Evidence map›Paper›PMID 39432221›Full record

ArticleMedical & biological engineering & computing2025

A multi-scale feature extraction and fusion-based model for retinal vessel segmentation in fundus images.

Jinzhi Zhou, Guangcen Ma, Haoyang He, Saifeng Li, Guopeng Zhang

Abstract read
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In one paragraph

Article in Medical & biological engineering & computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jinzhi ZhouSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China. zhoujinzhi@swust.edu.cn.ORCID http://orcid.org/0000-0001-8686-5213
Guangcen MaSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China.
Haoyang HeSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China.
Saifeng LiSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China.
Guopeng ZhangSchool of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China.

Funding

National Natural Science Foundation of China 61771411
6 · The paper itself

Abstract

In response to the challenge of low accuracy in retinal vessel segmentation attributed to the minute nature of the vessels, this paper proposes a retinal vessel segmentation model based on an improved U-Net, which combines multi-scale feature extraction and fusion techniques. An improved dilated residual module was first used to replace the original convolutional layer of U-Net, and this module, coupled with a dual attention mechanism and diverse expansion rates, facilitates the extraction of multi-scale vascular features. Moreover, an adaptive feature fusion module was added at the skip connections of the model to improve vessel connectivity. To further optimize network training, a hybrid loss function is employed to mitigate the class imbalance between vessels and the background. Experimental results on the DRIVE dataset and CHASE_DB1 dataset show that the proposed model has an accuracy of 96.27% and 96.96%, sensitivity of 81.32% and 82.59%, and AUC of 98.34% and 98.70%, respectively, demonstrating superior segmentation performance.

Indexed as

Fundus OculiImage Processing, Computer-AssistedRetinal VesselsAlgorithmsHumansNeural Networks, ComputerAttention mechanismDeep neural networkMulti-scale featuresRetinal vessel segmentation

Identifiers

PMID39432221

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.