Deep ensemble learning for accurate retinal vessel segmentation
Introduction
Cardiovascular disease remains the predominant cause of mortality on a global scale, and it will continue to rise in the next 10 years [[1], [2], [3]]. The fundus retinal blood vessels are unique in that they are the only blood vessels in the body that can be directly observed. Studies have found that their morphological and functional changes are associated over the course of its development of many cardiovascular diseases, including coronary heart disease, atherosclerosis, and hypertension, and have the potential to become a tool for the assessment and prediction of related diseases [[4], [5], [6], [7]]. Therefore, research on automated retinal analysis is undoubted of great significance to public health.
Earlier, researches on Vessel segmentation were mainly based on traditional handcrafted features. For instance, quadrature filters [8], matched filters [9], and Gabor filters [10,11]. Specifically, the alternative incorporates all vessel widths and orientations with a predefined kernel bank. Handcrafted features, in fact, have inevitable limitations like our capability to analytically model the segmentation process [12,13]. There are also some methods put forward by Kaur [14], including region growing [15], piece-wise thresholding [16], and concavity measurements [17]. To trace the vascular structure, researchers have used some graph-theoretic techniques, apart from handcrafted features. For example, the fast marching algorithm [18] and shortest-path tracking [19]. AlexNet won the championship of ILSVRC in 2012, marking a new breakthrough for convolutional neural networks (CNNs) on ImageNet dataset. Since then, deep learning has developed rapidly, making new breakthroughs in various fields [[20], [21], [22], [23], [24], [25], [26]]. At present, U-NET is the most successful deep network used for blood vessel segmentation. It adds feature upsampling on the basis of traditional convolutional layer and fuses the location information with the semantic information through skip connections. As mentioned above, prior deep learning approaches applied a classifier to a series of small patches that were divided from an image. Actually, that's exactly what Dasgupta and Singh did [27]. Numerous adaptations to the initial U-net design have been developed. Alom et al. added the addition of residual connections and the application of a recursive training strategy to the U-net, which improves performance but increases training time. To learn more features, Zhuang et al. introduced a multi-path network that consists of two U-net architectures were serially combined [28]. However, it leads to greatly increased training time. Besides, Jiang and Tan incorporated the U-net architecture augmented with conditional generative adversarial networks (GANs) resulted in improved performance and get a more balanced result, Jin et al. incorporating deformable CNNs resulted in better vascular feature construction [29], but they both significantly increased complexity.
To improve the segmentation performance, Our work introduces an novel framework for retinal vessel segmentation based on deep ensemble learning. Benchmarking comparison results show that our model surpasses existing approaches on several datasets, demonstrating that our models are more effective, superior, and robust for the retinal vessel segmentation. It evinces the capability of our model to capture the discriminative feature representations through introducing the ensemble strategy to integrate different base deep learning models like pyramid vision Transformer and FCN-Transformer. We expect our proposed method can benefit and accelerate the development of accurate retinal vessel segmentation in this field.
Section snippets
Methodology
We establish the overall framework illustrated in Fig. 1. First, we obtain the retinal vessel images from the three datasets. Then we used data preprocessing includes image cropping and green channel extraction from original retinal images. And we used the common augmented operations including image flipping and so on. After this, we used three convolutional vision transformer base networks to as the independent base model predicted the results. It is note that the independent base networks
Comparison with existing methods
To strengthen the evidence supporting the effectiveness of our proposed approach, we conducted a comparative analysis with the most advanced models currently available. These models included U-net architecture and its extensions, including LadderNet [38], R2Unet [37], and DUNet [29]. A summary of the performance of the compared methods on various datasets is presented in Table 1, Table 2, Table 3. It is worth noting that most of the current state-of-the-art methods exhibit lower F1 scores
Conclusion
The contribution of this work is a deep ensemble learning framework that is designed specifically for retinal vessel segmentation. Benchmarking comparison results show that our models perform better that the existing approaches on several datasets, demonstrating that our models are more effective, superior, and robust for the retinal vessel segmentation. Via feature analysis, we found that our model is capable of capturing the discriminative feature representations through introducing the
Funding
This study was supported by Scientific Research Innovation Fund of the First Hospital of Harbin Medical University (2017B003), the Natural Science Foundation of Heilongjiang Province (Grant No. LH2020H061), and the Foundation of Academy of Medical Sciences of Heilongjiang Province (Grant No. 201702).
Declaration of cCompeting iInterest
There is no competing financial interest to declare.
References (41)
- et al.
Blood vessel segmentation using multi-scale quadrature filtering
Pattern Recogn. Lett.
(2010) Toward better drug discovery with knowledge graph
Curr. Opin. Struct. Biol.
(2022)Deep generative molecular design reshapes drug discovery
Cell Rep. Med.
(2022)DUNet: a deformable network for retinal vessel segmentation
Knowl. Base Syst.
(2019)- et al.
An essential introduction to the annual report on cardiovascular health and diseases in China (2021)
Chinese General Practice
(2022) - et al.
Integration of multiple-omics data to analyze the population-specific differences for coronary artery disease
Comput. Math. Methods Med.
(2021) Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework
Nat. Mach. Intell.
(2022)Retinal vascular caliber measurements: clinical significance, current knowledge and future perspectives
Ophthalmologica
(2013)rs1990622 variant associates with Alzheimer's disease and regulates TMEM106B expression in human brain tissues
BMC Med.
(2021)rs34331204 regulates TSPAN13 expression and contributes to Alzheimer's disease with sex differences
Brain
(2020)
DSN-DDI: an accurate and generalized framework for drug–drug interaction prediction by dual-view representation learning
Briefings Bioinf.
Detection of blood vessels in retinal images using two-dimensional matched filters
IEEE Trans. Med. Imag.
Blood vessels detection and segmentation in retina using Gabor filters
Retinal blood vessel segmentation using Gabor filter and top-hat transform
Mendelian Randomization Highlights Causal Association between Genetically Increased C-Reactive Protein Levels and Reduced Alzheimer's Disease Risk
Cognitive performance protects against Alzheimer’s disease independently of educational attainment and intelligence
Mol. Psychiatr.
M. Computing, Various image segmentation techniques: a review
Int. J. Comput. Sci. Mobile Comput.
Retinal blood vessel segmentation by means of scale-space analysis and region growing
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
IEEE Trans. Med. Imag.
General retinal vessel segmentation using regularization-based multiconcavity modeling
IEEE Trans. Med. Imag.
Cited by (8)
Multi-threshold Image Segmentation based on an improved Salp Swarm Algorithm: Case study of breast cancer pathology images
2024, Computers in Biology and MedicineOII-DS: A benchmark Oral Implant Image Dataset for object detection and image classification evaluation
2023, Computers in Biology and MedicineMAFE-Net: retinal vessel segmentation based on a multiple attention-guided fusion mechanism and ensemble learning network
2024, Biomedical Optics ExpressA Review on Retinal Blood Vessel Enhancement and Segmentation Techniques for Color Fundus Photography
2024, Critical Reviews in Biomedical Engineering