Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation

Zhang, Yuan; He, M.; Chen, Zhineng; Hu, Kai; Li, Xuanya; Gao, Xieping

doi:10.1016/j.eswa.2022.116526

Cited by 72 publications

(16 citation statements)

References 47 publications

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“…Zhang et al [30] proposed a novel deep network architecture named Bridge-net, which combines recurrent neural network (RNN) and convolutional neural network (CNN) to effectively utilize the context of retinal vessels. Tan et al [31] introduced skeletal prior and contrast loss and proposed a new network named SkelCon, which is able to improve the integrity and continuity of thin blood vessels.…”

Section: Related Work Of Cnn On Image Segmentationmentioning

confidence: 99%

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN

Jiang

Liang

Cheng

et al. 2022

Sensors

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Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolutional neural networks are good at extracting local feature information, but the convolutional block receptive field is limited. Transformer, on the other hand, performs well in modeling long-distance dependencies. Therefore, in this paper, a new network model MTPA_Unet is designed from the perspective of extracting connections between local detailed features and making complements using long-distance dependency information, which is applied to the retinal vessel segmentation task. MTPA_Unet uses multi-resolution image input to enable the network to extract information at different levels. The proposed TPA module not only captures long-distance dependencies, but also focuses on the location information of the vessel pixels to facilitate capillary segmentation. The Transformer is combined with the convolutional neural network in a serial approach, and the original MSA module is replaced by the TPA module to achieve finer segmentation. Finally, the network model is evaluated and analyzed on three recognized retinal image datasets DRIVE, CHASE DB1, and STARE. The evaluation metrics were 0.9718, 0.9762, and 0.9773 for accuracy; 0.8410, 0.8437, and 0.8938 for sensitivity; and 0.8318, 0.8164, and 0.8557 for Dice coefficient. Compared with existing retinal image segmentation methods, the proposed method in this paper achieved better vessel segmentation in all of the publicly available fundus datasets tested performance and results.

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Section: Related Work Of Cnn On Image Segmentationmentioning

confidence: 99%

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN

Jiang

Liang

Cheng

et al. 2022

Sensors

Self Cite

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“… 17 In addition, others have developed AI-based models to segment blood vessels. 18 − 25 Wang et al (2015) integrated CNN and random forest (RF) for retinal blood vessel segmentation. 19 Soomro et al.…”

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%

“…Deng and Ye (2022) performed retinal blood vessel segmentation based on an improved deformable convolutional M-shaped network and a pulse-coupled neural network . 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 . In the regression task, researchers have attempted to develop intelligence-based approaches to reliably predict blood pressure. − Xu et al (2017) presented a capable methodology based on ANN for continuous blood pressure estimation based on multiple parameters from electrocardiogram and photoplethysmogram .…”

Section: Introductionmentioning

confidence: 99%

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

Alizamir

Gholami

Bahrami³

et al. 2022

ACS Omega

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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.

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“…The IterMiUnet [ 18 ] is designed to alleviate the heavy parameterization of U-Net, inspired by Internet [ 12 ] and MiUnet [ 19 ]. Zhang et al [ 20 ] designed the Bridge-net to learn context-involved and noncontextual features to obtain superior segmentation results.…”

Section: Introductionmentioning

confidence: 99%

MC-UNet: Multimodule Concatenation Based on U-Shape Network for Retinal Blood Vessels Segmentation

Zhang

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make blood vessel segmentation still very challenging. This paper proposes a novel multimodule concatenation via a U-shaped network for retinal vessels segmentation, which is based on atrous convolution and multikernel pooling. The proposed network structure retains three layers of the essential structure of U-Net, in which the atrous convolution combining the multikernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with the dense atrous convolution module and the multikernel pooling module to form a multimodule concatenation. And different dilation rates are selected by cascading to acquire a larger receptive field in atrous convolution. Adequate comparative experiments are conducted on these public retinal datasets: DRIVE, STARE, and CHASE_DB1. The results show that the proposed method is effective, especially for microvessels. The code will be released at https://github.com/rocklijun/MC-UNet.

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Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation

Cited by 72 publications

References 47 publications

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN

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

MC-UNet: Multimodule Concatenation Based on U-Shape Network for Retinal Blood Vessels Segmentation

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