Retinal vascular segmentation based on depth-separable convolution and attention mechanisms

Xiaopeng Liu; Dongxu Gao; Congyi Zhang; Hongwei Gao; Zhaojie Ju

doi:10.1007/978-981-99-6489-5_12

Retinal vascular segmentation based on depth-separable convolution and attention mechanisms

Xiaopeng Liu
, Dongxu Gao
, Congyi Zhang
, Hongwei Gao
, Zhaojie Ju

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

52 Downloads (Pure)

Abstract

Retinal vascular segmentation is an important research direction in the field of medical image processing, its main purpose is to automatically segment the vascular area from the fundus image, and provide doctors with more accurate diagnosis results and treatment plans. In recent years, with the continuous development of deep learning technology, retinal vascular segmentation algorithm based on deep learning has gradually become a research hotspot. In this paper, the retinal vascular segmentation algorithm based on deep learning is mainly improved, and the retinal vascular segmentation algorithm based on IPN-V2 is improved, in an attempt to make new explorations.

The retinal vascular segmentation algorithm based on IPN-V2 provides global information, but requires a large amount of image data and label information, the image size is different, and most importantly, the accuracy of the model for the segmentation of the original image is not enough. Therefore, this paper improves the retinal vascular segmentation algorithm based on IPN-V2, introduces the attention mechanism, and constructs a retinal vascular segmentation model based on ASR-IPN-V2, which enables the model to extract more image details from the original image through the depth-separable convolution and convolutional block attention mechanisms.

Experiments show that the retinal vascular segmentation model based on ASR-IPN-V2 greatly improves the efficiency of retinal vascular segmentation.

Original language	English
Title of host publication	Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III
Editors	Huayong Yang, Honghai Liu, Jun Zou, Zhouping Yin, Lianqing Liu, Geng Yang, Xiaoping Ouyang, Zhiyong Wang
Publisher	Springer
Pages	145-160
Number of pages	16
ISBN (Electronic)	9789819964895
ISBN (Print)	9789819964888
DOIs	https://doi.org/10.1007/978-981-99-6489-5_12
Publication status	Published - 11 Oct 2023
Event	International Conference on Intelligent Robotics and Applications - Hangzhou, China Duration: 5 Jul 2023 → 7 Jul 2023

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	14269
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Intelligent Robotics and Applications
Country/Territory	China
City	Hangzhou
Period	5/07/23 → 7/07/23

Keywords

IPN-V2 model
segmentation of retinal blood vessels
attention mechanism

Access to Document

10.1007/978-981-99-6489-5_12

Liu_et_al_2023_AAM
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-981-99-6489-5_12.
Accepted author manuscript (Post-print), 671 KB

Cite this

Liu, X., Gao, D., Zhang, C., Gao, H., & Ju, Z. (2023). Retinal vascular segmentation based on depth-separable convolution and attention mechanisms. In H. Yang, H. Liu, J. Zou, Z. Yin, L. Liu, G. Yang, X. Ouyang, & Z. Wang (Eds.), Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III (pp. 145-160). (Lecture Notes in Computer Science; Vol. 14269). Springer. https://doi.org/10.1007/978-981-99-6489-5_12

Liu, Xiaopeng ; Gao, Dongxu ; Zhang, Congyi et al. / Retinal vascular segmentation based on depth-separable convolution and attention mechanisms. Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III. editor / Huayong Yang ; Honghai Liu ; Jun Zou ; Zhouping Yin ; Lianqing Liu ; Geng Yang ; Xiaoping Ouyang ; Zhiyong Wang. Springer, 2023. pp. 145-160 (Lecture Notes in Computer Science).

@inproceedings{25210874677b40658182390ab2bbf1c1,

title = "Retinal vascular segmentation based on depth-separable convolution and attention mechanisms",

abstract = "Retinal vascular segmentation is an important research direction in the field of medical image processing, its main purpose is to automatically segment the vascular area from the fundus image, and provide doctors with more accurate diagnosis results and treatment plans. In recent years, with the continuous development of deep learning technology, retinal vascular segmentation algorithm based on deep learning has gradually become a research hotspot. In this paper, the retinal vascular segmentation algorithm based on deep learning is mainly improved, and the retinal vascular segmentation algorithm based on IPN-V2 is improved, in an attempt to make new explorations. The retinal vascular segmentation algorithm based on IPN-V2 provides global information, but requires a large amount of image data and label information, the image size is different, and most importantly, the accuracy of the model for the segmentation of the original image is not enough. Therefore, this paper improves the retinal vascular segmentation algorithm based on IPN-V2, introduces the attention mechanism, and constructs a retinal vascular segmentation model based on ASR-IPN-V2, which enables the model to extract more image details from the original image through the depth-separable convolution and convolutional block attention mechanisms. Experiments show that the retinal vascular segmentation model based on ASR-IPN-V2 greatly improves the efficiency of retinal vascular segmentation.",

keywords = "IPN-V2 model, segmentation of retinal blood vessels, attention mechanism",

author = "Xiaopeng Liu and Dongxu Gao and Congyi Zhang and Hongwei Gao and Zhaojie Ju",

year = "2023",

month = oct,

day = "11",

doi = "10.1007/978-981-99-6489-5\_12",

language = "English",

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series = "Lecture Notes in Computer Science",

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editor = "Huayong Yang and Honghai Liu and Jun Zou and Zhouping Yin and Lianqing Liu and Geng Yang and Xiaoping Ouyang and Zhiyong Wang",

booktitle = "Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III",

address = "Netherlands",

note = "International Conference on Intelligent Robotics and Applications ; Conference date: 05-07-2023 Through 07-07-2023",

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Liu, X, Gao, D, Zhang, C, Gao, H & Ju, Z 2023, Retinal vascular segmentation based on depth-separable convolution and attention mechanisms. in H Yang, H Liu, J Zou, Z Yin, L Liu, G Yang, X Ouyang & Z Wang (eds), Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III. Lecture Notes in Computer Science, vol. 14269, Springer, pp. 145-160, International Conference on Intelligent Robotics and Applications, Hangzhou, China, 5/07/23. https://doi.org/10.1007/978-981-99-6489-5_12

Retinal vascular segmentation based on depth-separable convolution and attention mechanisms. / Liu, Xiaopeng; Gao, Dongxu; Zhang, Congyi et al.
Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III. ed. / Huayong Yang; Honghai Liu; Jun Zou; Zhouping Yin; Lianqing Liu; Geng Yang; Xiaoping Ouyang; Zhiyong Wang. Springer, 2023. p. 145-160 (Lecture Notes in Computer Science; Vol. 14269).

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

TY - GEN

T1 - Retinal vascular segmentation based on depth-separable convolution and attention mechanisms

AU - Liu, Xiaopeng

AU - Gao, Dongxu

AU - Zhang, Congyi

AU - Gao, Hongwei

AU - Ju, Zhaojie

PY - 2023/10/11

Y1 - 2023/10/11

N2 - Retinal vascular segmentation is an important research direction in the field of medical image processing, its main purpose is to automatically segment the vascular area from the fundus image, and provide doctors with more accurate diagnosis results and treatment plans. In recent years, with the continuous development of deep learning technology, retinal vascular segmentation algorithm based on deep learning has gradually become a research hotspot. In this paper, the retinal vascular segmentation algorithm based on deep learning is mainly improved, and the retinal vascular segmentation algorithm based on IPN-V2 is improved, in an attempt to make new explorations. The retinal vascular segmentation algorithm based on IPN-V2 provides global information, but requires a large amount of image data and label information, the image size is different, and most importantly, the accuracy of the model for the segmentation of the original image is not enough. Therefore, this paper improves the retinal vascular segmentation algorithm based on IPN-V2, introduces the attention mechanism, and constructs a retinal vascular segmentation model based on ASR-IPN-V2, which enables the model to extract more image details from the original image through the depth-separable convolution and convolutional block attention mechanisms. Experiments show that the retinal vascular segmentation model based on ASR-IPN-V2 greatly improves the efficiency of retinal vascular segmentation.

AB - Retinal vascular segmentation is an important research direction in the field of medical image processing, its main purpose is to automatically segment the vascular area from the fundus image, and provide doctors with more accurate diagnosis results and treatment plans. In recent years, with the continuous development of deep learning technology, retinal vascular segmentation algorithm based on deep learning has gradually become a research hotspot. In this paper, the retinal vascular segmentation algorithm based on deep learning is mainly improved, and the retinal vascular segmentation algorithm based on IPN-V2 is improved, in an attempt to make new explorations. The retinal vascular segmentation algorithm based on IPN-V2 provides global information, but requires a large amount of image data and label information, the image size is different, and most importantly, the accuracy of the model for the segmentation of the original image is not enough. Therefore, this paper improves the retinal vascular segmentation algorithm based on IPN-V2, introduces the attention mechanism, and constructs a retinal vascular segmentation model based on ASR-IPN-V2, which enables the model to extract more image details from the original image through the depth-separable convolution and convolutional block attention mechanisms. Experiments show that the retinal vascular segmentation model based on ASR-IPN-V2 greatly improves the efficiency of retinal vascular segmentation.

KW - IPN-V2 model

KW - segmentation of retinal blood vessels

KW - attention mechanism

U2 - 10.1007/978-981-99-6489-5_12

DO - 10.1007/978-981-99-6489-5_12

M3 - Conference contribution

SN - 9789819964888

T3 - Lecture Notes in Computer Science

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EP - 160

BT - Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III

A2 - Yang, Huayong

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A2 - Zou, Jun

A2 - Yin, Zhouping

A2 - Liu, Lianqing

A2 - Yang, Geng

A2 - Ouyang, Xiaoping

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PB - Springer

T2 - International Conference on Intelligent Robotics and Applications

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ER -

Liu X, Gao D, Zhang C, Gao H, Ju Z. Retinal vascular segmentation based on depth-separable convolution and attention mechanisms. In Yang H, Liu H, Zou J, Yin Z, Liu L, Yang G, Ouyang X, Wang Z, editors, Intelligent Robotics and Applications 16th International Conference, ICIRA 2023, Hangzhou, China, July 5–7, 2023, Proceedings, Part III. Springer. 2023. p. 145-160. (Lecture Notes in Computer Science). doi: 10.1007/978-981-99-6489-5_12

Retinal vascular segmentation based on depth-separable convolution and attention mechanisms

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Keywords

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