An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy

Xue Li; Zihan Zhou; Kaifeng Wang; Haiying Liu; Yalong Qian; Xingguang Duan; Changsheng Li

doi:10.1007/978-981-99-8018-5_11

An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy

Xue Li, Zihan Zhou, Kaifeng Wang, Haiying Liu, Yalong Qian, Xingguang Duan^*, Changsheng Li

^*Corresponding author for this work

School of Mechatronical Engineering

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

Abstract

High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon’s visual presence for the guidance of surgical procedures. However, available image super-resolution methods, especially deep learning methods, are mostly trained with open-source life scene datasets which possess limited medical image features. To address this issue, we have proposed an improved SRGAN model for the visual enhancement of percutaneous endoscopic lumbar discectomy (PELD) surgical images. Specifically, a residual dense block (RDB) and a dynamic RELU function are introduced. We validate the proposed method on PELD datasets. Quantitative and qualitative comparisons are carried out by comparing methods. The method proposed in this paper improves PSNR by 2.8% and SSIM by 6% compared with the original SRGAN, which proves the superiority of this methods.

Original language	English
Title of host publication	Cognitive Systems and Information Processing - 8th International Conference, ICCSIP 2023, Revised Selected Papers
Editors	Fuchun Sun, Bin Fang, Qinghu Meng, Zhumu Fu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	149-160
Number of pages	12
ISBN (Print)	9789819980178
DOIs	https://doi.org/10.1007/978-981-99-8018-5_11
Publication status	Published - 2024
Event	8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023 - Fuzhou, China Duration: 10 Aug 2023 → 12 Aug 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1918 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023
Country/Territory	China
City	Fuzhou
Period	10/08/23 → 12/08/23

Keywords

Generative Adversarial Network
Medical images processing
Percutaneous Endoscopic Lumbar Discectomy
Single Image Super-Resolution

Access to Document

10.1007/978-981-99-8018-5_11

Cite this

Li, X., Zhou, Z., Wang, K., Liu, H., Qian, Y., Duan, X., & Li, C. (2024). An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy. In F. Sun, B. Fang, Q. Meng, & Z. Fu (Eds.), Cognitive Systems and Information Processing - 8th International Conference, ICCSIP 2023, Revised Selected Papers (pp. 149-160). (Communications in Computer and Information Science; Vol. 1918 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8018-5_11

Li, Xue ; Zhou, Zihan ; Wang, Kaifeng et al. / An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy. Cognitive Systems and Information Processing - 8th International Conference, ICCSIP 2023, Revised Selected Papers. editor / Fuchun Sun ; Bin Fang ; Qinghu Meng ; Zhumu Fu. Springer Science and Business Media Deutschland GmbH, 2024. pp. 149-160 (Communications in Computer and Information Science).

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title = "An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy",

abstract = "High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon{\textquoteright}s visual presence for the guidance of surgical procedures. However, available image super-resolution methods, especially deep learning methods, are mostly trained with open-source life scene datasets which possess limited medical image features. To address this issue, we have proposed an improved SRGAN model for the visual enhancement of percutaneous endoscopic lumbar discectomy (PELD) surgical images. Specifically, a residual dense block (RDB) and a dynamic RELU function are introduced. We validate the proposed method on PELD datasets. Quantitative and qualitative comparisons are carried out by comparing methods. The method proposed in this paper improves PSNR by 2.8% and SSIM by 6% compared with the original SRGAN, which proves the superiority of this methods.",

keywords = "Generative Adversarial Network, Medical images processing, Percutaneous Endoscopic Lumbar Discectomy, Single Image Super-Resolution",

author = "Xue Li and Zihan Zhou and Kaifeng Wang and Haiying Liu and Yalong Qian and Xingguang Duan and Changsheng Li",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023 ; Conference date: 10-08-2023 Through 12-08-2023",

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Li, X, Zhou, Z, Wang, K, Liu, H, Qian, Y, Duan, X & Li, C 2024, An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy. in F Sun, B Fang, Q Meng & Z Fu (eds), Cognitive Systems and Information Processing - 8th International Conference, ICCSIP 2023, Revised Selected Papers. Communications in Computer and Information Science, vol. 1918 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 149-160, 8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023, Fuzhou, China, 10/08/23. https://doi.org/10.1007/978-981-99-8018-5_11

An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy. / Li, Xue; Zhou, Zihan; Wang, Kaifeng et al.
Cognitive Systems and Information Processing - 8th International Conference, ICCSIP 2023, Revised Selected Papers. ed. / Fuchun Sun; Bin Fang; Qinghu Meng; Zhumu Fu. Springer Science and Business Media Deutschland GmbH, 2024. p. 149-160 (Communications in Computer and Information Science; Vol. 1918 CCIS).

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

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T1 - An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy

AU - Li, Xue

AU - Zhou, Zihan

AU - Wang, Kaifeng

AU - Liu, Haiying

AU - Qian, Yalong

AU - Duan, Xingguang

AU - Li, Changsheng

PY - 2024

Y1 - 2024

N2 - High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon’s visual presence for the guidance of surgical procedures. However, available image super-resolution methods, especially deep learning methods, are mostly trained with open-source life scene datasets which possess limited medical image features. To address this issue, we have proposed an improved SRGAN model for the visual enhancement of percutaneous endoscopic lumbar discectomy (PELD) surgical images. Specifically, a residual dense block (RDB) and a dynamic RELU function are introduced. We validate the proposed method on PELD datasets. Quantitative and qualitative comparisons are carried out by comparing methods. The method proposed in this paper improves PSNR by 2.8% and SSIM by 6% compared with the original SRGAN, which proves the superiority of this methods.

AB - High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon’s visual presence for the guidance of surgical procedures. However, available image super-resolution methods, especially deep learning methods, are mostly trained with open-source life scene datasets which possess limited medical image features. To address this issue, we have proposed an improved SRGAN model for the visual enhancement of percutaneous endoscopic lumbar discectomy (PELD) surgical images. Specifically, a residual dense block (RDB) and a dynamic RELU function are introduced. We validate the proposed method on PELD datasets. Quantitative and qualitative comparisons are carried out by comparing methods. The method proposed in this paper improves PSNR by 2.8% and SSIM by 6% compared with the original SRGAN, which proves the superiority of this methods.

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KW - Percutaneous Endoscopic Lumbar Discectomy

KW - Single Image Super-Resolution

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U2 - 10.1007/978-981-99-8018-5_11

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M3 - Conference contribution

AN - SCOPUS:85177177099

SN - 9789819980178

T3 - Communications in Computer and Information Science

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BT - Cognitive Systems and Information Processing - 8th International Conference, ICCSIP 2023, Revised Selected Papers

A2 - Sun, Fuchun

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A2 - Fu, Zhumu

PB - Springer Science and Business Media Deutschland GmbH

T2 - 8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

Y2 - 10 August 2023 through 12 August 2023

ER -

Li X, Zhou Z, Wang K, Liu H, Qian Y, Duan X et al. An Improved Image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy. In Sun F, Fang B, Meng Q, Fu Z, editors, Cognitive Systems and Information Processing - 8th International Conference, ICCSIP 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2024. p. 149-160. (Communications in Computer and Information Science). doi: 10.1007/978-981-99-8018-5_11