 2.5D colon wall flattening model for CT-based virtual colonoscopy. In Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings (pp. 203-210). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8184 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-02267-3_26

Wang, Huafeng ; Li, Lihong ; Han, Hao et al. / A 2.5D colon wall flattening model for CT-based virtual colonoscopy. Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings. Springer Verlag, 2013. pp. 203-210 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{36dca5bf9d164cef8816f35429a7a561,

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abstract = "Conformal mapping for Computed Tomography Colonography(CTC) provides a two-dimensional (2D) representations for the original three-dimensional (3D) colon wall. Based on the flattening results of the colon, efforts have been devoted toward its applications for some medical uses, such as colon registration, Taniae Coli (TC) detection and Haustral folds segmentation, and so on. Though, the previously-used conformal mapping-based flattening methods can preserve the angle or area on the wall, the 2D flattening result still limits itself to provide more accurate information contained on the original colon wall due to its' lack of the undulating topography. In view of this limitation of the 2D flattening model, a novelty 2.5D approach was proposed in this paper. The new approach was tested for two of the many applications, i.e., the detections of Haustral folds and TCs. Experimental results revealed its validity in these applications.",

keywords = "2.5D representation, colon wall, Computed Tomography Colonography, Conformal mapping, medical imaging",

author = "Huafeng Wang and Lihong Li and Hao Han and Rui Shi and Bowen Song and Hao Peng and Yan Liu and Xianfeng Gu and Yunhong Wang and Zhengrong Liang",

year = "2013",

doi = "10.1007/978-3-319-02267-3\_26",

language = "English",

isbn = "9783319022666",

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

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booktitle = "Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings",

note = "4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 22-09-2013",

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Wang, H, Li, L, Han, H, Shi, R, Song, B, Peng, H, Liu, Y, Gu, X, Wang, Y & Liang, Z 2013, A 2.5D colon wall flattening model for CT-based virtual colonoscopy. in Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8184 LNCS, Springer Verlag, pp. 203-210, 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 09/22/13. https://doi.org/10.1007/978-3-319-02267-3_26

A 2.5D colon wall flattening model for CT-based virtual colonoscopy. / Wang, Huafeng; Li, Lihong; Han, Hao et al.
Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings. Springer Verlag, 2013. p. 203-210 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8184 LNCS).

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

TY - GEN

T1 - A 2.5D colon wall flattening model for CT-based virtual colonoscopy

AU - Wang, Huafeng

AU - Li, Lihong

AU - Han, Hao

AU - Shi, Rui

AU - Song, Bowen

AU - Peng, Hao

AU - Liu, Yan

AU - Gu, Xianfeng

AU - Wang, Yunhong

AU - Liang, Zhengrong

PY - 2013

Y1 - 2013

N2 - Conformal mapping for Computed Tomography Colonography(CTC) provides a two-dimensional (2D) representations for the original three-dimensional (3D) colon wall. Based on the flattening results of the colon, efforts have been devoted toward its applications for some medical uses, such as colon registration, Taniae Coli (TC) detection and Haustral folds segmentation, and so on. Though, the previously-used conformal mapping-based flattening methods can preserve the angle or area on the wall, the 2D flattening result still limits itself to provide more accurate information contained on the original colon wall due to its' lack of the undulating topography. In view of this limitation of the 2D flattening model, a novelty 2.5D approach was proposed in this paper. The new approach was tested for two of the many applications, i.e., the detections of Haustral folds and TCs. Experimental results revealed its validity in these applications.

AB - Conformal mapping for Computed Tomography Colonography(CTC) provides a two-dimensional (2D) representations for the original three-dimensional (3D) colon wall. Based on the flattening results of the colon, efforts have been devoted toward its applications for some medical uses, such as colon registration, Taniae Coli (TC) detection and Haustral folds segmentation, and so on. Though, the previously-used conformal mapping-based flattening methods can preserve the angle or area on the wall, the 2D flattening result still limits itself to provide more accurate information contained on the original colon wall due to its' lack of the undulating topography. In view of this limitation of the 2D flattening model, a novelty 2.5D approach was proposed in this paper. The new approach was tested for two of the many applications, i.e., the detections of Haustral folds and TCs. Experimental results revealed its validity in these applications.

KW - 2.5D representation

KW - colon wall

KW - Computed Tomography Colonography

KW - Conformal mapping

KW - medical imaging

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DO - 10.1007/978-3-319-02267-3_26

M3 - Conference contribution

AN - SCOPUS:84886738258

SN - 9783319022666

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

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BT - Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings

PB - Springer Verlag

T2 - 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013

Y2 - 22 September 2013 through 22 September 2013

ER -

Wang H, Li L, Han H, Shi R, Song B, Peng H et al. A 2.5D colon wall flattening model for CT-based virtual colonoscopy. In Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings. Springer Verlag. 2013. p. 203-210. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-02267-3_26

A 2.5D colon wall flattening model for CT-based virtual colonoscopy

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Keywords

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