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.
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 |
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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 | |
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 |
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Publisher | Springer |
Volume | 14269 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Intelligent Robotics and Applications |
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Country/Territory | China |
City | Hangzhou |
Period | 5/07/23 → 7/07/23 |
Keywords
- IPN-V2 model
- segmentation of retinal blood vessels
- attention mechanism