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Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation

Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lo...

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Autores principales: Zhang, Jiawei, Zhang, Yanchun, Qiu, Hailong, Xie, Wen, Yao, Zeyang, Yuan, Haiyun, Jia, Qianjun, Wang, Tianchen, Shi, Yiyu, Huang, Meiping, Zhuang, Jian, Xu, Xiaowei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8688400/
https://www.ncbi.nlm.nih.gov/pubmed/34950679
http://dx.doi.org/10.3389/fmed.2021.761050
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author Zhang, Jiawei
Zhang, Yanchun
Qiu, Hailong
Xie, Wen
Yao, Zeyang
Yuan, Haiyun
Jia, Qianjun
Wang, Tianchen
Shi, Yiyu
Huang, Meiping
Zhuang, Jian
Xu, Xiaowei
author_facet Zhang, Jiawei
Zhang, Yanchun
Qiu, Hailong
Xie, Wen
Yao, Zeyang
Yuan, Haiyun
Jia, Qianjun
Wang, Tianchen
Shi, Yiyu
Huang, Meiping
Zhuang, Jian
Xu, Xiaowei
author_sort Zhang, Jiawei
collection PubMed
description Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lower scale or a higher scale, which may result in a limited segmentation performance, especially on thin vessels. This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). At each layer, IPABs generate two associated branches at a higher scale and a lower scale, respectively, and the two with the main branch at the current scale operate in a pyramid-scale manner. Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). The experimental results show that Pyramid-Net can effectively improve the segmentation performance especially on thin vessels, and outperforms the current state-of-the-art methods on all the adopted three datasets. In addition, our method is more efficient than existing methods with a large reduction in computational cost. We have released the source code at https://github.com/JerRuy/Pyramid-Net.
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spelling pubmed-86884002021-12-22 Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation Zhang, Jiawei Zhang, Yanchun Qiu, Hailong Xie, Wen Yao, Zeyang Yuan, Haiyun Jia, Qianjun Wang, Tianchen Shi, Yiyu Huang, Meiping Zhuang, Jian Xu, Xiaowei Front Med (Lausanne) Medicine Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lower scale or a higher scale, which may result in a limited segmentation performance, especially on thin vessels. This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). At each layer, IPABs generate two associated branches at a higher scale and a lower scale, respectively, and the two with the main branch at the current scale operate in a pyramid-scale manner. Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). The experimental results show that Pyramid-Net can effectively improve the segmentation performance especially on thin vessels, and outperforms the current state-of-the-art methods on all the adopted three datasets. In addition, our method is more efficient than existing methods with a large reduction in computational cost. We have released the source code at https://github.com/JerRuy/Pyramid-Net. Frontiers Media S.A. 2021-12-07 /pmc/articles/PMC8688400/ /pubmed/34950679 http://dx.doi.org/10.3389/fmed.2021.761050 Text en Copyright © 2021 Zhang, Zhang, Qiu, Xie, Yao, Yuan, Jia, Wang, Shi, Huang, Zhuang and Xu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Zhang, Jiawei
Zhang, Yanchun
Qiu, Hailong
Xie, Wen
Yao, Zeyang
Yuan, Haiyun
Jia, Qianjun
Wang, Tianchen
Shi, Yiyu
Huang, Meiping
Zhuang, Jian
Xu, Xiaowei
Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation
title Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation
title_full Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation
title_fullStr Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation
title_full_unstemmed Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation
title_short Pyramid-Net: Intra-layer Pyramid-Scale Feature Aggregation Network for Retinal Vessel Segmentation
title_sort pyramid-net: intra-layer pyramid-scale feature aggregation network for retinal vessel segmentation
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8688400/
https://www.ncbi.nlm.nih.gov/pubmed/34950679
http://dx.doi.org/10.3389/fmed.2021.761050
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