Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN

Shujaat Khan; Jaeyoung Huh; Jong Chul Ye

doi:10.1007/978-3-030-87722-4_15

Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN

Shujaat Khan^*
, Jaeyoung Huh
, Jong Chul Ye

^*Corresponding author for this work

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

6 Scopus citations

Abstract

Point-of-Care Ultrasound (POCUS) imaging can help efficient resource utilization by reducing the secondary care referrals, and work as an extension in physical examination. Recently, many methods were proposed to reduce the size and power consumption of the system while improving the visual quality, but hand-held POCUS devices still have inferior image contrast and spatial resolution compared to the high-end ultrasound systems. To address this, here we propose an efficient solution for contrast and resolution enhancement of hand-held POCUS images using unsupervised deep learning. In contrast to the existing CycleGAN approaches that have difficulty in improving both contrast and image resolutions, the proposed method mitigate the problem by decomposing the contrast transfer and resolution improvement through CycleGAN and self-supervised learning. Experimental results confirmed that our method is superior than the conventional approaches.

Original language	English
Title of host publication	Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings
Editors	Shadi Albarqouni, M. Jorge Cardoso, Qi Dou, Konstantinos Kamnitsas, Bishesh Khanal, Islem Rekik, Nicola Rieke, Debdoot Sheet, Sotirios Tsaftaris, Daguang Xu, Ziyue Xu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	158-167
Number of pages	10
ISBN (Print)	9783030877217
DOIs	https://doi.org/10.1007/978-3-030-87722-4_15
State	Published - 2021
Externally published	Yes
Event	3rd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the 1st MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: 27 Sep 2021 → 1 Oct 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12968 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the 1st MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	27/09/21 → 1/10/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

CycleGAN
Hand-held ultrasound
POCUS
Ultrasound imaging
Unsupervised learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-87722-4_15

Cite this

Khan, S., Huh, J., & Ye, J. C. (2021). Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN. In S. Albarqouni, M. J. Cardoso, Q. Dou, K. Kamnitsas, B. Khanal, I. Rekik, N. Rieke, D. Sheet, S. Tsaftaris, D. Xu, & Z. Xu (Eds.), Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings (pp. 158-167). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12968 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87722-4_15

Khan, Shujaat ; Huh, Jaeyoung ; Ye, Jong Chul. / Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN. Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings. editor / Shadi Albarqouni ; M. Jorge Cardoso ; Qi Dou ; Konstantinos Kamnitsas ; Bishesh Khanal ; Islem Rekik ; Nicola Rieke ; Debdoot Sheet ; Sotirios Tsaftaris ; Daguang Xu ; Ziyue Xu. Springer Science and Business Media Deutschland GmbH, 2021. pp. 158-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f468e37982814086b0b1f5d64731b8e5,

title = "Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN",

abstract = "Point-of-Care Ultrasound (POCUS) imaging can help efficient resource utilization by reducing the secondary care referrals, and work as an extension in physical examination. Recently, many methods were proposed to reduce the size and power consumption of the system while improving the visual quality, but hand-held POCUS devices still have inferior image contrast and spatial resolution compared to the high-end ultrasound systems. To address this, here we propose an efficient solution for contrast and resolution enhancement of hand-held POCUS images using unsupervised deep learning. In contrast to the existing CycleGAN approaches that have difficulty in improving both contrast and image resolutions, the proposed method mitigate the problem by decomposing the contrast transfer and resolution improvement through CycleGAN and self-supervised learning. Experimental results confirmed that our method is superior than the conventional approaches.",

keywords = "CycleGAN, Hand-held ultrasound, POCUS, Ultrasound imaging, Unsupervised learning",

author = "Shujaat Khan and Jaeyoung Huh and Ye, \{Jong Chul\}",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 3rd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the 1st MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 01-10-2021",

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doi = "10.1007/978-3-030-87722-4\_15",

language = "English",

isbn = "9783030877217",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "158--167",

editor = "Shadi Albarqouni and Cardoso, \{M. Jorge\} and Qi Dou and Konstantinos Kamnitsas and Bishesh Khanal and Islem Rekik and Nicola Rieke and Debdoot Sheet and Sotirios Tsaftaris and Daguang Xu and Ziyue Xu",

booktitle = "Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings",

address = "Germany",

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Khan, S, Huh, J & Ye, JC 2021, Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN. in S Albarqouni, MJ Cardoso, Q Dou, K Kamnitsas, B Khanal, I Rekik, N Rieke, D Sheet, S Tsaftaris, D Xu & Z Xu (eds), Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12968 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 158-167, 3rd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the 1st MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 27/09/21. https://doi.org/10.1007/978-3-030-87722-4_15

Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN. / Khan, Shujaat; Huh, Jaeyoung; Ye, Jong Chul.
Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings. ed. / Shadi Albarqouni; M. Jorge Cardoso; Qi Dou; Konstantinos Kamnitsas; Bishesh Khanal; Islem Rekik; Nicola Rieke; Debdoot Sheet; Sotirios Tsaftaris; Daguang Xu; Ziyue Xu. Springer Science and Business Media Deutschland GmbH, 2021. p. 158-167 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12968 LNCS).

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

TY - GEN

T1 - Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN

AU - Khan, Shujaat

AU - Huh, Jaeyoung

AU - Ye, Jong Chul

PY - 2021

Y1 - 2021

N2 - Point-of-Care Ultrasound (POCUS) imaging can help efficient resource utilization by reducing the secondary care referrals, and work as an extension in physical examination. Recently, many methods were proposed to reduce the size and power consumption of the system while improving the visual quality, but hand-held POCUS devices still have inferior image contrast and spatial resolution compared to the high-end ultrasound systems. To address this, here we propose an efficient solution for contrast and resolution enhancement of hand-held POCUS images using unsupervised deep learning. In contrast to the existing CycleGAN approaches that have difficulty in improving both contrast and image resolutions, the proposed method mitigate the problem by decomposing the contrast transfer and resolution improvement through CycleGAN and self-supervised learning. Experimental results confirmed that our method is superior than the conventional approaches.

AB - Point-of-Care Ultrasound (POCUS) imaging can help efficient resource utilization by reducing the secondary care referrals, and work as an extension in physical examination. Recently, many methods were proposed to reduce the size and power consumption of the system while improving the visual quality, but hand-held POCUS devices still have inferior image contrast and spatial resolution compared to the high-end ultrasound systems. To address this, here we propose an efficient solution for contrast and resolution enhancement of hand-held POCUS images using unsupervised deep learning. In contrast to the existing CycleGAN approaches that have difficulty in improving both contrast and image resolutions, the proposed method mitigate the problem by decomposing the contrast transfer and resolution improvement through CycleGAN and self-supervised learning. Experimental results confirmed that our method is superior than the conventional approaches.

KW - CycleGAN

KW - Hand-held ultrasound

KW - POCUS

KW - Ultrasound imaging

KW - Unsupervised learning

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U2 - 10.1007/978-3-030-87722-4_15

DO - 10.1007/978-3-030-87722-4_15

M3 - Conference contribution

AN - SCOPUS:85116439306

SN - 9783030877217

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

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BT - Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings

A2 - Albarqouni, Shadi

A2 - Cardoso, M. Jorge

A2 - Dou, Qi

A2 - Kamnitsas, Konstantinos

A2 - Khanal, Bishesh

A2 - Rekik, Islem

A2 - Rieke, Nicola

A2 - Sheet, Debdoot

A2 - Tsaftaris, Sotirios

A2 - Xu, Daguang

A2 - Xu, Ziyue

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the 1st MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021

Y2 - 27 September 2021 through 1 October 2021

ER -

Khan S, Huh J, Ye JC. Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN. In Albarqouni S, Cardoso MJ, Dou Q, Kamnitsas K, Khanal B, Rekik I, Rieke N, Sheet D, Tsaftaris S, Xu D, Xu Z, editors, Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 158-167. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-87722-4_15

Contrast and Resolution Improvement of POCUS Using Self-consistent CycleGAN

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this