A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model

Gegúndez-Arias, Manuel Emilio; Marín, D.; Pérez-Borrero, Isaac; Vasallo-Vazquez, Manuel J.

doi:10.1016/j.cmpb.2021.106081

Cited by 66 publications

(19 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The average accuracy is 0.9780 for STARE. The method proposed in reference [ 10 ] is composed of a convolution neural network based on a simplified version of u-net architecture. The network receives small blocks extracted from the original image as input and uses a new loss function considering the distance between each pixel and the vascular tree for training.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation

Yan

Zhou

Luo

et al. 2022

Sensors

View full text Add to dashboard Cite

The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized by the Particle Swarm Optimization (PSO) algorithm, Otsu algorithm (OTSU) multi-threshold segmentation is utilized to segment the retinal image extracted by the multi-scale matched filtering method. Finally, the image is post-processed, including binarization, morphological operation and edge-contour removal. The test experiments are implemented on the DRIVE and STARE datasets to evaluate the effectiveness and practicability of the proposed method. Compared with other existing methods, it can be concluded that the proposed method can segment more small blood vessels while ensuring the integrity of vascular structure and has a higher performance. The proposed method has more obvious targets, a higher contrast, more plentiful detailed information, and local features. The qualitative and quantitative analysis results show that the presented method is superior to the other advanced methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation

Yan

Zhou

Luo

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“… 14 Several works have been dedicated to segmentation of MA, 15 – 19 HEM, 19 – 21 HE, and CWS, 19 , 22 – 24 and retinal vessels. 25 – 27 Segmentation of IRMA, panretinal photocoagulation scars (PC) and NV in retinal fundus images have at present, to the best of our knowledge, not been explored to the same degree. Models that are able to accurately detect these specific abnormalities could serve as an important part of an automatic grading method, as a way to minimize the adverse effect of reducing input resolution, and in turn improve automatic grading models’ ability to stratify DR across all levels.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification

Andersen

Hubel

Rasmussen

et al. 2022

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

“…Gegundez-Arias et al (2021) developed a new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and a modified U-net model. 23 Deng and Ye (2022) performed retinal blood vessel segmentation based on an improved deformable convolutional M-shaped network and a pulse-coupled neural network. 24 Zhang et al (2022) presented a novel automatic method based on bridge-net by joint learning context-involved and non-context features for the segmentation of retinal blood vessels.…”

Section: Introductionmentioning

confidence: 99%

“…Davamani et al (2022) developed fuzzy c-means clustering for blood cell classification . In addition, others have developed AI-based models to segment blood vessels. − Wang et al (2015) integrated CNN and random forest (RF) for retinal blood vessel segmentation . Soomro et al.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the resulting features were used to train an ANN in order to recognize each pixel as belonging to blood vessels or not. Gegundez-Arias et al (2021) developed a new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and a modified U-net model . Deng and Ye (2022) performed retinal blood vessel segmentation based on an improved deformable convolutional M-shaped network and a pulse-coupled neural network .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

Alizamir

Gholami

Bahrami³

et al. 2022

ACS Omega

View full text Add to dashboard Cite

Hemoglobin is one of the most important blood elements, and its optical properties will determine all other optical properties of human blood. Since the refractive index (RI) of hemoglobin plays a vital role as a non-invasive indicator of some illnesses, accurate calculation of it would be of great importance. Moreover, measurement of the RI of hemoglobin in the laboratory is time-consuming and expensive; thus, developing a smart approach to estimate this parameter is necessary. In this research, four viable strategies were used to make a quantitative correlation between the RI of hemoglobin and its influencing parameters including the concentration, wavelength, and temperature. First, alternating conditional expectations (ACE), a statistical approach, was employed to generate a correlation to predict the RI of hemoglobin. Then, three different optimized intelligent techniques—optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)—were used to model the RI. A bat-inspired (BA) algorithm was embedded in the formulation of intelligent models to obtain the optimal values of weights and biases of an artificial neural network, membership functions of the fuzzy inference system, and free parameters of support vector regression. The coefficient of determination, root-mean-square error, average absolute relative error, and symmetric mean absolute percentage error for each of the ACE, ONN, OFIS, and OSVR were found as the measure of each model’s accuracy. Results showed that ACE and optimized models (ONN, OFIS, and OSVR) have promising results in the estimation of hemoglobin’s RI. Collectively, ACE outperformed ONN, OFIS, and OSVR, while sensitivity analysis indicated that the concentration, wavelength, and, lastly, temperature would have the highest impact on the RI.

show abstract

A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model

Cited by 66 publications

References 63 publications

Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation

Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation

Automatic Detection of Abnormalities and Grading of Diabetic Retinopathy in 6-Field Retinal Images: Integration of Segmentation Into Classification

Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

Contact Info

Product

Resources

About