Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation

Amélie Grall; Azam Hamidinekoo; Paul Malcolm; Reyer Zwiggelaar

doi:10.1007/978-3-030-39343-4_2

Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation

Amélie Grall
, Azam Hamidinekoo
, Paul Malcolm
, Reyer Zwiggelaar

Adran Cyfrifiadureg

Prifysgol Aberystwyth | Aberystwyth University
Norfolk and Norwich University Hospitals NHS Foundation Trust

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (ISBN)

6 Dyfyniadau (Scopus)

Crynodeb

Prostate cancer is the second most commonly diagnosed cancer among men and currently multi-parametric MRI is a promising imaging technique used for clinical workup of prostate cancer. Accurate detection and localisation of the prostate tissue boundary on various MRI scans can be helpful for obtaining a region of interest for Computer Aided Diagnosis systems. In this paper, we present a fully automated detection and segmentation pipeline using a conditional Generative Adversarial Network (cGAN). We investigated the robustness of the cGAN model against adding Gaussian noise or removing noise from the training data. Based on the detection and segmentation metrics, de-noising did not show a significant improvement. However, by including noisy images in the training data, the detection and segmentation performance was improved in each 3D modality, which resulted in comparable to state-of-the-art results.

Iaith wreiddiol	Saesneg
Teitl	Medical Image Understanding and Analysis
Is-deitl	23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings
Golygyddion	Yalin Zheng, Bryan M. Williams, Ke Chen
Cyhoeddwr	Springer Nature
Tudalennau	15-25
Nifer y tudalennau	11
ISBN (Electronig)	978-3-030-39343-4
ISBN (Argraffiad)	978-3-030-39342-7
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-3-030-39343-4_2
Statws	Cyhoeddwyd - 24 Ion 2020
Digwyddiad	Proceedings 23rd Conference on Medical Image Understanding and Analysis - University of Liverpool, Liverpool, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon Hyd: 24 Gorff 2019 → 26 Gorff 2019

Cyfres gyhoeddiadau

Enw	Communications in Computer and Information Science
Cyhoeddwr	Springer Nature
Cyfrol	1065
ISSN (Argraffiad)	1865-0929
ISSN (Electronig)	1865-0937

Cynhadledd

Cynhadledd	Proceedings 23rd Conference on Medical Image Understanding and Analysis
Teitl cryno	MIUA 2019
Gwlad/Tiriogaeth	Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Dinas	Liverpool
Cyfnod	24 Gorff 2019 → 26 Gorff 2019

NDC y CU

Mae’r allbwn hwn yn cyfrannu at y Nod(au) Datblygu Cynaliadwy canlynol

NDC 3 Iechyd a Llesiant Da

Mynediad at Ddogfen

10.1007/978-3-030-39343-4_2

Cysylltiad parhaol

Dolen barhaus

Ffeiliau eraill a chysylltiadau

Dolen i’r cyhoeddiad yn Scopus

Dyfynnu hyn

Grall, A., Hamidinekoo, A., Malcolm, P., & Zwiggelaar, R. (2020). Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation. Yn Y. Zheng, B. M. Williams, & K. Chen (Gol.), Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings (tt. 15-25). (Communications in Computer and Information Science; Cyfrol 1065). Springer Nature. https://doi.org/10.1007/978-3-030-39343-4_2

Grall, Amélie ; Hamidinekoo, Azam ; Malcolm, Paul et al. / Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation. Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. Gol. / Yalin Zheng ; Bryan M. Williams ; Ke Chen. Springer Nature, 2020. tt. 15-25 (Communications in Computer and Information Science).

@inproceedings{7bdba6ed3ff34eb49aceccdaae870057,

title = "Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation",

abstract = "Prostate cancer is the second most commonly diagnosed cancer among men and currently multi-parametric MRI is a promising imaging technique used for clinical workup of prostate cancer. Accurate detection and localisation of the prostate tissue boundary on various MRI scans can be helpful for obtaining a region of interest for Computer Aided Diagnosis systems. In this paper, we present a fully automated detection and segmentation pipeline using a conditional Generative Adversarial Network (cGAN). We investigated the robustness of the cGAN model against adding Gaussian noise or removing noise from the training data. Based on the detection and segmentation metrics, de-noising did not show a significant improvement. However, by including noisy images in the training data, the detection and segmentation performance was improved in each 3D modality, which resulted in comparable to state-of-the-art results.",

keywords = "Prostate MRI, Computer Aided Diagnosis, Segmentation, Detection, Generative Adversarial Network",

author = "Am{\'e}lie Grall and Azam Hamidinekoo and Paul Malcolm and Reyer Zwiggelaar",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Proceedings 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 ; Conference date: 24-07-2019 Through 26-07-2019",

year = "2020",

month = jan,

day = "24",

doi = "10.1007/978-3-030-39343-4\_2",

language = "English",

isbn = "978-3-030-39342-7",

series = "Communications in Computer and Information Science",

publisher = "Springer Nature",

pages = "15--25",

editor = "Yalin Zheng and Williams, \{Bryan M.\} and Ke Chen",

booktitle = "Medical Image Understanding and Analysis",

address = "Switzerland",

}

Grall, A, Hamidinekoo, A, Malcolm, P & Zwiggelaar, R 2020, Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation. yn Y Zheng, BM Williams & K Chen (gol.), Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. Communications in Computer and Information Science, cyfrol. 1065, Springer Nature, tt. 15-25, Proceedings 23rd Conference on Medical Image Understanding and Analysis, Liverpool, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon, 24 Gorff 2019. https://doi.org/10.1007/978-3-030-39343-4_2

Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation. / Grall, Amélie; Hamidinekoo, Azam; Malcolm, Paul et al.
Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. gol. / Yalin Zheng; Bryan M. Williams; Ke Chen. Springer Nature, 2020. t. 15-25 (Communications in Computer and Information Science; Cyfrol 1065).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (ISBN)

TY - GEN

T1 - Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation

AU - Grall, Amélie

AU - Hamidinekoo, Azam

AU - Malcolm, Paul

AU - Zwiggelaar, Reyer

PY - 2020/1/24

Y1 - 2020/1/24

N2 - Prostate cancer is the second most commonly diagnosed cancer among men and currently multi-parametric MRI is a promising imaging technique used for clinical workup of prostate cancer. Accurate detection and localisation of the prostate tissue boundary on various MRI scans can be helpful for obtaining a region of interest for Computer Aided Diagnosis systems. In this paper, we present a fully automated detection and segmentation pipeline using a conditional Generative Adversarial Network (cGAN). We investigated the robustness of the cGAN model against adding Gaussian noise or removing noise from the training data. Based on the detection and segmentation metrics, de-noising did not show a significant improvement. However, by including noisy images in the training data, the detection and segmentation performance was improved in each 3D modality, which resulted in comparable to state-of-the-art results.

AB - Prostate cancer is the second most commonly diagnosed cancer among men and currently multi-parametric MRI is a promising imaging technique used for clinical workup of prostate cancer. Accurate detection and localisation of the prostate tissue boundary on various MRI scans can be helpful for obtaining a region of interest for Computer Aided Diagnosis systems. In this paper, we present a fully automated detection and segmentation pipeline using a conditional Generative Adversarial Network (cGAN). We investigated the robustness of the cGAN model against adding Gaussian noise or removing noise from the training data. Based on the detection and segmentation metrics, de-noising did not show a significant improvement. However, by including noisy images in the training data, the detection and segmentation performance was improved in each 3D modality, which resulted in comparable to state-of-the-art results.

KW - Prostate MRI

KW - Computer Aided Diagnosis

KW - Segmentation

KW - Detection

KW - Generative Adversarial Network

UR - https://www.scopus.com/pages/publications/85079091149

U2 - 10.1007/978-3-030-39343-4_2

DO - 10.1007/978-3-030-39343-4_2

M3 - Conference Proceeding (ISBN)

SN - 978-3-030-39342-7

T3 - Communications in Computer and Information Science

SP - 15

EP - 25

BT - Medical Image Understanding and Analysis

A2 - Zheng, Yalin

A2 - Williams, Bryan M.

A2 - Chen, Ke

PB - Springer Nature

T2 - Proceedings 23rd Conference on Medical Image Understanding and Analysis

Y2 - 24 July 2019 through 26 July 2019

ER -

Grall A, Hamidinekoo A, Malcolm P, Zwiggelaar R. Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation. Yn Zheng Y, Williams BM, Chen K, golygyddion, Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. Springer Nature. 2020. t. 15-25. (Communications in Computer and Information Science). Epub 2020 Ion 23. doi: 10.1007/978-3-030-39343-4_2

Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation

Crynodeb

Cyfres gyhoeddiadau

Cynhadledd

NDC y CU

Mynediad at Ddogfen

Cysylltiad parhaol

Ffeiliau eraill a chysylltiadau

Ôl bys

Dyfynnu hyn