Automatic 3D modelling for prostate cancer brachytherapy

Mohammad Ali Jan Ghasab; Andrew P. Paplinski; John M. Betts; Hayley M. Reynolds; Annette Haworth

doi:10.1109/ICIP.2017.8297124

Automatic 3D modelling for prostate cancer brachytherapy

Mohammad Ali Jan Ghasab, Andrew P. Paplinski, John M. Betts, Hayley M. Reynolds, Annette Haworth

Department of Data Science & AI

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

3 Citations (Scopus)

Abstract

3D visualisation is being used increasingly to improve the accuracy of biopsies and seed placement for prostate cancer brachytherapy (the implantation of small radioactive 'seeds' for radiotherapy). The construction of a 3D model requires the segmentation of 2D Magnetic Resonance (MR) images, which remains a challenging problem. The current practice of manual segmentation requires a very high level of expertise and is time-consuming. In this paper, we propose a new method for creating a 3D model of the prostate from MR images obtained pre-operatively. To do this, an Active Appearance Model (AAM) is created from the image data by grouping the MR images based on the region of the prostate each is taken from. A fast optimisation algorithm is then used to segment a new prostate using the AAM. Our results show that this method is fast and accurate compared with manual segmentation enabling this method to be used intra-operatively.

Original language	English
Title of host publication	2017 IEEE International Conference on Image Processing - Proceedings
Subtitle of host publication	17–20 September 2017 China National Convention Center Beijing, China
Editors	Jiebo Luo, Wenjun Zeng, Yu-Jin Zhang
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	4452-4456
Number of pages	5
ISBN (Electronic)	9781509021758
ISBN (Print)	9781509021765, 9781509021741
DOIs	https://doi.org/10.1109/ICIP.2017.8297124
Publication status	Published - 2017
Event	IEEE International Conference on Image Processing 2017 - China National Convention Center (CNCC), Beijing, China Duration: 17 Sept 2017 → 20 Sept 2017 Conference number: 24th http://www.2017.ieeeicip.org/ http://2017.ieeeicip.org/ https://ieeexplore.ieee.org/xpl/conhome/8267582/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Image Processing 2017
Abbreviated title	ICIP 2017
Country/Territory	China
City	Beijing
Period	17/09/17 → 20/09/17
Internet address	http://www.2017.ieeeicip.org/ http://2017.ieeeicip.org/ https://ieeexplore.ieee.org/xpl/conhome/8267582/proceeding (Proceedings)

Keywords

Medical Image Analysis
Prostate Modelling
Segmentation
Visualisation

UN SDGs

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

Access to Document

10.1109/ICIP.2017.8297124

Cite this

Ghasab, M. A. J., Paplinski, A. P., Betts, J. M., Reynolds, H. M., & Haworth, A. (2017). Automatic 3D modelling for prostate cancer brachytherapy. In J. Luo, W. Zeng, & Y.-J. Zhang (Eds.), 2017 IEEE International Conference on Image Processing - Proceedings: 17–20 September 2017 China National Convention Center Beijing, China (pp. 4452-4456). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICIP.2017.8297124

Ghasab, Mohammad Ali Jan ; Paplinski, Andrew P. ; Betts, John M. et al. / Automatic 3D modelling for prostate cancer brachytherapy. 2017 IEEE International Conference on Image Processing - Proceedings: 17–20 September 2017 China National Convention Center Beijing, China. editor / Jiebo Luo ; Wenjun Zeng ; Yu-Jin Zhang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 4452-4456

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title = "Automatic 3D modelling for prostate cancer brachytherapy",

abstract = "3D visualisation is being used increasingly to improve the accuracy of biopsies and seed placement for prostate cancer brachytherapy (the implantation of small radioactive 'seeds' for radiotherapy). The construction of a 3D model requires the segmentation of 2D Magnetic Resonance (MR) images, which remains a challenging problem. The current practice of manual segmentation requires a very high level of expertise and is time-consuming. In this paper, we propose a new method for creating a 3D model of the prostate from MR images obtained pre-operatively. To do this, an Active Appearance Model (AAM) is created from the image data by grouping the MR images based on the region of the prostate each is taken from. A fast optimisation algorithm is then used to segment a new prostate using the AAM. Our results show that this method is fast and accurate compared with manual segmentation enabling this method to be used intra-operatively.",

keywords = "Medical Image Analysis, Prostate Modelling, Segmentation, Visualisation",

author = "Ghasab, {Mohammad Ali Jan} and Paplinski, {Andrew P.} and Betts, {John M.} and Reynolds, {Hayley M.} and Annette Haworth",

year = "2017",

doi = "10.1109/ICIP.2017.8297124",

language = "English",

isbn = "9781509021765",

pages = "4452--4456",

editor = "Jiebo Luo and Zeng, {Wenjun } and Zhang, {Yu-Jin }",

booktitle = "2017 IEEE International Conference on Image Processing - Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE International Conference on Image Processing 2017, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",

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Ghasab, MAJ, Paplinski, AP, Betts, JM, Reynolds, HM & Haworth, A 2017, Automatic 3D modelling for prostate cancer brachytherapy. in J Luo, W Zeng & Y-J Zhang (eds), 2017 IEEE International Conference on Image Processing - Proceedings: 17–20 September 2017 China National Convention Center Beijing, China. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 4452-4456, IEEE International Conference on Image Processing 2017, Beijing, China, 17/09/17. https://doi.org/10.1109/ICIP.2017.8297124

Automatic 3D modelling for prostate cancer brachytherapy. / Ghasab, Mohammad Ali Jan; Paplinski, Andrew P.; Betts, John M. et al.
2017 IEEE International Conference on Image Processing - Proceedings: 17–20 September 2017 China National Convention Center Beijing, China. ed. / Jiebo Luo; Wenjun Zeng; Yu-Jin Zhang. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 4452-4456.

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

TY - GEN

T1 - Automatic 3D modelling for prostate cancer brachytherapy

AU - Ghasab, Mohammad Ali Jan

AU - Paplinski, Andrew P.

AU - Betts, John M.

AU - Reynolds, Hayley M.

AU - Haworth, Annette

N1 - Conference code: 24th

PY - 2017

Y1 - 2017

N2 - 3D visualisation is being used increasingly to improve the accuracy of biopsies and seed placement for prostate cancer brachytherapy (the implantation of small radioactive 'seeds' for radiotherapy). The construction of a 3D model requires the segmentation of 2D Magnetic Resonance (MR) images, which remains a challenging problem. The current practice of manual segmentation requires a very high level of expertise and is time-consuming. In this paper, we propose a new method for creating a 3D model of the prostate from MR images obtained pre-operatively. To do this, an Active Appearance Model (AAM) is created from the image data by grouping the MR images based on the region of the prostate each is taken from. A fast optimisation algorithm is then used to segment a new prostate using the AAM. Our results show that this method is fast and accurate compared with manual segmentation enabling this method to be used intra-operatively.

AB - 3D visualisation is being used increasingly to improve the accuracy of biopsies and seed placement for prostate cancer brachytherapy (the implantation of small radioactive 'seeds' for radiotherapy). The construction of a 3D model requires the segmentation of 2D Magnetic Resonance (MR) images, which remains a challenging problem. The current practice of manual segmentation requires a very high level of expertise and is time-consuming. In this paper, we propose a new method for creating a 3D model of the prostate from MR images obtained pre-operatively. To do this, an Active Appearance Model (AAM) is created from the image data by grouping the MR images based on the region of the prostate each is taken from. A fast optimisation algorithm is then used to segment a new prostate using the AAM. Our results show that this method is fast and accurate compared with manual segmentation enabling this method to be used intra-operatively.

KW - Medical Image Analysis

KW - Prostate Modelling

KW - Segmentation

KW - Visualisation

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U2 - 10.1109/ICIP.2017.8297124

DO - 10.1109/ICIP.2017.8297124

M3 - Conference Paper

AN - SCOPUS:85045310816

SN - 9781509021765

SN - 9781509021741

SP - 4452

EP - 4456

BT - 2017 IEEE International Conference on Image Processing - Proceedings

A2 - Luo, Jiebo

A2 - Zeng, Wenjun

A2 - Zhang, Yu-Jin

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE International Conference on Image Processing 2017

Y2 - 17 September 2017 through 20 September 2017

ER -

Ghasab MAJ, Paplinski AP, Betts JM, Reynolds HM, Haworth A. Automatic 3D modelling for prostate cancer brachytherapy. In Luo J, Zeng W, Zhang YJ, editors, 2017 IEEE International Conference on Image Processing - Proceedings: 17–20 September 2017 China National Convention Center Beijing, China. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 4452-4456 doi: 10.1109/ICIP.2017.8297124

Automatic 3D modelling for prostate cancer brachytherapy

Abstract

Conference

Keywords

UN SDGs

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