Residual semantic segmentation of the prostate from magnetic resonance images

Md Sazzad Hossain; Andrew P. Paplinski; John M. Betts

doi:10.1007/978-3-030-04239-4_46

Residual semantic segmentation of the prostate from magnetic resonance images

Md Sazzad Hossain, Andrew P. Paplinski, John M. Betts

Department of Data Science & AI

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

9 Citations (Scopus)

Abstract

The diagnosis and treatment of prostate cancer requires the accurate segmentation of the prostate in Magnetic Resonance Images (MRI). Manual segmentation is currently the most accurate method of performing this task. However, this requires specialist knowledge, and is time consuming. To overcome these limitations, we demonstrate an automatic segmentation of the prostate region in MRI images using a VGG19-based fully convolutional neural network. This new network, VGG19RSeg, identifies a region of interest in the image using semantic segmentation, that is, a pixel-wise classification of the content of the input image. Although several studies have applied fully convolutional neural networks to medical image segmentation tasks, our study introduces two new forms of residual connections (remote and neighbouring) which increases the accuracy of segmentation over the basic architecture. Our results, using this new architecture, show that the proposed VGG19RSeg can achieve a mean Dice Similarity Coefficient of 94.57%, making it more accurate than comparable methods reported in the literature.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part VII
Editors	Long Cheng, Andrew Chi Sing Leung, Seiichi Ozawa
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	510-521
Number of pages	12
ISBN (Electronic)	9783030042394
ISBN (Print)	9783030042387
DOIs	https://doi.org/10.1007/978-3-030-04239-4_46
Publication status	Published - 2018
Event	International Conference on Neural Information Processing 2018 - Siem Reap, Cambodia Duration: 13 Dec 2018 → 16 Dec 2018 Conference number: 25th https://conference.cs.cityu.edu.hk/iconip/ https://link.springer.com/book/10.1007/978-3-030-04167-0 (Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11307
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Neural Information Processing 2018
Abbreviated title	ICONIP 2018
Country/Territory	Cambodia
City	Siem Reap
Period	13/12/18 → 16/12/18
Internet address	https://conference.cs.cityu.edu.hk/iconip/ https://link.springer.com/book/10.1007/978-3-030-04167-0 (Proceedings)

Keywords

Deep convolutional neural networks
MRI images
Prostate
Semantic segmentation

UN SDGs

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

Access to Document

10.1007/978-3-030-04239-4_46

Cite this

Hossain, M. S., Paplinski, A. P., & Betts, J. M. (2018). Residual semantic segmentation of the prostate from magnetic resonance images. In L. Cheng, A. C. S. Leung, & S. Ozawa (Eds.), Neural Information Processing: 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part VII (pp. 510-521). (Lecture Notes in Computer Science ; Vol. 11307 ). Springer. https://doi.org/10.1007/978-3-030-04239-4_46

Hossain, Md Sazzad ; Paplinski, Andrew P. ; Betts, John M. / Residual semantic segmentation of the prostate from magnetic resonance images. Neural Information Processing: 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part VII. editor / Long Cheng ; Andrew Chi Sing Leung ; Seiichi Ozawa. Cham Switzerland : Springer, 2018. pp. 510-521 (Lecture Notes in Computer Science ).

@inproceedings{b1c77d9da0f9421094ab54bdc9e8f1ad,

title = "Residual semantic segmentation of the prostate from magnetic resonance images",

abstract = "The diagnosis and treatment of prostate cancer requires the accurate segmentation of the prostate in Magnetic Resonance Images (MRI). Manual segmentation is currently the most accurate method of performing this task. However, this requires specialist knowledge, and is time consuming. To overcome these limitations, we demonstrate an automatic segmentation of the prostate region in MRI images using a VGG19-based fully convolutional neural network. This new network, VGG19RSeg, identifies a region of interest in the image using semantic segmentation, that is, a pixel-wise classification of the content of the input image. Although several studies have applied fully convolutional neural networks to medical image segmentation tasks, our study introduces two new forms of residual connections (remote and neighbouring) which increases the accuracy of segmentation over the basic architecture. Our results, using this new architecture, show that the proposed VGG19RSeg can achieve a mean Dice Similarity Coefficient of 94.57%, making it more accurate than comparable methods reported in the literature.",

keywords = "Deep convolutional neural networks, MRI images, Prostate, Semantic segmentation",

author = "Hossain, {Md Sazzad} and Paplinski, {Andrew P.} and Betts, {John M.}",

year = "2018",

doi = "10.1007/978-3-030-04239-4_46",

language = "English",

isbn = "9783030042387",

series = "Lecture Notes in Computer Science ",

publisher = "Springer",

pages = "510--521",

editor = "Long Cheng and Leung, {Andrew Chi Sing} and Seiichi Ozawa",

booktitle = "Neural Information Processing",

note = "International Conference on Neural Information Processing 2018, ICONIP 2018 ; Conference date: 13-12-2018 Through 16-12-2018",

url = "https://conference.cs.cityu.edu.hk/iconip/, https://link.springer.com/book/10.1007/978-3-030-04167-0",

}

Hossain, MS, Paplinski, AP & Betts, JM 2018, Residual semantic segmentation of the prostate from magnetic resonance images. in L Cheng, ACS Leung & S Ozawa (eds), Neural Information Processing: 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part VII. Lecture Notes in Computer Science , vol. 11307 , Springer, Cham Switzerland, pp. 510-521, International Conference on Neural Information Processing 2018, Siem Reap, Cambodia, 13/12/18. https://doi.org/10.1007/978-3-030-04239-4_46

Residual semantic segmentation of the prostate from magnetic resonance images. / Hossain, Md Sazzad; Paplinski, Andrew P.; Betts, John M.
Neural Information Processing: 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part VII. ed. / Long Cheng; Andrew Chi Sing Leung; Seiichi Ozawa. Cham Switzerland: Springer, 2018. p. 510-521 (Lecture Notes in Computer Science ; Vol. 11307 ).

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

TY - GEN

T1 - Residual semantic segmentation of the prostate from magnetic resonance images

AU - Hossain, Md Sazzad

AU - Paplinski, Andrew P.

AU - Betts, John M.

N1 - Conference code: 25th

PY - 2018

Y1 - 2018

N2 - The diagnosis and treatment of prostate cancer requires the accurate segmentation of the prostate in Magnetic Resonance Images (MRI). Manual segmentation is currently the most accurate method of performing this task. However, this requires specialist knowledge, and is time consuming. To overcome these limitations, we demonstrate an automatic segmentation of the prostate region in MRI images using a VGG19-based fully convolutional neural network. This new network, VGG19RSeg, identifies a region of interest in the image using semantic segmentation, that is, a pixel-wise classification of the content of the input image. Although several studies have applied fully convolutional neural networks to medical image segmentation tasks, our study introduces two new forms of residual connections (remote and neighbouring) which increases the accuracy of segmentation over the basic architecture. Our results, using this new architecture, show that the proposed VGG19RSeg can achieve a mean Dice Similarity Coefficient of 94.57%, making it more accurate than comparable methods reported in the literature.

AB - The diagnosis and treatment of prostate cancer requires the accurate segmentation of the prostate in Magnetic Resonance Images (MRI). Manual segmentation is currently the most accurate method of performing this task. However, this requires specialist knowledge, and is time consuming. To overcome these limitations, we demonstrate an automatic segmentation of the prostate region in MRI images using a VGG19-based fully convolutional neural network. This new network, VGG19RSeg, identifies a region of interest in the image using semantic segmentation, that is, a pixel-wise classification of the content of the input image. Although several studies have applied fully convolutional neural networks to medical image segmentation tasks, our study introduces two new forms of residual connections (remote and neighbouring) which increases the accuracy of segmentation over the basic architecture. Our results, using this new architecture, show that the proposed VGG19RSeg can achieve a mean Dice Similarity Coefficient of 94.57%, making it more accurate than comparable methods reported in the literature.

KW - Deep convolutional neural networks

KW - MRI images

KW - Prostate

KW - Semantic segmentation

UR - http://www.scopus.com/inward/record.url?scp=85059022877&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-04239-4_46

DO - 10.1007/978-3-030-04239-4_46

M3 - Conference Paper

AN - SCOPUS:85059022877

SN - 9783030042387

T3 - Lecture Notes in Computer Science

SP - 510

EP - 521

BT - Neural Information Processing

A2 - Cheng, Long

A2 - Leung, Andrew Chi Sing

A2 - Ozawa, Seiichi

PB - Springer

CY - Cham Switzerland

T2 - International Conference on Neural Information Processing 2018

Y2 - 13 December 2018 through 16 December 2018

ER -

Hossain MS, Paplinski AP, Betts JM. Residual semantic segmentation of the prostate from magnetic resonance images. In Cheng L, Leung ACS, Ozawa S, editors, Neural Information Processing: 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part VII. Cham Switzerland: Springer. 2018. p. 510-521. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-030-04239-4_46

Residual semantic segmentation of the prostate from magnetic resonance images

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Cite this