Fast background removal method for 3D multi-channel deep tissue fluorescence imaging

Chenchen Li; Xiaowei Li; Hongji Cao; He Jiang; Xiaotie Deng; Danny Z. Chen; Lin Yang; Zhifeng Shao

doi:10.1007/978-3-319-66185-8_11

Fast background removal method for 3D multi-channel deep tissue fluorescence imaging

Chenchen Li
, Xiaowei Li
, Hongji Cao
, He Jiang
, Xiaotie Deng
, Danny Z. Chen
, Lin Yang
, Zhifeng Shao

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

The recent advances in tissue clearing and optical imaging have enabled us to obtain three-dimensional high-resolution images of various tissues. However, the severe background noise remains a major obstacle. In addition, there is an urgent need for fast background ground correction methods. In this paper, we present a fast background removal method for 3D multi-channel deep tissue fluorescence images, in which the objectives of different channels are well separated. We first conduct a window-based normalization to distinguish foreground signals from background noises in all channels. Then, we identify the pure background regions by conducting subtraction of images in different channels, which allow us to estimate the background noises of the whole images by interpolation. Experiments on real 3D datasets of mouse stomach show our method has superior performance and efficiency comparing with the current state-of-the-art background correction methods. © Springer International Publishing AG 2017.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
Publisher	Springer Verlag
Pages	92-99
Volume	10434 LNCS
ISBN (Print)	9783319661841
DOIs	https://doi.org/10.1007/978-3-319-66185-8_11
Publication status	Published - 2017
Externally published	Yes
Event	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 11 Sept 2017 → 13 Sept 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10434 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Place	Canada
City	Quebec City
Period	11/09/17 → 13/09/17

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

Acknowledgment. This work are partially supported by the National Natural Science Foundation of China (Grant No. 11426026, 61632017, 61173011) and a Project 985 grant of Shanghai Jiao Tong University and the National Science Foundation of China (Grant No. 11374207, 31370750, 31670722 and 81627801) and SJTU Cross-Disciplinary Research Fund in Medicine and Engineering (Grant No. YG2012MS23). D. Chen’s research was supported in part by NSF grants CCF-1217906 and CCF-1617735. The authors are grateful to the generous support by Nikon Instruments Co., Ltd. (Shanghai, China).

Research Keywords

Background removal
Fluorescence microscopy image
Multi-channel

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10.1007/978-3-319-66185-8_11

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Li, C., Li, X., Cao, H., Jiang, H., Deng, X., Chen, D. Z., Yang, L., & Shao, Z. (2017). Fast background removal method for 3D multi-channel deep tissue fluorescence imaging. In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (Vol. 10434 LNCS, pp. 92-99). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10434 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66185-8_11

Li, Chenchen ; Li, Xiaowei ; Cao, Hongji et al. / Fast background removal method for 3D multi-channel deep tissue fluorescence imaging. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Vol. 10434 LNCS Springer Verlag, 2017. pp. 92-99 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Fast background removal method for 3D multi-channel deep tissue fluorescence imaging",

abstract = "The recent advances in tissue clearing and optical imaging have enabled us to obtain three-dimensional high-resolution images of various tissues. However, the severe background noise remains a major obstacle. In addition, there is an urgent need for fast background ground correction methods. In this paper, we present a fast background removal method for 3D multi-channel deep tissue fluorescence images, in which the objectives of different channels are well separated. We first conduct a window-based normalization to distinguish foreground signals from background noises in all channels. Then, we identify the pure background regions by conducting subtraction of images in different channels, which allow us to estimate the background noises of the whole images by interpolation. Experiments on real 3D datasets of mouse stomach show our method has superior performance and efficiency comparing with the current state-of-the-art background correction methods. {\textcopyright} Springer International Publishing AG 2017.",

keywords = "Background removal, Fluorescence microscopy image, Multi-channel",

author = "Chenchen Li and Xiaowei Li and Hongji Cao and He Jiang and Xiaotie Deng and Chen, \{Danny Z.\} and Lin Yang and Zhifeng Shao",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 ; Conference date: 11-09-2017 Through 13-09-2017",

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Li, C, Li, X, Cao, H, Jiang, H, Deng, X, Chen, DZ, Yang, L & Shao, Z 2017, Fast background removal method for 3D multi-channel deep tissue fluorescence imaging. in Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. vol. 10434 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10434 LNCS, Springer Verlag, pp. 92-99, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 11/09/17. https://doi.org/10.1007/978-3-319-66185-8_11

Fast background removal method for 3D multi-channel deep tissue fluorescence imaging. / Li, Chenchen; Li, Xiaowei; Cao, Hongji et al.
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Vol. 10434 LNCS Springer Verlag, 2017. p. 92-99 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10434 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Fast background removal method for 3D multi-channel deep tissue fluorescence imaging

AU - Li, Chenchen

AU - Li, Xiaowei

AU - Cao, Hongji

AU - Jiang, He

AU - Deng, Xiaotie

AU - Chen, Danny Z.

AU - Yang, Lin

AU - Shao, Zhifeng

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2017

Y1 - 2017

N2 - The recent advances in tissue clearing and optical imaging have enabled us to obtain three-dimensional high-resolution images of various tissues. However, the severe background noise remains a major obstacle. In addition, there is an urgent need for fast background ground correction methods. In this paper, we present a fast background removal method for 3D multi-channel deep tissue fluorescence images, in which the objectives of different channels are well separated. We first conduct a window-based normalization to distinguish foreground signals from background noises in all channels. Then, we identify the pure background regions by conducting subtraction of images in different channels, which allow us to estimate the background noises of the whole images by interpolation. Experiments on real 3D datasets of mouse stomach show our method has superior performance and efficiency comparing with the current state-of-the-art background correction methods. © Springer International Publishing AG 2017.

AB - The recent advances in tissue clearing and optical imaging have enabled us to obtain three-dimensional high-resolution images of various tissues. However, the severe background noise remains a major obstacle. In addition, there is an urgent need for fast background ground correction methods. In this paper, we present a fast background removal method for 3D multi-channel deep tissue fluorescence images, in which the objectives of different channels are well separated. We first conduct a window-based normalization to distinguish foreground signals from background noises in all channels. Then, we identify the pure background regions by conducting subtraction of images in different channels, which allow us to estimate the background noises of the whole images by interpolation. Experiments on real 3D datasets of mouse stomach show our method has superior performance and efficiency comparing with the current state-of-the-art background correction methods. © Springer International Publishing AG 2017.

KW - Background removal

KW - Fluorescence microscopy image

KW - Multi-channel

UR - https://www.scopus.com/pages/publications/85029516357

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U2 - 10.1007/978-3-319-66185-8_11

DO - 10.1007/978-3-319-66185-8_11

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783319661841

VL - 10434 LNCS

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

SP - 92

EP - 99

BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings

PB - Springer Verlag

T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017

Y2 - 11 September 2017 through 13 September 2017

ER -

Li C, Li X, Cao H, Jiang H, Deng X, Chen DZ et al. Fast background removal method for 3D multi-channel deep tissue fluorescence imaging. In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Vol. 10434 LNCS. Springer Verlag. 2017. p. 92-99. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-66185-8_11

Fast background removal method for 3D multi-channel deep tissue fluorescence imaging

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Research Keywords

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