DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images
In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to v...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719769/?tool=EBI |
| _version_ | 1819283596200902656 |
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| author | Mohsin Raza Khuram Naveed Awais Akram Nema Salem Amir Afaq Hussain Ahmad Madni Mohammad A. U. Khan Mui-zzud- din |
| author_facet | Mohsin Raza Khuram Naveed Awais Akram Nema Salem Amir Afaq Hussain Ahmad Madni Mohammad A. U. Khan Mui-zzud- din |
| author_sort | Mohsin Raza |
| collection | DOAJ |
| description | In this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters. |
| first_indexed | 2024-12-24T01:33:59Z |
| format | Article |
| id | doaj.art-75a72fbf5fa54f9984646f75490eb217 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-24T01:33:59Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-75a72fbf5fa54f9984646f75490eb2172022-12-21T17:22:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-011612DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus imagesMohsin RazaKhuram NaveedAwais AkramNema SalemAmir AfaqHussain Ahmad MadniMohammad A. U. KhanMui-zzud- dinIn this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719769/?tool=EBI |
| spellingShingle | Mohsin Raza Khuram Naveed Awais Akram Nema Salem Amir Afaq Hussain Ahmad Madni Mohammad A. U. Khan Mui-zzud- din DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images PLoS ONE |
| title | DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images |
| title_full | DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images |
| title_fullStr | DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images |
| title_full_unstemmed | DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images |
| title_short | DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images |
| title_sort | davs net dense aggregation vessel segmentation network for retinal vasculature detection in fundus images |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719769/?tool=EBI |
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