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A Novel Deep Learning Based OCTA De-striping Method. / Gao, Dongxu; Celik, Numan; Wu, Xiyin et al.
Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. ed. / Yalin Zheng; Bryan M. Williams; Ke Chen. Vol. 2019 Springer, 2020. p. 189-197 (Communications in Computer and Information Science; Vol. 1065 CCIS).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Gao, D, Celik, N, Wu, X
, Williams, BM, Stylianides, A & Zheng, Y 2020,
A Novel Deep Learning Based OCTA De-striping Method. in Y Zheng, BM Williams & K Chen (eds),
Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. vol. 2019, Communications in Computer and Information Science, vol. 1065 CCIS, Springer, pp. 189-197, 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019, Liverpool, United Kingdom,
24/07/19.
https://doi.org/10.1007/978-3-030-39343-4_16APA
Gao, D., Celik, N., Wu, X.
, Williams, B. M., Stylianides, A., & Zheng, Y. (2020).
A Novel Deep Learning Based OCTA De-striping Method. In Y. Zheng, B. M. Williams, & K. Chen (Eds.),
Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings (Vol. 2019, pp. 189-197). (Communications in Computer and Information Science; Vol. 1065 CCIS). Springer.
https://doi.org/10.1007/978-3-030-39343-4_16Vancouver
Gao D, Celik N, Wu X
, Williams BM, Stylianides A, Zheng Y.
A Novel Deep Learning Based OCTA De-striping Method. In Zheng Y, Williams BM, Chen K, editors, Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. Vol. 2019. Springer. 2020. p. 189-197. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-39343-4_16
Author
Gao, Dongxu ; Celik, Numan ; Wu, Xiyin et al. /
A Novel Deep Learning Based OCTA De-striping Method. Medical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings. editor / Yalin Zheng ; Bryan M. Williams ; Ke Chen. Vol. 2019 Springer, 2020. pp. 189-197 (Communications in Computer and Information Science).
Bibtex
@inproceedings{1f7b8a014ef748518adaacc7e9cecc00,
title = "A Novel Deep Learning Based OCTA De-striping Method",
abstract = "Noise in images presents a considerable problem, limiting their readability and hindering the performance of post-processing and analysis tools. In particular, optical coherence tomography angiography (OCTA) suffers from stripe noise. In medical imaging, clinicians rely on high quality images in order to make accurate diagnoses and plan management. Poor quality images can lead to pathology being overlooked or undiagnosed. Image denoising is a fundamental technique that can be developed to tackle this problem and improve performance in many applications, yet there exists no method focused on removing stripe noise in OCTA. Existing OCTA denoising methods do not consider the structure of stripe noise, which severely limits their potential for recovering the image. The development of artificial intelligence (AI) have enabled deep learning approaches to obtain impressive results and play a dominant role in many areas, but require a ground truth for training, which is difficult to obtain for this problem. In this paper, we propose a revised U-net framework for removing the stripe noise from OCTA images, leaving a clean image. With our proposed method, a ground truth is not required for training, allowing both the stripe noise and the clean image to be estimated, preserving more image detail without compromising image quality. The experimental results show the impressive de-striping performance of our method on OCTA images. We evaluate the effectiveness of our proposed method using the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), achieving excellent results as well.",
keywords = "Deep learning, Image decomposition, OCTA, Stripe noise removal",
author = "Dongxu Gao and Numan Celik and Xiyin Wu and Williams, {Bryan M.} and Amira Stylianides and Yalin Zheng",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-3-030-39343-4_16",
language = "English",
isbn = "9783030393427",
volume = "2019",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "189--197",
editor = "Yalin Zheng and Williams, {Bryan M.} and Ke Chen",
booktitle = "Medical Image Understanding and Analysis",
note = "23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 ; Conference date: 24-07-2019 Through 26-07-2019",
}
RIS
TY - GEN
T1 - A Novel Deep Learning Based OCTA De-striping Method
AU - Gao, Dongxu
AU - Celik, Numan
AU - Wu, Xiyin
AU - Williams, Bryan M.
AU - Stylianides, Amira
AU - Zheng, Yalin
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Noise in images presents a considerable problem, limiting their readability and hindering the performance of post-processing and analysis tools. In particular, optical coherence tomography angiography (OCTA) suffers from stripe noise. In medical imaging, clinicians rely on high quality images in order to make accurate diagnoses and plan management. Poor quality images can lead to pathology being overlooked or undiagnosed. Image denoising is a fundamental technique that can be developed to tackle this problem and improve performance in many applications, yet there exists no method focused on removing stripe noise in OCTA. Existing OCTA denoising methods do not consider the structure of stripe noise, which severely limits their potential for recovering the image. The development of artificial intelligence (AI) have enabled deep learning approaches to obtain impressive results and play a dominant role in many areas, but require a ground truth for training, which is difficult to obtain for this problem. In this paper, we propose a revised U-net framework for removing the stripe noise from OCTA images, leaving a clean image. With our proposed method, a ground truth is not required for training, allowing both the stripe noise and the clean image to be estimated, preserving more image detail without compromising image quality. The experimental results show the impressive de-striping performance of our method on OCTA images. We evaluate the effectiveness of our proposed method using the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), achieving excellent results as well.
AB - Noise in images presents a considerable problem, limiting their readability and hindering the performance of post-processing and analysis tools. In particular, optical coherence tomography angiography (OCTA) suffers from stripe noise. In medical imaging, clinicians rely on high quality images in order to make accurate diagnoses and plan management. Poor quality images can lead to pathology being overlooked or undiagnosed. Image denoising is a fundamental technique that can be developed to tackle this problem and improve performance in many applications, yet there exists no method focused on removing stripe noise in OCTA. Existing OCTA denoising methods do not consider the structure of stripe noise, which severely limits their potential for recovering the image. The development of artificial intelligence (AI) have enabled deep learning approaches to obtain impressive results and play a dominant role in many areas, but require a ground truth for training, which is difficult to obtain for this problem. In this paper, we propose a revised U-net framework for removing the stripe noise from OCTA images, leaving a clean image. With our proposed method, a ground truth is not required for training, allowing both the stripe noise and the clean image to be estimated, preserving more image detail without compromising image quality. The experimental results show the impressive de-striping performance of our method on OCTA images. We evaluate the effectiveness of our proposed method using the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), achieving excellent results as well.
KW - Deep learning
KW - Image decomposition
KW - OCTA
KW - Stripe noise removal
UR - http://www.scopus.com/inward/record.url?scp=85079090799&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39343-4_16
DO - 10.1007/978-3-030-39343-4_16
M3 - Conference contribution/Paper
AN - SCOPUS:85079090799
SN - 9783030393427
VL - 2019
T3 - Communications in Computer and Information Science
SP - 189
EP - 197
BT - Medical Image Understanding and Analysis
A2 - Zheng, Yalin
A2 - Williams, Bryan M.
A2 - Chen, Ke
PB - Springer
T2 - 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Y2 - 24 July 2019 through 26 July 2019
ER -
