TY - JOUR
T1 - Unveiling the future of breast cancer assessment
T2 - a critical review on generative adversarial networks in elastography ultrasound
AU - Ansari, Mohammed Yusuf
AU - Qaraqe, Marwa
AU - Righetti, Raffaella
AU - Serpedin, Erchin
AU - Qaraqe, Khalid
N1 - Funding Information:
This publication was made possible by the support of Texas A&M at Qatar Research Initiative. The statements made herein are solely the responsibility of the authors.
Publisher Copyright:
Copyright © 2023 Ansari, Qaraqe, Righetti, Serpedin and Qaraqe.
PY - 2023
Y1 - 2023
N2 - Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.
AB - Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.
KW - artificial intelligence in medical imaging
KW - breast cancer diagnosis
KW - computer-aided diagnosis
KW - elastography ultrasound
KW - enhancing pocket ultrasound
KW - generative adversarial networks
KW - image-to-image translation
KW - medical image synthesis
UR - http://www.scopus.com/inward/record.url?scp=85180402614&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85180402614&partnerID=8YFLogxK
U2 - 10.3389/fonc.2023.1282536
DO - 10.3389/fonc.2023.1282536
M3 - Short survey
AN - SCOPUS:85180402614
SN - 2234-943X
VL - 13
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 1282536
ER -